# data[].attributes.body
Co-designing a values-based future for digital education at The University of Edinburgh.

# data[].attributes.attachment_link.readable_text
The Near Future Teaching project ran between 2017 and 2019, with the goal to develop a values-based vision for the future of digital education at the University of Edinburgh. It used futures-thinking and design-based methodologies to work with more than 400 students, staff and other stakeholders in the production of this vision.This final report from the project explains its rationale and design, detailing the approach it took to mapping and understanding the future of digital education within the University. It shows how the project engaged widely with the University community in developing core values to guide us, and then sets out a vision and aims for a near future of teaching.

# data[].attributes.attachment_link.description
The Near Future Teaching project ran between 2017 and 2019, with the goal to develop a values-based vision for the future of digital education at the University of Edinburgh.

# data[].attributes.headline
MSc in Digital Education - University of Edinburgh

# data[].attributes.subject
MSc in Digital Education - University of Edinburgh

# data[].attributes.attachment_link.description
The MSc in Digital Education gives professionals in higher and further education and training and development the practical skills and critical insight they need in this fast-moving and richly diverse field.

# data[].attributes.attachment_link.provider_name
LACE - Learning Analytics Community Exchange

# data[].attributes.attachment_link.title
Homepage - LACE - Learning Analytics Community Exchange

# data[].attributes.attachment_link.description
Since the LACE Visions of the Future report was published, we have heard of a number of people who have made use of the visions in one way or another (see here for a brief summary of the visions).

# data[].attributes.attachment_link.provider_name
Society for Learning Analytics Research (SoLAR)

# data[].attributes.attachment_link.title
Home - Society for Learning Analytics Research (SoLAR)

# data[].attributes.attachment_link.description
Learning Analytics is what we do. Community is what makes us grow. SoLAR Newsletter | Become a Member | Attend Conferences | Get Involved

# data[].attributes.body
Rewriting the rules of the information age.

# data[].attributes.headline
Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education

# data[].attributes.subject
Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education

# data[].attributes.attachment_link.title
View of Openness and Praxis: Exploring the Use of Open Educational Practices in Higher Education | The International Review of Research in Open and Distributed Learning

# data[].attributes.headline
OER and OEP in the Global South: Implications and recommendations for social inclusion

# data[].attributes.subject
OER and OEP in the Global South: Implications and recommendations for social inclusion

# data[].attributes.attachment_link.readable_text
Arinto, Patricia; Hodgkinson-Williams, Cheryl; Trotter, Henry

The Research on Open Educational Resources for Development (ROER4D) project was undertaken to provide a better understanding of the uptake of Open Educational Resources (OER) and their impact on education in the Global South. The 18 sub-projects that comprise the larger project investigated the extent of OER adoption by educators and students; the factors influencing OER adoption; and the impact of OER adoption on access to educational resources, the quality of teaching and learning, and some of the costs of education provision in 21 countries in South America, Sub-Saharan Africa, and South and Southeast Asia.

The findings of each of the sub-projects are discussed in the various chapters comprising this volume, and a meta-synthesis of these findings is presented in Chapter 2. Using a social realist lens, the meta-synthesis provides a comparative analysis of OER use, adaptation and creation across the research sites, and identifies the structural, cultural and agential factors that enable and constrain these Open Educational Practices (OEP). It points out disjunctures in adoption processes in the countries and institutions studied, and draws insights regarding the extent to which OER adoption can expand access to educational materials, enhance the quality of educational resources and educators' pedagogical perspectives and practices, and improve the affordability and sustainability of education in the Global South.

This concluding chapter explores the implications of the main research findings presented in the meta-synthesis for the attainment of social inclusion, which lies at the heart of the Open Education movement. The Paris OER Declaration of 2012 explicitly calls upon states to "[p]romote and use OER to ... contribut[e] to social inclusion, gender equity and special needs education [and i]mprove both cost-efficiency and quality of teaching and learning outcomes" (emphasis added). The Ljubljana OER Action Plan of 2017 likewise recognises that, "[t]oward the realization of inclusive Knowledge Societies ... [OER] support quality education that is equitable, inclusive, open and participatory". Understanding how OER, OEP and Open Education more generally, can help to achieve social inclusion is particularly critical in the Global South where increased demand, lack of resources and high costs limit the capacity of education systems to provide accessible, relevant, highquality and affordable education. This chapter aims to contribute to this understanding the potential of OER and their accompanying OEP through a critical exploration of the ROER4D findings in terms of whether and how OER adoption promotes equitable access, participatory education and empowerment of teachers and students, and thus helps to achieve social inclusion. The chapter begins with a brief overview of the relationship between OER and social inclusion, details the implications of ROER4D's findings as they pertain to social inclusion, and concludes with recommendations for advocacy, policy, practice and further research in OER and OEP in the Global South.

Citation: Arinto, P. B., Hodgkinson-Williams, C. & Trotter, H. (2017). OER and OEP in the Global South: Implications and recommendations for social inclusion. In C. Hodgkinson-Williams & P. B. Arinto (Eds.), Adoption and impact of OER in the Global South (pp. 577-592). Retrieved from https://doi.org/10.5281/zenodo.1043829

# data[].attributes.attachment_link.title
OER and OEP in the Global South: Implications and recommendations for social inclusion

# data[].attributes.attachment_link.description
The Research on Open Educational Resources for Development (ROER4D) project was undertaken to provide a better understanding of the uptake of Open Educational Resources (OER) and their impact on education in the Global South.

# data[].attributes.body
O Estado da Arte, Desafios e Perspectivas para o Desenvolvimento e Inovação

# data[].attributes.headline
Moocs para apoiar o aprendizado de línguas

# data[].attributes.subject
Moocs para apoiar o aprendizado de línguas

# data[].attributes.headline
Aprendizado através de investigação individual

# data[].attributes.subject
Aprendizado através de investigação individual

# data[].attributes.body
Explorando novas formas
de ensino, aprendizagem
e avaliação, para
orientar educadores e
formuladores de políticas.

# data[].attributes.body
Por que professores e universidades ainda resistem em tornar mais públicas e abertas suas pesquisas?

# data[].attributes.attachment_link.readable_text
By no means an exhaustive list, herein lie three tensions that exist within academia, about the use, uptake, embrace and definition of Digital Scholarship...or Open Scholarship.

Tension 1

tenure and promotion is tied to traditional publishing of journal articles

When institutions live by the old paper model motto "publish or perish"; repurposed and translated to the electronic journal industry, there is an immediate disconnect between the work of a scholar using Networked Participatory Scholarship as fuel for enriching knowledge and knowledge networks. With a few notable exceptions, university promotion systems still rely on academics doing research so that it can be written up and published in a journal. There is little workload benefit for academics to spend time on being participatory, when they're obligated to persevere with traditional options. Martin Weller discussed The Open University and how it has recently made a small concession to recognising the work of Open Scholarship (Weller, 2018); this from the host of the free and open course, The Digital Scholar! The University of British Columbia is also changing the wording of promotion policy to recognise the benefit of networked participatory scholarship on the ability of academics to pursue and create valuable research using the knowledge networks technology has gifted them.

Dutch academic Julian Kirchher published this excellent piece in The Guardian, where he makes the publish-for-tenure argument look like the work of unimaginative accountants. He describes how he published research in his area of expertise (hydro dams) and received three citations. He was told, without ceremony, that this wasn't good enough to guarantee academic permanence. So, he played them, publishing a piece reviewing all of the available definitions of the term "circular economy", receiving ten times the number of citations, as well as the acknowledgement of peers and complete strangers at conferences. Voila - without being open, or sharing or participating in knowledge networks of any kind, he gave them what they wanted, without adding anything much to the pool of knowledge academia is meant to be adding to. He regards his work on "circular economy" as junk research.

According to Veletsianos and Kimmons(2012 p767) because the value or potential of open scholarship is still being demonstrated, the risky work of 'going open' isn't worth the time, effort and the risk of compromising promotion and tenure. Much is made of academics risking their jobs through activism on social media, an aspect of open scholarship that can spectacularly backfire. This, in spite of the overwhelming evidence that scholars can use, and are using social networks to share, reflect, critique, improve, validate and develop ideas. (Veletsianos & Kimmons, 2012. p.768

Tension 2

Universities are knowledge businesses and traditionally, they don't share well with others.

Scientia Potentia Est

Businesses don't like sharing their currency unless there is an equal exchange. Universities stake reputation, funding, donations and status on research and scholarly output, as well as student learning. They have much to lose by embracing open scholarship. As Stewart (2012) suggests, network participatory scholarship extends the walls of the university in "a new techno-cultural system" (p335) and this new system represents knowledge abundance. The idea that analogue/closed scholarship has always represented a framework of scarcity, relates to the idea of academia as a 'closed shop' that only the blessed few shall enter. Stewart adds, "While the research and teaching functions of the university have to some extent incorporated digital knowledge artifacts, many aspects of the techno-cultural system of the contemporary academy remain rooted in the premise of scarcity, or the hierarchies scarcity has fostered".(p.320)

As Katz boldly stated in 2010, "Technologies that foster an abundance and multiplicity of relationships create or reinforce relationships across sectors of society." (p.49), ergo......the great unwashed community of scholars who work beyond the ivory towers can now actively contribute to real scholarship. The occupants of the towers know a good thing when they see it too; many agitators for changing to a more participatory culture, are right there.

All the world's a stage, and all the men and women, merely players.

And all the men and women are now part of a "broad diverse and global ecosystem" (Katz, 2010 p49) of scholars and scholarship. As Masterman (2016) notes, "...the open sharing of knowledge has facilitated the blurring of boundaries between disciplines; between roles (research and teaching, teacher and learner); between the scholarly community and the lay community; between institutions and the world beyond; between formal and informal learning; and between geographical borders. "

Another way of referencing this is to speak about the democratisation of knowledge. It used to be scarce, in the hands of the few, and now it is not. This represents a profound knowledge revolution. Knowledge is power. (Bacon, F. a long time ago - maybe - nobody really knows if he said it or not)

Tension 3

Definitions

Open Scholarship is, as Weller (2018) suggested, a much more descriptive term for what many(but not all) have named "digital scholarship". One of the many reasons the former is better, is there are whole schools of the sciences (library and information science, social science, history, zooarcheology - just a few examples) who have a very different understanding of what digital scholarship is. It's not a subtle difference either.

A discussion of definitions beckons - based on literature searches using the terms "digital scholarship" and "open scholarship". To paraphrase large swathes of information into a couple of sentences

Open Scholars think it's about using the power of digital networks to connect, share, collaborate, refine, curate..and use that power to produce new scholarly material with a communal benefit to humans

Others consider "digital scholarship" to be the work of digital systems, metadata, machines, software, coding and programming. Their scholarship is producing the kind of information that simply wasn't possible with data spreadsheets and human analysis.

If we settle on a basic and entirely contestable definition; that Open Scholarship is about broadening scholarly work through "fostering extensive cross-disciplinary, public ties and rewarding connection, collaboration and curation between individuals" (Manca, 2017) so too, can we perhaps agree that it is not about crunching numbers in massive datasets using digital tools to uncover information too large for us to calculate without machine help.

While open scholars may have satisfied themselves about how they define their work, a large alternate universe within the academy, does define digital scholarship quite differently. It's the kind of scholarship that allows a journal of Zooarcheology (McKechnie et al 2015) to crunch very large number sets to make connections hitherto unseen. One of the papers in the journal, Ethnobiology Letters, details how bar coding software increases the efficiency and accuracy of zooarcheological data collection. You get the idea.

As @MelRicco says......

90% of world's #data was created in the last 2 years #O365SatBNE18 #o365sat18 pic.twitter.com/Q5GnL29spc

- Mel Khim (@MelRicco) 11 August 2018

Roy (2013) asks, "Is linking, thinking?" - suggesting in the process that linking big data sets is not scholarship. I don't agree with that, for a whole range of reasons, because it still takes a scholar to analyse and see significant factors in data. It is using digital tools to create new information, but this isn't about the openness or otherwise of the endeavour, it's simply about using digital tools to assess data for scholarship, and that's why I think the discussion about definitions is important.

For a team of history scholars, they linked the data and made some accidental discoveries about women in jazz.
Linked Jazz aims to uncover meaningful connections between data and documents from digital archives of jazz history (Pattueli et al 2017 p.918)

access to integrated views of otherwise dispersed information offers approaches to discovery that might otherwise be impossible, and new methods to engage with cultural heritage information.(Pattueli, Hwang & Miller, 2017)

It's clever and it is scholarship, but it isn't open scholarship. Not as those of us on the inside of this #openscholar machine understand it.

References

Bateman, O. (2017 May 10th). The young academic's Twitter conundrum. The Atlantic. Retrieved from https://www.theatlantic.com/education/archive/2017/05/the-young-academics-twitter-conundrum/525924/

Katz, R. (2010, April 15th). Scholars, scholarship, and the scholarly enterprise in the digital age. EDUCAUSE Review, 45(2), 44-56

Kirchherr, J. (2018, Aug 9th). A PhD should be about improving society, not chasing academic kudos. The Guardian. Retrieved from https://www.theguardian.com/higher-education-network/2018/aug/09/a-phd-should-be-about-improving-society-not-chasing-academic-kudos?CMP=twt_gu

Manca, S., & Ranieri, M. (2017). A study on use of social media for scholarly communication among Italian academics Research 2.0 and the impact of digital technologies on scholarly inquiry (pp. 117-142): IGI Global.

Masterman, E. (2016). Bringing Open Educational Practice to a Research-Intensive University: Prospects and Challenges. Electronic Journal of e-Learning, 14(1), 31-42.

McKechnie, I., Kansa, S. W., & Wolverton, S. (2015). Snapshots of Digital Scholarship in Zooarchaeology: Introduction to the Special Issue. Ethnobiology Letters, 6(2), 218-223. doi:10.14237/ebl.6.1.2015.556

Pasquini, L., Wakefield, J., & Roman, T. (2014). Impact Factor: Early Career Research & Digital Scholarship. TechTrends: Linking Research & Practice to Improve Learning, 58(6), 12-13. doi:10.1007/s11528-014-0797-7

Pattuelli, M. C., Hwang, K., & Miller, M. (2017). Accidental discovery, intentional inquiry: Leveraging linked data to uncover the women of jazz. Digital Scholarship in the Humanities, 32(4), 918-924. doi:10.1093/llc/fqw047

Roy, M. (2013). Is Linking Thinking? Addressing and Assessing Scholarship in a Digital Era. EDUCAUSE Review, 48(1), 52-53.

Shakespeare, W. (long, long ago) As You Like It

Stewart, B. E. (2015). In Abundance: Networked Participatory Practices as Scholarship. International Review of Research in Open & Distance Learning, 16(3), 318-340.

Veletsianos, G., & Kimmons, R. (2012). Networked Participatory Scholarship: Emergent techno-cultural pressures toward open and digital scholarship in online networks. Computers & Education, 58(2), 766-774. doi:https://doi.org/10.1016/j.compedu.2011.10.001

Weller, M. Guest colloquium #INF537 CSU MKNDI (yes, I know - that's not a proper citation but for now, it'll do)

Winter Solstice [youtube] (Producer). (2014, Aug 19th). All the world's a stage [from the motion picture As You Like It, 1936 directed by Paul Czinner]. [motion picture] Retrieved from https://www.youtube.com/watch?v=qJ2xxvGA7xE

Yano, B., Munro, D., & Coolidge, A. (2018 July 18th). University of Britsh Columbia: recognising open in promotion and tenure. EDUCAUSE Review. Retrieved from https://er.educause.edu/blogs/2018/7/university-of-british-columbia-recognizing-open-in-promotion-and-tenure

@MelRicco. (2018 Aug 10th 5.08pm). 90% of the world's #data was created in the last 2 years #0365SatBNE18 #o365sat18 [tweet]. Retrieved from https://twitter.com/MelRicco/status/1028070664287047682 doi:https://twitter.com/MelRicco/status/1028070664287047682

# data[].attributes.attachment_link.description
By no means an exhaustive list, herein lie three tensions that exist within academia, about the use, uptake, embrace and definition of Digital Scholarship...or Open Scholarship. When institutions live by the old paper model motto "publish or perish"; repurposed and translated to the electronic journal industry, there is an immediate disconnect between the work of a scholar using Networked Participatory Scholarship as fuel for enriching knowledge and knowledge networks.

# data[].attributes.body
Existe um interesse emergente nas universidades canadenses de avançar em direção a práticas de ensino baseadas em evidências (MacLean & Poole, 2010), ou seja, consultar a literatura sobre ensino e aprendizagem para tomar decisões informadas sobre o ensino e o envolvimento em algum nível de pesquisa em sala de aula para descobrir como os alunos aprendem melhor. Embora a maioria dos professores reflita sobre suas práticas como uma maneira de orientar seu ensino, um número menor de professores se engaja em Scholarship of Teaching and Learning (SoTL) e em sua investigação sistemática sobre como maximizar o aprendizado dos alunos através do ensino eficaz.

Em sua fundação, SoTL é “o processo de explorar, pesquisar, desenvolver, refinar, refletir e comunicar melhores maneiras e meios de produzir, promover e aprimorar o aprendizado e o ensino acadêmico” (Healy et al. 2013 : 24). 'Ir a público' é o fator chave que distingue o SoTL do ensino acadêmico (Vajoczki et al. 2011). Ser sistemático em como proceder para observar a aprendizagem, reunir evidências e disseminar descobertas em locais revisados ​​por pares é outra característica importante da pesquisa do SoTL.

Por isso, compartilhamos com Potter e Kustra (2011: 2) a visão de que o SoTL é “[o] estudo sistemático de ensino e aprendizagem, usando critérios estabelecidos ou validados de bolsa de estudos, para entender como o ensino (crenças, comportamentos, atitudes e valores) pode maximizar o aprendizado e / ou desenvolver um entendimento mais preciso do aprendizado, resultando em produtos que são compartilhados publicamente para crítica e uso por uma comunidade apropriada. ”

# data[].attributes.attachment_link.readable_text
There is an emerging interest in Canadian universities to move towards evidenced-based teaching practices (MacLean & Poole, 2010), that is, consulting the literature on teaching and learning to make informed decisions about teaching and engaging in some level of classroom research to uncover how students learn best. While most professors reflect on their practices as a way to guide their teaching, a smaller number of faculty engage in the Scholarship of Teaching and Learning (SoTL) and its systematic inquiry into how to maximize student learning through effective teaching.

At its foundation, the scholarship of teaching and learning is "the process of exploring, researching, developing, refining, reflecting upon, and communicating better ways and means of producing, promoting, and enhancing scholarly learning and teaching" (Healy et al. 2013: 24). 'Going public' is the key factor that distinguishes SoTL from scholarly teaching (Vajoczki et al. 2011). Being systematic in how to go about observing learning, gathering evidence of it and disseminating findings in peer-reviewed venues is another important characteristic of SoTL research.

Hence we share with Potter and Kustra (2011:2) the view that SoTL is "[t]he systematic study of teaching and learning, using established or validated criteria of scholarship, to understand how teaching (beliefs, behaviours, attitudes and values) can maximize learning, and/or develop a more accurate understanding of learning, resulting in products that are publicly shared for critique and use by an appropriate community."

# data[].attributes.attachment_link.description
There is an emerging interest in Canadian universities to move towards evidenced-based teaching practices (MacLean & Poole, 2010), that is, consulting the literature on teaching and learning to make informed decisions about teaching and engaging in some level of classroom research to uncover how students learn best.

# data[].attributes.body
Veletsianos e Kimmons destacam as vantagens da bolsa aberta, mas também alertam para os aspectos negativos, como apropriação indébita, expectativa de alfabetização digital e o potencial de abertura criando desigualdades nas comunidades acadêmicas. Tressie McMillan Cottom (2012) aponta os riscos da bolsa de estudos on-line para bolsistas membros de grupos marginalizados ou minoritários. As instituições devem oferecer apoio a esses acadêmicos, especialmente se forem minorias, mulheres, e estudiosos juniores.

# data[].attributes.attachment_link.readable_text
There is an emerging interest in Canadian universities to move towards evidenced-based teaching practices (MacLean & Poole, 2010), that is, consulting the literature on teaching and learning to make informed decisions about teaching and engaging in some level of classroom research to uncover how students learn best. While most professors reflect on their practices as a way to guide their teaching, a smaller number of faculty engage in the Scholarship of Teaching and Learning (SoTL) and its systematic inquiry into how to maximize student learning through effective teaching.

At its foundation, the scholarship of teaching and learning is "the process of exploring, researching, developing, refining, reflecting upon, and communicating better ways and means of producing, promoting, and enhancing scholarly learning and teaching" (Healy et al. 2013: 24). 'Going public' is the key factor that distinguishes SoTL from scholarly teaching (Vajoczki et al. 2011). Being systematic in how to go about observing learning, gathering evidence of it and disseminating findings in peer-reviewed venues is another important characteristic of SoTL research.

Hence we share with Potter and Kustra (2011:2) the view that SoTL is "[t]he systematic study of teaching and learning, using established or validated criteria of scholarship, to understand how teaching (beliefs, behaviours, attitudes and values) can maximize learning, and/or develop a more accurate understanding of learning, resulting in products that are publicly shared for critique and use by an appropriate community."

# data[].attributes.attachment_link.description
There is an emerging interest in Canadian universities to move towards evidenced-based teaching practices (MacLean & Poole, 2010), that is, consulting the literature on teaching and learning to make informed decisions about teaching and engaging in some level of classroom research to uncover how students learn best.

# data[].attributes.headline
Potenciais dilemas éticos e princípios fundamentais da prática ética

# data[].attributes.subject
Potenciais dilemas éticos e princípios fundamentais da prática ética

# data[].attributes.body
Fedoruk (2017, p. 3) identifica conflitos de interesse e relações de poder como duas áreas centrais para considerações éticas no SoTL. A tabela abaixo, extraída do guia Ética no Bolsa de Ensino e Aprendizagem licenciado pela Creative Commons da Fedoruk (2017, p. 16), descreve alguns princípios-chave para a prática ética e questões a serem consideradas, começando por abordar a questão da 'influência indevida, coerção ou desequilíbrio de poder ".
Link para a publicação completa: https://taylorinstitute.ucalgary.ca/sites/default/files/Ethics%20in%20SoTL-Taylor%20Institute%20Guide.pdf

# data[].attributes.body
Creswell e Plano-Clark (2007) identificam quatro projetos de métodos mistos, cada um envolvendo papéis diferentes para as fases de pesquisa quantitativa e qualitativa.
A escolha da metodologia e dos métodos de coleta de dados usados ​​para coletar evidências para qualquer estudo deve ser orientada pela pergunta da pesquisa.

# data[].attributes.body
A journal dedicated to the scholarship of teaching and learning in the ‘global South’

# data[].attributes.body
A journal dedicated to the scholarship of teaching and learning in the ‘global South’

# data[].attributes.attachment_link.title
Scholarship of Teaching and Learning in the South

# data[].attributes.attachment_link.description
Scholarship of Teaching and Learning of the South

# data[].attributes.body
2018

# data[].attributes.headline
Scholarship of Teaching and Learning(SoTL) in the South Conference

# data[].attributes.subject
Scholarship of Teaching and Learning(SoTL) in the South Conference

# data[].attributes.body
2017

# data[].attributes.attachment_link.description
Scoop.it enables professionals and businesses to research and publish content through its content curation tool.

# data[].attributes.body
Página em Francês + Inglês

# data[].attributes.attachment_link.readable_text
Comparatif des plates-formes de curation

Outils permettant de sélectionner manuellement des contenus en ligne, de les éditorialiser et de les partager.

Ce comparatif fait partie d'un article sur les outils de curation, Le Guide de la Curation. Pour plus d'explications sur les produits et les critères : http://pro.01net.com/editorial/529626/le-guide-de-la-curation-(3)-les-outils/

Ce comparatif est associé à celui des outils de publication automatique, qui font de la sélection automatique de contenu : http://socialcompare.com/fr/comparison/outils-de-publication-automatique-md16pw3

Le tableau est librement modifiable, vous pouvez le mettre à jour vous-même et noter vos outils préférés.

# data[].attributes.attachment_link.description
Outils permettant de sélectionner manuellement des contenus en ligne, de les éditorialiser et de les partager. Ce comparatif fait partie d'un article sur les outils de curation, Le Guide de la Curation.

# data[].attributes.attachment_link.description
One place for all the stories you care about. Join the Flipboard community to discover and share what inspires you.

# data[].attributes.attachment_link.title
Diigo - Better reading and research with annotation, highlighter, sticky notes, archiving, bookmarking & more.

# data[].attributes.attachment_link.description
Diigo is a powerful research tool and a knowledge-sharing community

# data[].attributes.attachment_link.title
Wakelet | The All In One Content Platform

# data[].attributes.attachment_link.description
Wakelet allows you to bookmark, organize, collaborate and present on content all in one platform. Harness your content to build remarkable things.

# data[].attributes.attachment_link.description
Pearltrees is a place to organize everything you're interested in. It lets you organize, explore and share everything you like.

# data[].attributes.attachment_link.readable_text
For individuals

Publish your topic page with curated content in minutes.
Distribute it automatically with your network
to build your professional brand.

For businesses

Curate, share, and read content on private hubs.
Publish on your websites and blogs, newsletters and social media.
Aggregate curated content across multiple WordPress blogs.

Trusted by 8+ million curators, business intelligence professionals & marketers

How do content curation tools work?

A powerful content engine

Scoop.it's advanced content engine monitors global sources to find and curate relevant third-party content.

With a content curation tool you'll:

Boost credibility with readers and build brand awareness

Establish thought leadership and strengthen influencer connections

Access sources ranging from mainstream media to niche publications

Create and share curated
content pages

Publish topic pages with curated content in minutes.
Share them with your networks to build your professional brand.

Create engaging web pages from the content you curate

Feed your social channels automatically with curated content

Embed your content pages in your website

Leverage curated content in templated newsletters

Monitor and share knowledge with coworkers privately

Make it easy for your team to discover, curate and share content.

Automate content monitoring

Share curated content privately with your team

Generate internal email newsletters in a few clicks

Create private curated content hubs for your team

Integrate with SSO, intranet and enterprise social networks

Publish curated content for a targeted audience

Leverage curated content: store, share, categorize and disseminate it.

Aggregate curated content on one or multiple websites

Publish content on one or multiple Wordpress blogs

Share your team content on multiple social media

Generate customized newsletter for Mailchimp or any emailing solutions

Resources to make the most of content opportunities

# data[].attributes.attachment_link.title
Scoop.it - Content Curation Tool | Scoop.it

# data[].attributes.attachment_link.description
Scoop.it enables professionals and businesses to research and publish content through its content curation tool.

# data[].attributes.attachment_link.readable_text
A Escola Virtual de Governo (EV.G) é uma iniciativa da Enap, junto a escolas de governo e instituições parceiras, e consiste em um portal único para oferta de capacitação a distância voltado a servidores públicos e cidadãos de todo o país.

A EV.G oferece cursos a distância de diferentes instituições e em diferentes temáticas ligadas à administração pública e cidadania, viabilizando o desafio de contribuir para a formação e o desenvolvimento de milhares de servidores públicos e cidadãos.

Acreditamos que a centralização e publicização dos dados relativos à capacitação a distância no governo interessa a instituições, servidores e sociedade e pode contribuir para avançarmos juntos na oferta de soluções educacionais mais eficientes.

Seja bem-vindo, use sem moderação! =)

Esta iniciativa apoia-se em 4 pilares:

Transparência:

Acreditamos que a informação aberta, tempestiva e dinâmica é um dever das instituições públicas, em especial das escolas. O acesso facilitado a dados reais e precisos permite não apenas o controle social de cidadãos e interessados nas políticas públicas ligadas à educação de servidores públicos, mas também a análise de informações de acordo com as mais variadas necessidades.

Compartilhamento:

Este é o pilar básico da era digital: a revolução promovida pela internet transforma as relações humanas e suas atividades, em especial os campos da informação e do conhecimento. Nós da EV.G buscamos sempre usar este potencial para compartilhar o que temos e conectar interesses e necessidades, em prol de um modelo mais acessível e eficaz.

Colaboração:

Acreditamos que, conhecendo melhor nossa realidade, podemos usar nossas informações para desenvolvimento e aprimoramento de nossas atividades, bem como contribuir para o aprimoramento de outras instituições. A partir de um modelo mais aberto e acessível, estamos contribuindo para a formação de redes de colaboração.

Melhoria Contínua:

Ser capaz de compartilhar dados e informações requer o reconhecimento de que o processo de melhoria é contínuo e permanente. É preciso se desapegar da ideia da perfeição e do acabado. Este não é um canal para divulgar apenas sucessos e conquistas. Nós da EV.G reconhecemos que estamos em processo permanente de melhoria e contamos com sua contribuição para este processo.

Painéis

Conheça nossos painéis de informações e indicadores de desempenho de cursos e alunos da Escola Virtual Gov.

"Ensinar não é transferir conhecimento, mas criar as possibilidades para a sua própria produção ou a sua construção."

Paulo Freire

# data[].attributes.attachment_link.description
A Escola Virtual de Governo (EV.G) é uma iniciativa da Enap, junto a escolas de governo e instituições parceiras, e consiste em um portal único para oferta de capacitação a distância voltado a servidores públicos e cidadãos de todo o país.

# data[].attributes.headline
Apolitical - Pay toll ahead: the public sector's data problem

# data[].attributes.subject
Apolitical - Pay toll ahead: the public sector's data problem

# data[].attributes.attachment_link.title
Pay toll ahead: the public sector's data problem

# data[].attributes.attachment_link.description
Government can’t access the data it needs to improve services. Here’s how that can change

# data[].attributes.headline
Apolitical - The first decade of open data has been a win (but not for the reasons you think)

# data[].attributes.subject
Apolitical - The first decade of open data has been a win (but not for the reasons you think)

# data[].attributes.attachment_link.title
The first decade of open data has been a win - but not for the reasons you think

# data[].attributes.attachment_link.description
Opinion: One unheralded benefit has been getting government to realise the value of its data

# data[].attributes.headline
Apolitical - What I learned from two years at the heart of government data science

# data[].attributes.subject
Apolitical - What I learned from two years at the heart of government data science

# data[].attributes.attachment_link.title
What I learned from two years at the heart of government data science

# data[].attributes.attachment_link.description
Opinion: Better use of data in public services is changing mindsets

# data[].attributes.body
Hospedado pela Sociedade Internacional de Data Mining Educacional

# data[].attributes.attachment_link.readable_text
Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in.

Whether educational data is taken from students' use of interactive learning environments, computer-supported collaborative learning, or administrative data from schools and universities, it often has multiple levels of meaningful hierarchy, which often need to be determined by properties of the data itself, rather than in advance. Issues of time, sequence, and context also play important roles in the study of educational data.

The International Educational Data Mining Society's aim is to support collaboration and scientific development in this new discipline, through the organization of the EDM conference series, the Journal of Educational Data Mining, and mailing lists, as well as the development of community resources, to support the sharing of data and techniques.

# data[].attributes.attachment_link.description
Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique and increasingly large-scale data that come from educational settings and using those methods to better understand students, and the settings which they learn in.

# data[].attributes.headline
Projeto LACE - Learning Analytics Community Exchange 

# data[].attributes.subject
Projeto LACE - Learning Analytics Community Exchange 

# data[].attributes.attachment_link.provider_name
LACE - Learning Analytics Community Exchange

# data[].attributes.attachment_link.title
Homepage - LACE - Learning Analytics Community Exchange

# data[].attributes.attachment_link.description
Since the LACE Visions of the Future report was published, we have heard of a number of people who have made use of the visions in one way or another (see here for a brief summary of the visions).

# data[].attributes.headline
Projeto SoLAR - Society for Learning Analytics Research

# data[].attributes.subject
Projeto SoLAR - Society for Learning Analytics Research

# data[].attributes.attachment_link.provider_name
Society for Learning Analytics Research (SoLAR)

# data[].attributes.attachment_link.title
Home - Society for Learning Analytics Research (SoLAR)

# data[].attributes.attachment_link.description
Learning Analytics is what we do. Community is what makes us grow. SoLAR Newsletter | Become a Member | Attend Conferences | Get Involved

# data[].attributes.headline
Learning analytics: drivers, developments and challenges

# data[].attributes.subject
Learning analytics: drivers, developments and challenges

# data[].attributes.body
Artigo de 2012, introdutório sobre o assunto.

# data[].attributes.attachment_link.description
Key information for Brazil participating in PISA

# data[].attributes.body
O termo action analytics refere-se a recursos e práticas de análise
que são poderosos, imediatos e levam a resultados úteis para uma ampla variedade de stakeholders. Mais importante, os resultados altamente benéficos da análise de ação só podem acontecer em empresas e ambientes genuinamente comprometidos em medir e melhorar os principais aspectos da produtividade,
inovação e desempenho. Action analytics é mais sobre liderar e navegar por mudanças significativas na cultura organizacional e
comportamento do que adquirir soluções tecnológicas.

# data[].attributes.headline
Academic Analytics: A New Tool for a New Era

# data[].attributes.subject
Academic Analytics: A New Tool for a New Era

# data[].attributes.body
À medida que as populações se preocupam com seu bem-estar financeiro e segurança econômica, aumentam as pressões sobre os indivíduos e instituições que podem influenciar o resultado. Na era da informação, uma das instituições mais influentes é a educação. E em uma era de responsabilidade e confiança, as organizações que resistem às pressões por resultados, responsabilidade e ação são suspeitas.

# data[].attributes.attachment_link.readable_text
© 2007 John P. Campbell, Peter B. DeBlois, and Diana G. Oblinger

EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40-57

John P. Campbell is an EDUCAUSE Learning Initiative (ELI) Scholar in Residence and Associate Vice President for Teaching and Learning Technologies at Purdue University, where he is responsible for all aspects of instructional technology. Peter B. DeBlois is Director of Programs and Media Relations at EDUCAUSE, where he coordinates member-engagement programs and media relations. Diana G. Oblinger is Vice President of EDUCAUSE, where she is responsible for the association's teaching and learning activities and for the EDUCAUSE Learning Initiative. Comments on this article can be sent to the authors at [email protected] , [email protected] , and [email protected] and/or can be posted to the Web via the link at the bottom of this page.

In responding to internal and external pressures for accountability in higher education, especially in the areas of improved learning outcomes and student success, IT leaders may soon become critical partners with academic and student affairs. IT can help answer this call for accountability through academic analytics, which is emerging as a new tool for a new era.

A New Era

Although A Nation at Risk was released by the U.S. Department of Education in 1983, in many ways it remains an apt description of current conditions. ¹ Consider the following statistics:

Western nations are looking over their shoulders at China and India. As a popular YouTube video highlighted, if you are "one in a million" in China, there are 1,300 other people just like you; in India, there are 1,100 others just like you. The 25 percent of the Chinese population with the highest IQs is greater than the total population of North America; in India, the 28 percent of the population with the highest IQs is greater than the total population of North America.²

Economies depend on a well-educated population. As a result, many countries are investing in strengthening their educational systems, with an emphasis on increasing the proportion of their population that has a postsecondary degree.³

In spite of the importance of postsecondary education, the percentage of the U.S. population with a postsecondary degree has slipped relative to that of other countries. Rather than leading the world, as it has for most of the past fifty years, in 2003 the United States ranked ninth among industrialized nations in the percentage of 25-to-34-year-olds who have completed at least an associate's degree.⁴ If current trends are left unchanged, the percentage of the U.S. workforce with a bachelor's degree will decline from 17.1 percent (as of 2000) to 16.4 percent (in 2020).⁵

Minorities are an increasing percentage of the population, as well as of the college-going population. For example, by 2015, Hispanics will be the second-largest student group (by race/ethnicity), growing to 15.4 percent of the nation's campus population.⁶ Yet the educational attainment for minorities is below that of other groups. High school graduation rates in the United States are around 70 percent but fall to 50 percent for black, Native American, and Hispanic students.⁷ Although the percentage of students who graduate from four-year institutions after six years is 57 percent overall, the rate is 41 percent for African Americans, 47 percent for Hispanics, and 39 percent for Native Americans.⁸

Just being able to hold a job may not be enough. Each year, more jobs are outsourced to countries with lower labor costs. The good jobs that remain in countries with higher labor costs are those that require education and more refined skills. For example, the U.S. Department of Labor projects that twenty of the thirty fastest-growing jobs in the United States will require education beyond high school and that 40 percent will require at least an associate's degree.⁹

If the current educational gaps remain, U.S. per capita income is projected to decline 2 percent from 2000 to 2020. By contrast, per capita income has increased 41 percent in the past two decades. And trailing other developed countries on education may reduce U.S. economic growth by as much as half a percent a year.¹⁰

As populations become concerned for their financial well-being and economic security, pressures increase on those individuals and institutions that might influence the outcome. In the information age, one of the most influential institutions is education. And in an era of accountability and liability, organizations that resist pressures for results, accountability, and action are suspect. With economic security at stake, how long will society accept that the percentage of the population with a college/university education is stagnant even as the demand has risen, that retention rates have not significantly improved in decades, and that graduates may not have mastered even basic competencies? ¹¹

A greater proportion of the U.S. population is being educated, but overall college/university graduation rates have remained relatively unchanged for decades. And even though the United States is holding its own in the percentage of the population with college/university degrees, other countries are moving ahead (e.g., Sweden, the United Kingdom). ¹² Blaming K-12 for inadequately preparing students does not divert attention from the root problem, since K-12 teachers are the products of higher education.

But thanks to enterprise-wide systems that generate massive amounts of data, data warehouses that aggregate disparate types of data, and processing power that sifts, sorts, and surfaces patterns, academic analytics is emerging as a new tool that can address what seem like intractable challenges.

A New Tool

Analytics marries large data sets, statistical techniques, and predictive modeling. It could be thought of as the practice of mining institutional data to produce "actionable intelligence." Just as Amazon.com knows when to send someone an e-mail notice of a new book that he/she might be interested in buying, so does an admissions office know whether to invest in the printing and postage necessary to send a high school junior a glossy campus viewbook.

Today, analytics is most often used in higher education for administrative decisions-from delivering the targeted number and quality of a freshman class to cultivating likely donors. But the use of analytics will likely grow in high-stakes areas such as academic success. Whether the catalyst for adoption is a call for accountability from outside of higher education or the need for scorecards or decision-making models from within, analytics is in higher education's future. To prepare, IT and institutional leaders need to begin to understand analytics-as well as the changes that may be required in data standards, tools, processes, organizations, policies, and institutional culture.

Many institutions have implemented analytics to improve enrollment management. Institutional researchers collaborating with admissions staff have created complex formulas-based on standardized exam scores, high school coursework, and other information-to determine which applicants will be admitted. The "actionable intelligence" generated from statistical analyses of these diverse data sources can guide a more efficient use of limited admissions budgets and staff time. For some institutions, analytics means that the institution can provide applicants with an immediate response to their admissions applications. Analytical models and decisions have been refined over the years to produce fairly predictable enrollment rates, as well as balance in areas such as in-state/out-of-state students, enrollments within programs, and other demographic factors. Based on current data, models are refined annually to improve enrollment decisions.

Beyond enrollment management, analytics is increasingly used to inform fund-raising. By building a data warehouse containing information about alumni and friends, institutions can use predictive models to identify those donors who are most likely to give. Aside from academic and co-curricular history, information may include an individual's response to past solicitations; interest in particular college/university initiatives; employment in, contributions to, and honors received in fields related to institutional programs; and participation in institutional events.

With the increased concern for accountability, academic analytics has the potential to create actionable intelligence to improve teaching, learning, and student success. Traditionally academic systems-such as course management systems, student response systems, and similar tools-have generated a wide array of data that may relate to student effort and success. Early academic analytics initiatives are seeking to predict which students are in academic difficulty, allowing faculty and advisors to customize learning paths or provide instruction tailored to specific learning needs.

Sample Academic Analytics Initiatives

Baylor University, the University of Alabama, Sinclair Community College, Northern Arizona University, and Purdue University are among the pioneers in higher education analytics.

Using Enrollment Predictive Modeling at Baylor University

Since the late 1990s, Baylor University has pioneered and refined the gathering and analysis of massive amounts of data on prospective students to support a sophisticated admissions strategy. The traditional admissions cohort-action funnel (moving from inquiries to applications to acceptances to deposit payments through enrollments) analyzes factors from the application stage forward to the enrollment yield. Baylor, however, looks at a broad array of variables from the inquiry stage forward.

Out of all the variables available, Baylor identified eight that result in the best predictive model for Texas residents:

Attendance of a premier event

Campus visit

Extracurricular interest

High school attended

Mail qualifying score (Baylor level of interest)

SAT score (for non-Texas residents, this variable was replaced by the number of solicited contacts)

Number of self-initiated contacts

Telecounselor score (Baylor level of interest)¹³

The inquiry pool is updated once a week with additional prospective students and with additional data on existing inquirers. Scores from the predictive model are added to the student database, which admissions staff can then query to identify those students most likely to be admitted. Specific scores trigger who will receive various types of follow-up. For example, the top 75 percent of scored inquirers are sent the expensive-to-print-and-mail campus viewbook, whereas the bottom 25 percent receive only a reply card and the application form. The goal over time has been to refine the model so that no mailings go to those lower-scoring inquirers who are least likely to enroll.

The same model can be used to select the most likely enrollees for phone calls by telecounselors. Admissions counselors making high school visits use the model to identify students to seek out personally. At all points of contact, admissions staff and telecounselors enter new or updated interest scores into the prospect database; the system populates missing data with averages from past cohorts. The net effect has been the creation of information that lets Baylor segment its prospect pool, target the most likely enrollees, and more efficiently use human and financial resources to deliver the desired freshman class. One measure of this use of actionable intelligence was the increase in new student applications from approximately 15,000 for the fall semester of 2005 to approximately 26,000 in fall 2006.

Predicting and Improving Student Retention at the University of Alabama

As part of an effort to improve student retention from the freshman to sophomore year, the University of Alabama (UA) experimented with analytics. Graduate students in a data-mining course were given access to the data files of enrolled freshmen (identities were concealed) from 1999, 2000, and 2001 and were asked to develop predictive models of at-risk students. Using such statistical techniques as logistic regression, decision trees, and neural networks, the students developed a single refined model with eight significant variables:

UA cumulative GPA

English course

English course grade

Distance from UA campus to home

Race

Math course grade

Total earned hours

Highest ACT score (ACT or ACT-converted SAT score)¹⁴

Using the retention model along with pre-enrollment data and an aggregate cut-off score-a score developed in consultation with the university registrar and one below which students are considered in need of intervention-UA is able to identify 150-200 freshmen each year who are not likely to return for their sophomore year. The information is then shared with faculty and academic advisors for outreach efforts, counseling, or other action. Consistent with best practices in deploying analytics, a UA admissions research team is presently assessing and refining the retention model and will introduce a new one next year.

Developing a Student Success Plan and Early Alert System at Sinclair Community College

Since 2004, the Student Success Plan (SSP) at Sinclair Community College (SCC) has won seven national awards for its innovative and effective data-gathering for student advising and retention. ¹⁵ The SSP is a Web-based counseling records management, reference, and reporting system that uses an SQL database to integrate demographic and admissions information from a data warehouse; real-time course registration, grades, and financial aid status from the student information system; and counselor risk-assessment notes and faculty-initiated early alerts.

Analytics generates a system alert for advisors to initiate an Individual Learning Plan (ILP) whenever any one of the following four criteria appears in a new student's profile:

Placement-test referrals into two or more developmental courses numbered below "100"

Individual or family income level below the federal poverty level

Full-time work

Undecided major

As the student and the counselor develop an ILP, additional information is captured, including Myers-Briggs Type Indicator, Learning and Study Strategies Inventory, personal life challenges, student satisfaction surveys, course enrollment planning, study plan and tutorial referrals, student progress markers, and counselors' notes. Aggregate factors that signal student success are achieving a GPA of 2.0 or better, passing all developmental courses, deciding on a major and a career, resolving child-care and transportation issues, and attending class regularly. ¹⁶ The fall 2006 impact assessment of the ILP program from the SCC Office of Research, Analytics, and Reporting shows that first- to second-quarter new student retention was 93.3 percent for ILP completers, 76.0 percent for ILP-active students, and 65.7 percent for non-ILP students. 17

The SSP, funded in part through a five-year federal Title III "Strengthening Institutional Programs" planning grant, has future plans to develop a student success course, create secure faculty-access modules, integrate the early-alert system into the campus portal, and develop new analytics reports to support prediction and analysis of student success.

Connecting Resource Utilization, Risk Level, and Outcomes at Northern Arizona University

Northern Arizona University (NAU) is in the third year of an initiative to use multiple data sources to identify at-risk first-year students and to assess which proactive interventions have the best influence on their academic success and retention. Like most other colleges and universities, NAU has a robust set of academic and student-life support resources, but members of a support task force knew that students and, by extension, staff were using services in a reactive way-after problems had surfaced and students were already at greater risk of failure-withdrawal. The task force set as its goal a predictive model that would identify which students would benefit from which resources. ¹⁸ The model comprised three critical elements:

Resource/Service Utilization: a distillation of NAU's numerous resources and services into five categories that would make the analysis manageable but not too broad-brush: (1) academic services (academic advising and tutoring); (2) recreational resources (recreation center usage, fitness program, intramural sports, and wellness education); (3) social resources (student organization membership, after-hours events, and social activities); (4) academic referrals (by centralized advising center staff to academic departments for curricular and degree program assistance); and (5) advising/career sessions (with centralized advising center staff and resources). Although gathering the data for some categories of utilization involved blending online records and manual rosters, much of the data gathering was facilitated by usage records created from student ID card swipes by service offices and at campus activities and events.

Levels of Risk: established by admissions test scores, high school GPAs, and psychosocial factors as measured by NAU's deployment of the ACT Student Readiness Inventory

Outcomes: measured by first-year student GPAs and enrollment retention status

Among the telling results: the GPAs of students who used one to three academic services increased 0.192 points, on average; those who used four services increased GPAs by 0.280 points; and students who were high-risk and used four services increased GPAs by 0.460 points. The utilizations that had the greatest impact on retention were academic referrals and advising/career sessions. ¹⁹

The NAU researchers found that the most efficient use of advising and support resources occurs when interventions are focused on high-risk students who are engaged through academic referrals and advising/career sessions. They also recognized an important aspect of "intrusive advising": that despite the positive gains in performance and retention, the way in which students learn about institutional efforts on their behalf may affect their perceptions of privacy; consequently, the timing and the content of communications require careful planning.

In the future, NAU hopes to use prediction information to connect resources and services with students as early as possible and to continue tracking both resource use and student success to refine the predictive model. One possibility is to add new data sources to the model, such as UCLA's Cooperative Institutional Research Program (CIRP) Freshman Survey ( http://www.gseis.ucla.edu/heri/freshman.html) and the National Survey of Student Engagement ( http://nsse.iub.edu/redirect.cfm?target).

Using Course Management System Data to Identify At-Risk Students at Purdue University

Purdue University is extracting data from the course management system (CMS) to build models that predict which students may be struggling academically and to provide proactive intervention. Purdue's premise is that student academic success is the result of the student's aptitude (as measured by standardized test scores and similar information) and the student's effort (as measured by participation within the CMS).

The CMS was selected as the initial focus because of broad campus adoption and the automatic collection of more than twenty activity variables, ranging from the time spent within the CMS to the number of discussion postings. Since it can be difficult to understand how each faculty member utilizes the CMS, an individual's data is compared with that of his/her peers. The resulting comparison provides a standardized value of student effort (e.g., the number of discussion postings is shown as the standard deviation from the class mean). If the faculty member decides not to utilize a tool, everyone in the course is "average."

Using factor analysis and logistic regression, a model was developed to predict student success within a course. Six variables were found to be significant:

ACT or SAT score

Overall grade-point average

CMS usage composite

CMS assessment composite

CMS assignment composite

CMS calendar composite

Two models were developed and validated: for freshmen and for the overall campus population. Both models included the same significant variables, but the variables differed in importance. In each model, the CMS data significantly contributed to predicting academic success. However, the freshman-only model was able to correctly classify nearly 80 percent of the students, whereas the all-student model was able to correctly classify only 67 percent of the students.

Purdue's next step is to connect the results of the predictive models to existing student-intervention programs including help desks, supplemental instruction, and similar programs.

Building an Academic Analytics Initiative

Academic analytics relies on the extraction of data from one or more systems, such as the CMS or a student information system. The data, which may be stored in a data warehouse for ongoing use, is analyzed using statistical software, and a mathematical model is generated. Based on the model and predetermined values, a particular action may be triggered, such as sending the student an electronic notification or initiating a personal intervention by college/university staff.

For example, data extracted from a student information system provides baseline student demographic, academic performance, and aptitude information. The CMS provides a snapshot of the student's course-related efforts by providing real-time interaction information that allows for comparison with peers. The two sources of data are combined to predict the probability of student success. Using this probability, the institution can decide whether to take certain actions such as inviting a student to a help session via e-mail or calling a student with an invitation to meet with an advisor.

Three characteristics of successful academic analytics projects are worth highlighting:

1. Leaders who are committed to evidence-based decision-making

2. Administrative staff who are skilled at data analysis

3. A flexible technology platform that is available to collect, mine, and analyze data²⁰

Any academic analytics effort begins with leaders who are committed to decision-making based on institutional data. Analytics can be used to examine key institutional issues, such as enrollment or retention, which by their nature are complex and often sensitive, but the decision to move forward with analytics depends on knowledgeable champions among senior administrators.

The second critical component to building an academic analytics initiative is staffing. Staff members involved in analytics efforts often include database administrators, institutional researchers, educational researchers, programmers, and domain specialists (e.g., student services, retention, development/advancement). Academic computing staff may be needed to collect information from various academic systems such as the CMS. The team must have the skill to build predictive models based on institutional data guided by educational research. Other staff may be needed to focus on policy development and clarify who has access to the data, how the data can be used, and which data-security models are required. Since analytics requires data analysis, institutions will need to invest in effective training to produce skilled analysis staff. Obtaining or developing skilled staff may present the largest barrier and the greatest cost to any academic analytics initiative. Whether such staff are added to existing institutional research units or are cultivated in the IT organization, student affairs divisions, or academic units will depend on the organizational culture and the locus of resources.

The third element in any academic analytics project is technology. A data warehouse is the key component of the technology infrastructure, housing information from a variety of sources in a common structure that enables data analysis. To populate the data warehouse, the institution will need to build a "bridge" between the application and the warehouse. For some applications, standard interfaces facilitate the transfer of data. For other applications, the interface development requires significant programming effort.

Piecing together a coherent academic analytics effort can be difficult, requiring support from many units: enrollment management, institutional research, IT, the registrar's office, academic divisions, student affairs, and more. Standards must be agreed upon for the data (e.g., is enrollment based on headcount on day seven after the start of the semester or on day ten?). Extracting information from academic systems requires careful analysis and programming effort. Building the appropriate models requires staff with statistics and educational research backgrounds. Creating interventions requires domain knowledge (e.g., advising, retention) and advising/counseling skills. For institutions to be successful in academic analytics projects, IT leaders must build a coalition of people.

Considerations and Concerns

With analytics being used to address complex institutional issues, concerns are likely to arise that the complexity has been reduced to "a number," potentially resulting in oversimplification or insensitivity. In particular, analytics projects that focus on teaching and learning need to be approached deliberately. As these projects are developed, refined, and implemented to support academic success, several concerns must be addressed:

Big Brother: The notion that a person or institution can track the actions of individuals within a software application will be welcomed by some and will be threatening to others. Who determines which data is collected? What obligation does the institution have to inform faculty and/or students that their behavior within an application is being tracked? Does an individual need to provide formal consent before data can be collected and/or analyzed? Does an individual have an option to "opt out" of an analytics project?

Holistic View: Although analytics produces a prediction based on the data available, no prediction can take into account all the possible causes of success or lack of success (problems at home, financial difficulty, and so on). In addition, some will be skeptical of the ability of "a number" to account for the interpersonal relationships and personal growth that come from attending a college or university, irrespective of grades or graduation.

Faculty Involvement: As analytics enters the academic realm, ensuring faculty involvement is critical?in the measures as well as the actions that address at-risk students' needs. Faculty are key to "interventions" such as inviting students to office hours, providing additional practice quizzes, or encouraging participation in tutorial programs. For some faculty, analytics may provide a valuable insight into which students are struggling or which instructional approaches are making the greatest impact.

Profiling: One potential use of analytics is to create a profile of successful, or unsuccessful, students. The profile may be used to prompt interventions or to predict student success. Does the profile bias people's expectations and behaviors? Should the institution even create profiles that lead to generalizations about students? Are there profile uses that should be prohibited?

Data Privacy: The data that is collected and analyzed may be protected by federal, state, and institutional privacy regulations. For example, the Family Educational Rights and Privacy Act (FERPA) of 1974 ensures privacy except in cases of "legitimate educational interests."²¹ Does the institution need approval before data is used? Who has access to the data during model development and implementation? Will the information be shared?

Data Stewardship: The data for any academic analytics project may derive from a wide range of sources. How is the data preserved, secured, and shared? Once a data warehouse has been established, can anyone use it for any purpose? If not, how are use decisions made?

Information Sharing: Initial academic analytic models have produced a probability of student success. Should the results be shared with the student, faculty, or other staff? Who makes the determination of what and how information is shared?

Obligation to Act: If the academic analytics model provides a probability of student success, what is the obligation of faculty, students, and institutions to act on that information? With whom does the obligation to act lie? How is the responsibility shared among different groups?

Distribution of Resources: With quantifiable prediction models, the distribution of resources to those who most need them may emerge as an issue. Will access to support services be limited to those with the greatest need, or will anyone who has interest be able to receive help? Who receives priority if resources are limited?

Analytics can be a powerful tool for higher education, but analytics can also magnify existing value conflicts and introduce new ones. Higher education will need to balance the expectations of faculty (are faculty required to intervene in all situations where a student is at risk?), federal privacy laws (who can view student information or have access to predictions of success?), and the institution's own philosophy of student development (should the institution endorse a sink-or-swim or a nurturing environment?).

Potential Impact

Richer data sets, new ways of extracting and organizing data, more sophisticated predictive models, and additional research will drive the evolution of analytics. As the practice of analytics is refined, colleges and universities can place more and better information into the hands of a greater number of people, enabling informed decision-making.

With the public demand for documented learning outcomes and increased retention, academic analytics can contribute to institutional action. Data from the CMS, e-portfolios, student response systems, course podcast downloads, and similar applications can be used in academic analytics. And the focus of future analytics efforts can shift from predicting who is going to be successful to customizing learning environments so that the most effective instructional approaches are used for each student. Eventually, institutions may be able to provide unique learning paths, matching instructional activities to a student's learning needs.

As higher education continues to implement systems that collect a wide range of data, IT units will be called on to support analytics efforts. As a result, IT leaders will find that new expectations are being placed on their units. Staff will be required to have more than the traditional IT skills. They will need to be adept at mining data, understanding the nature of the data, creating metadata to provide long-term data management, analyzing data from multiple sources, and developing models that can be used for decision-making and action. By sharing data that is collaboratively interpreted and acted on, IT can help institutions bridge academic affairs and student affairs. As colleges and universities respond to the demand for greater accountability in higher education, the emerging practice of academic analytics is likely to become a new, highly useful tool for a new, highly demanding era.

1. U.S. Department of Education, by the National Commission on Excellence in Education, A Nation at Risk: The Imperative for Educational Reform, a Report to the Nation and the Secretary of Education, April 1983, http://www.ed.gov/pubs/NatAtRisk/index.html.

2. "Did You Know?/Shift Happens," created by Karl Fisch, modified by Scott McLeod, http://www.youtube.com/watch?v=xHWTLA8WecI, sources: http://www.lps.k12.co.us/schools/arapahoe/fisch/didyouknow/sourcesfordidyouknow.doc; blog post: http://thefischbowl.blogspot.com/2006/08/did-you-know.html.

3. Thomas L. Friedman, The World Is Flat: A Brief History of the Twenty-First Century, 1st updated and expanded ed. (New York: Farrar, Straus and Giroux, 2006).

4. "Education at a Glance, 2005," http://www.oecd.org/dataoecd/41/13/35341210.pdf.

5. Anne K. Walters, "Minority Education Should Be a Priority, Report Says," Chronicle of Higher Education, November 18, 2005.

6. Debra Humphreys, "Achieving Equity as Generation Y Goes to College: New Data," Diversity Digest (Spring/Summer 00), http://www.diversityweb.org/Digest/Sp.Sm00/geny.html

7. G. Orfield, D. Losen, J. Wald, and C. Swanson, Losing Our Future: Our Minority Youth Are Being Left Behind by the Graduation Rate Crisis (Cambridge, Mass.: The Civil Rights Project at Harvard University, 2004), p. 2, http://www.urban.org/UploadedPDF/410936_LosingOurFuture.pdf.

8. Kevin Carey, "One Step from the Finish Line: Higher College Graduation Rates Are Within Our Reach" (The Education Trust, January 2005), http://www2.edtrust.org/NR/rdonlyres/12656449-03FD-4F3F-A617-920E58F009C0/0/one_step_from.pdf.

9. Daniel E. Hecker, "Occupational Employment Projections to 2014," Monthly Labor Review (November 2005), http://www.bls.gov/opub/mlr/2005/11/art5full.pdf.

10. Patrick J. Kelly, "As America Becomes More Diverse: The Impact of State Higher Education Inequality" (National Center for Higher Education Management Systems, November 2005), http://www.higheredinfo.org/raceethnicity/InequalityPaperNov2005.pdf; June Kronholz, "Economic Time Bomb: U.S. Teens Are among the Worst at Math," Wall Street Journal Online, December 7, 2004, http://www.mathnasium.com/press/wsj1204.pdf.

11. "Fact Sheet: The National Survey of America's College Students" (American Institutes for Research, January 19, 2006), http://www.air.org/news/documents/collegeliteracyfactsheet.htm.

12. "Education at a Glance, 2005," http://www.oecd.org/dataoecd/41/13/35341210.pdf.

13. Kathleen Morley and Tom Bohannon, "Applications of Data Mining in Institutional Research," presentation, Association for Institutional Research Annual Conference, Cincinnati, May 2000.

14. Cali M. Davis, J. Michael Hardin, Tom Bohannon, and Jerry Oglesby, "Data Mining Applications in Higher Education," unpublished book chapter (2007).

15. Awards include the 2004 EDUCAUSE Information Technology Solutions Award, the 2005 National Council for Student Development Exemplary Practice Award, and the 2007 Bellwether Award for Instructional Programs and Services.

16. "Student Success Plan and Early Alert System: Individual Learning Plan, Holistic Counseling, and Intervention Model," Sinclair Community College Presentation, March 5, 2007, http://www.sinclair.edu/stservices/edu/pub/Generic%20SSP-EAP%20Pres.ppt.

17. Sinclair Community College, Office of Research, Analytics, and Reporting, "Fall 2006 ILP Results," March 2007, http://www.sinclair.edu/stservices/edu/pub/FA06%20ILP%20Results.pdf.

18. Rebecca Pollard Cole, Margot Saltonstall, and Paul A. Gore Jr., "Assessing Student Readiness to Promote Student Success: A Campus Collaboration," unpublished manuscript developed from a paper presented at the 25th Annual Conference on The First-Year Experience, Atlanta, Georgia, February 24-28, 2007.

19. Steven Robbins, Jeff Allen, Alex Casillas, Adaeze Akamigbo, Margot Saltonstall, Rebecca Cole, Eileen Mahoney, and Paul Gore, "Associations of Resource and Service Utilization, Risk Level, and College Outcomes," unpublished paper (2007).

20. Philip J. Goldstein, with Richard N. Katz, "Academic Analytics: The Uses of Management Information and Technology in Higher Education," EDUCAUSE Center for Applied Research (ECAR) Study, vol. 8 (2005), key findings publicly available: http://www.educause.edu/ir/library/pdf/ecar_so/ers/ers0508/EKF0508.pdf.

21. The specific FERPA provision (�99.31) under which a student's prior consent is not required to disclose information is as follows: "An educational agency or institution may disclose personally identifiable information from an education record of a student without the consent required by �99.30 if the disclosure meets one or more of the following conditions: (1) The disclosure is to other school officials, including teachers, within the agency or institution whom the agency or institution has determined to have legitimate educational interests" http://www.ed.gov/policy/gen/reg/ferpa/rights_pg19.html#17. This would seem to clear the anticipated federal legislative hurdle. However, whether there is an institutional will and constituent buy-in to create and share "actionable information" from diverse sources is something else altogether. Because each institution is obligated to interpret and publicly declare what "legitimate educational interests" means, this is an aspect of FERPA that has beguiled registrars and academic administrators for some time in aligning local information-sharing policies.

# data[].attributes.attachment_link.keywords[]
academic analytics: a new tool for a new era

# data[].attributes.attachment_link.title
Academic Analytics: A New Tool for a New Era

# data[].attributes.attachment_link.description
© 2007 John P. Campbell, Peter B. DeBlois, and Diana G. Oblinger EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40-57 John P. Campbell is an EDUCAUSE Learning Initiative (ELI) Scholar in Residence and Associate Vice President for Teaching and Learning Technologies at Purdue University, where he is responsible for all aspects of instructional technology.

# data[].attributes.body
Iniciativa interessante da Netflix sob diversos aspectos. Funcionou de 2006 a 2011. Serve de inspiração para os Desafios Enap.

# data[].attributes.attachment_link.readable_text
Overview:

We're quite curious, really. To the tune of one million dollars.

Netflix is all about connecting people to the movies they love. To help customers find those movies, we've developed our world-class movie recommendation system: Cinematch. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. We use those predictions to make personal movie recommendations based on each customer's unique tastes. And while Cinematch is doing pretty well, it can always be made better.

Now there are a lot of interesting alternative approaches to how Cinematch works that we haven't tried. Some are described in the literature, some aren't. We're curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business.

So, we thought we'd make a contest out of finding the answer. It's "easy" really. We provide you with a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.) If you develop a system that we judge most beats that bar on the qualifying test set we provide, you get serious money and the bragging rights. But (and you knew there would be a catch, right?) only if you share your method with us and describe to the world how you did it and why it works.

Serious money demands a serious bar. We suspect the 10% improvement is pretty tough, but we also think there is a good chance it can be achieved. It may take months; it might take years. So to keep things interesting, in addition to the Grand Prize, we're also offering a $50,000 Progress Prize each year the contest runs. It goes to the team whose system we judge shows the most improvement over the previous year's best accuracy bar on the same qualifying test set. No improvement, no prize. And like the Grand Prize, to win you'll need to share your method with us and describe it for the world.

There is no cost to enter, no purchase required, and you need not be a Netflix subscriber. So if you know (or want to learn) something about machine learning and recommendation systems, give it a shot. We could make it really worth your while.

Terms and Conditions in a Nutshell

Contest begins October 2, 2006 and continues through at least October 2, 2011.

Contest is open to anyone, anywhere (except certain countries listed below).

You have to register to enter.

Once you register and agree to these Rules, you'll have access to the Contest training data and qualifying test sets.

To qualify for the $1,000,000 Grand Prize, the accuracy of your submitted predictions on the qualifying set must be at least 10% better than the accuracy Cinematch can achieve on the same training data set at the start of the Contest.

To qualify for a year's $50,000 Progress Prize the accuracy of any of your submitted predictions that year must be less than or equal to the accuracy value established by the judges the preceding year.

To win and take home either prize, your qualifying submissions must have the largest accuracy improvement verified by the Contest judges, you must share your method with (and non-exclusively license it to) Netflix, and you must describe to the world how you did it and why it works.

Still interested? On to the fine print then! And, because of the serious amount of money, there is some important legalese you need to attend to...

Contest Rules, Terms and Conditions

Term

The Contest entry period commences on October 2, 2006 (00:00:00 UTC) and continues until the Grand Prize is awarded (the "Contest Term"). No additional registrations or Contest submissions of qualifying set predictions will be accepted after the close of the Contest Term. Netflix reserves the right to cancel the Contest after October 2, 2011 in its sole discretion.

Who can participate?

Participants are welcomed either as individuals or as teams made up of several persons. In the case of teams, one person must be designated as the team leader and will be solely responsible for receiving communications from and communicating with Netflix. Awarded Prizes will be distributed among the Participants and affiliated organizations according to the instructions of the team leader. We recommend teams determine ahead of time how Prizes, if any, would be shared among team members. Participants can represent themselves or be the authorized representatives of a company or academic institution.

If a Participant is an employee of a corporation or academic institution, enrolled as a student or representing their employer or academic institution in this Contest, it is their sole responsibility to review, understand and abide by their employer's or academic institution's policies regarding eligibility to participate in the Contest. If a Participant is found to be in violation of their school's or employer's policies, they will be disqualified from entering the Contest and being awarded or retaining Contest Prizes. Netflix disclaims any and all liability or responsibility for disputes arising between a student or employee and their school or employer related to this Contest.

Current and former employees, officers, contractors and agents of Netflix, its subsidiaries and equity affiliates, together with members of their immediate families (parent, child, sibling and spouse of each) and those living in their same households, or Netflix and independent contractors supporting the Contest, are ineligible to enter and participate in the Contest or be awarded or retain any Contest Prize.

Residents of Cuba, Iran, Syria, North Korea, Myanmar (formerly Burma) and Sudan are ineligible to participate. The Contest is void in these countries and where prohibited or restricted by law. Netflix reserves the right to limit, or restrict upon notice, participation in the Contest to any person at any time for any reason.

Registration

To participate in the Contest, Participants must accurately and truthfully complete the Participant registration information request online at www.NetflixPrize.com (the "Site"). To register, Participants must provide a team name, a list of all team Participants, and identify the team leader, providing each Participant's full name, and e-mail address, affiliation, if any, and country. Any publicly displayed team data, including team names, may be changed if deemed inappropriate by Netflix in its sole discretion. Each Participant must consent to be bound by these Rules. Individuals may be members of multiple teams; teams with an identical set of members are not permitted. All responses must be in English. All entry information and submissions shall be deemed collected and judged in the United States. Netflix will not reveal Participant identities unless and until a team wins a prize. In any case, Netflix will not use a team's registration information for commercial purposes.

The Prize Structure

Upon registration, Participants may access the Contest training data and qualifying test sets. The training data set consists of more than 100 million ratings from over 480 thousand randomly-chosen, anonymous customers on nearly 18 thousand movie titles. The data were collected between October, 1998 and December, 2005 and reflect the distribution of all ratings received by Netflix during this period. The ratings are on a scale from 1 to 5 (integral) stars. To protect customer privacy, all personal information identifying individual customers has been removed and all customer ids have been replaced by randomly-assigned ids. The date of each rating and the title and year of release for each movie are provided. No other customer or movie information is provided. No other data were employed to compute Cinematch's accuracy values used in this Contest.

In addition to the training data set, a qualifying test set is provided containing over 2.8 million customer/movie id pairs with rating dates but with the ratings withheld. These pairs were selected from the most recent ratings from a subset of the same customers in the training data set, over a subset of the same movies.

Eligible algorithms must provide predictions for all the withheld ratings for each customer/movie id pair in the qualifying set. Each submitted prediction set must be uploaded to the Site in the format specified in the dataset. Submissions that fail to follow these requirements will be rejected. A day must elapse between each team's submissions.

The qualifying set is divided into two disjoint subsets containing randomly selected pairs from the qualifying set. The assignment of pairs to these subsets is not disclosed. The Site will score each subset by computing the square root of the averaged squared difference between each prediction and the actual rating (the root mean squared error or "RMSE") in the subset, rounded to the nearest .0001.

The RMSE for the first "quiz" subset will be reported publicly on the Site; the RMSE for the second "test" subset will not be reported publicly but will be employed to qualify a submission as described below. The reported RMSE scores on the quiz subset provide a public announcement that a potential qualifying score has been reached and provide feedback to Participants on both their absolute and relative performance.

Submitted prediction sets will not be made public during the Contest Term. Submitted prediction sets become the property of Netflix, which reserves the right to perform additional assessments on the predictions. However, these additional assessments shall have no influence on the decision to qualify a submission for either the Grand Prize or a Progress Prize.

To prevent certain inferences being drawn about the Netflix customer base, some of the rating data for some customers in the training and qualifying sets have been deliberately perturbed in one or more of the following ways: deleting ratings; inserting alternative ratings and dates; and modifying rating dates. However, the Cinematch RMSE measured on the final, perturbed dataset does not differ significantly from the RMSE measured on the unperturbed dataset for the purposes of Grand or Progress Prize qualification described below. The RMSE values reported below represent the RMSE measured on both the perturbed and unperturbed datasets to the precision specified above.

As of the start of the Contest, the RMSE of Cinematch on the quiz subset, based on training the Cinematch algorithm using the training data set alone, was 0.9514. The RMSE of Cinematch on the test subset, based on training the Cinematch algorithm using the training data set alone, was 0.9525.

To qualify for the Grand Prize the RMSE of a Participant's submitted predictions on the test subset must be less than or equal to 90% of 0.9525, or 0.8572 (the "qualifying RMSE"). After three (3) months have elapsed from the start of the Contest, when the RMSE of a submitted prediction set on the quiz subset improves beyond the qualifying RMSE an electronic announcement will inform all registered Participants that they have thirty (30) days to submit additional candidate prediction sets to be considered for judging.

At the end of this period, qualifying submissions will be judged (see Judging below) in order of the largest improvement over the qualifying RMSE on the test subset. In the case of tied RMSE values on the test subsets, the submission received earliest by the Site will be judged first. If no qualifying submission can be verified or no Prize can be awarded, the Contest will reopen and new qualifying submissions will be considered according to the protocol described above. The decisions of the Contest judges are final.

To qualify for a Progress Prize a Participant must have submitted a prediction set within the twelve-month period following the anniversary date of the prize start (October 2, 00:00:00 UTC) and before the end of the Contest Term. An electronic announcement, sent on September 2nd each year, will inform all registered Participants that they have thirty (30) days to submit additional candidate prediction sets to be considered for Progress Prize judging. To qualify for a year's Progress Prize, the RMSE of your submitted predictions on the test subset must be less than or equal to 99% the RMSE of the last Progress Prize winner. The judging of the Progress Prize then follows the protocol for judging the Grand Prize described above. The minimum qualifying RMSE value for the Progress Prize will be posted each year. For the first year, qualifiers for the Progress Prize must be less than or equal to 0.9430, 99% of Cinematch's current accuracy on the test subset. If no entry in a particular year qualifies, a Progress Prize will not be awarded for that year. However, the Progress Prize contest remains open for subsequent years or until the Contest Term is over. Winners of Progress Prizes remain eligible to qualify and win subsequent Progress Prizes as well as the Grand Prize.

Improvements to Cinematch made during the Contest Term will not affect the requirements for qualifying or winning either the Grand or Progress prize.

During the Contest, participants may not redistribute the training data set or qualifying sets without the express written permission of Netflix. These data may be used for research purposes but may not be used for any commercial purpose. At the end of the Contest, the ratings for the qualifying set will be released and the combined training data and qualifying sets will become available upon request at the Machine Learning Archive at UC Irvine.

Eligibility of Qualifying Algorithms

An eligible algorithm must be originally developed or implemented (e.g., must not violate or infringe on any applicable law or regulation or third-party rights); must be written in English; and must not require any third-party software or licenses, payment on the part of Netflix, or otherwise prevent Netflix from exercising the rights granted hereunder.

Judging of Qualifying Algorithms

Upon qualifying, as described above, the Participant is required to submit within one (1) week for judging a description of their algorithm along with all source code. The Participant warrants that the source code is either fully or substantially developed and functions or will function as represented by the description. Failure to deliver both the description and source code within one (1) week will disqualify that entry and additional qualifying entries will be considered.

The description of the algorithm must be written in English, with mathematical formulae as necessary. The description must be written at a level sufficient for a practitioner in computer science to reproduce the results obtained by the contestant. It must describe substantially all the steps performed both by any learning component and by the prediction component that produced the submitted predictions. If applicable, it should detail any initialization and convergence properties of the method as well as tuning procedures for setting any free parameters of the algorithm. If applicable, it should also list any prior applications of the techniques employed by the algorithm. Finally, it must provide an analysis of how the algorithm achieved its improvement over previous Progress Prize winners, if any.

Qualified entries will be reviewed, considered and judged by a panel of senior Netflix engineers and qualified independent judges. Participants shall reasonably cooperate with the judges in any verification activities. The judge's sole activity during verification will be to ensure that the provided algorithm description and source code could reasonably have generated the prediction sets submitted. Application of the foregoing judging criteria to eligible Contest entries in the Contest shall be at the judges' reasonable discretion and, as to elements of the judging criteria involving matters of subjectivity, at the judges' sole discretion.

In order for Netflix to verify the submitted algorithm, each Participant will be required to grant to Netflix (including its affiliates and subsidiaries, employees, agents, and contractors), and any testing partners, an irrevocable, royalty free, fully paid up, worldwide non-exclusive license under the Participants' copyrights, patents or other intellectual property rights to use, review, assess, test and otherwise analyze the submissions (prediction sets, algorithm descriptions, and source code) and all their content in connection with this Contest. In addition, Participants waive on an irrevocable basis all moral rights in the submissions and all their content. Netflix shall be free to use for any purpose in any and all media now known or hereinafter developed in any territory in perpetuity the residuals resulting from access to or work with Participants' Applications. The term "residuals" means information in intangible form, which is retained in memory by persons who have had access to the submissions, including ideas, concepts, know-how, or techniques contained therein. Netflix shall not have any obligation to limit or restrict the assignment of such persons or to pay royalties for any work resulting from the use of residuals.

After qualifying for either the Grand or Progress Prize and being verified by the Contest judges, as a condition to receiving either Prize, the winning individual and/or team must grant to Netflix (including its affiliates and subsidiaries, employees, agents, and contractors), an irrevocable, royalty free, fully paid up, worldwide non-exclusive license under the Participants' copyrights, patents or other intellectual property rights in the winning algorithm ("Winning Algorithm") to reproduce, distribute, display, and create derivative works from the Winning Algorithm and also to make, have made, use, sell, offer for sale, and import products that would otherwise infringe the Winning Algorithm. Except as encompassed in the concept of "have made", this license will not include the right to grant further licenses or sublicenses.

Once an algorithm is verified by the judges and the license is granted, Netflix will award the appropriate Contest Prize to the winning Participant. The description of the algorithm, but not the source code, will be published on the Site along with the names of the team members who won the Prize.

Award of Contest Prizes

Contest Prizes:

1. Grand Prize: $1,000,000 (USD) Cash

2. Progress Prizes: $50,000 (USD) Cash each award

A Participant is eligible to win and be awarded only one (1) Grand Prize. A Participant is eligible to win and be awarded any and all Progress Prizes available during the Contest Term. In the event a qualified Participant cannot be reasonably notified, verified or confirmed based on two (2) attempts (and further attempts at Netflix's sole discretion), a Contest Prize is declined or a potential winner is not in compliance with these Rules or eligible for award of a Contest Prize for whatever reason, the Contest will remain open and an alternate potential prize winner will be chosen and verified in accordance with these Rules.

Awarded Grand and Progress Prizes will be distributed amongst team members and affiliated organizations in a lump sum payments according to the winning team leader's instructions 4-6 weeks after Netflix has declared a winner. Return of a Contest Prize or failure of Contest Prize delivery will result in disqualification and an alternative potential winner will be chosen and verified in accordance with these Rules.

Any costs or expenses associated with the acceptance and use of any Contest Prize monies are the sole responsibility of the Contest Prize winners. All Contest Prize details are at Netflix's sole discretion. A Contest Prize winner may make no substitutions or assignment of a Contest Prize other than as specified in these Rules.

Prize monies should be retained by individuals only in conformity with any applicable policies of their employers or academic institutions, regarding participation in and receipt of promotional consideration relating to the Contest and receipt and retention of Contest Prizes. If an employer's or school's policies are applicable, it is the Participant's sole and ultimate responsibility, in consultation with their employer or school, to determine how and if any Contest Prize will be retained and/or distributed and accounted for and Netflix assume no responsibility for the decisions made by such employers or schools regarding this issue.

General Contest Terms and Conditions

A. Delivery. A Participant is solely responsible for the equipment and Internet access required to connect to the Site and/or participate in the Contest. Netflix will not be responsible for lost, late, incomplete, stolen, misdirected, undelivered, delayed, inaccurate, garbled or illegible materials, entries, email or mail; or for any computer, telephone, cable, network, satellite, electronic or Internet hardware or software malfunctions, failures, connections, or availability, or for garbled or jumbled transmissions, or for service provider/Internet/Web site/use net accessibility or availability, traffic congestion, or unauthorized human intervention, or for inaccurate capture of any information, or the failure to capture any such information. Netflix is not responsible for any incorrect or inaccurate information, whether caused by printing errors, Site users, tampering, hacking, or by any of the equipment or programming associated with or utilized in the Contest, and is not responsible for any error, omission, interruption, deletion, defect, delay in operation or transmission, communications line failure, theft or destruction or unauthorized access to the Contest Site or any communications means or for any other error of failure of any kind in connection with or relating to this Contest, whether caused by any human, typographical, printing, mechanical, computer or other means, including, without limitation, errors or failures which may occur in connection with the processing or storing of information, the administration of the Contest, the uploading, processing validating, posting and/or judging of entries, the announcement of the Contest Prizes or in any Contest-related materials. Participants located outside of the U.S. should note that delivery/and or custom's delays could occur. Netflix is not responsible for injury or damage to Participants' or to any other person's computer related to or resulting from participating in this Contest or downloading materials from or use of the Site. Entries delayed past the submission deadline of the Contest will not be accepted for review.

B. Entries; Verification/Audit; Participant Cooperation. All activities relating to Participant's participation in the Contest and material submitted are subject to verification and/or auditing for compliance with these Rules and Participants agree to reasonably cooperate with Netflix concerning verification and/or auditing. In the event that Contest verification activity or an audit evidences non-compliance with the Rules or official Contest communications, as determined in Netflix's reasonable discretion, a Participant's continuing participation in any aspect of the Contest may be suspended or terminated.

Netflix reserves the right, in its sole discretion, to reasonably suspend or cancel the Contest should any virus, bug and/or other cause corrupt the administration, security or proper operation of the Contest. In the event the Contest is cancelled, Netflix may, at its sole discretion, determine the winners from among all eligible and non-suspect validated entries received up to the time of such action using the judging procedure outlined above.

C. Participants' Affidavits, Contest Consent and Releases. As a condition of winning the Contest and receipt and acceptance of a Contest Prize, a Participant will be required to complete, sign and return a non-exclusive grant of license to Netflix, an Affidavit of Eligibility and Liability Release and where lawful, a Publicity and Submission Release and License ("Publicity Release"), within two (2) weeks of the start of verification. The Publicity Release will include consent by a winner and/or his or her employer/principal for Netflix and those acting under its control to use a winner's name, picture, voice, likeness, and the name, description and Application comprising winner's entry, worldwide in perpetuity, for the purposes of Contest-related advertising, promotion and publicity, and additionally including those specific purposes disclosed on the Site, without further compensation, notification or permission, unless prohibited by law. If the judges are unable to verify the qualifying entry, for whatever reason, the signed Affidavits, Releases, and the non-exclusive License shall be void. Failure to complete and sign these documents within two (2) weeks will disqualify that entry and additional qualifying entries will be considered.

In addition, winning U.S. and foreign Participants will be required to complete applicable U.S. tax withholding-related documentation and, if necessary, provide proof of foreign status, as beneficiaries of U.S.-sourced promotional consideration.

Without limitation, if the foregoing documents are not executed by the prescribed persons noted therein and returned timely, or if notification is returned as non-deliverable, or if a Participant is found to be ineligible or otherwise not in compliance with these Rules, a Contest Prize may be forfeited at the sole discretion of Netflix and, at Netflix's discretion, the next eligible Participant in accordance with these Rules will be notified. Contest Prizes won by team leaders who are eligible minors in their state, province or country of residence shall be awarded to eligible minor's parent or legal guardian who must sign and return all required documentation and otherwise fulfill all requirements imposed on winners set forth herein.

D. Tax Consequences. Participation and receipt of benefits from the Contest will have tax consequences. Participants should speak with their tax advisors prior to participation and receipt and acceptance of Contest Prizes to prevent any undesired results. Should there be any local tax liability for participation in the Contest or the receipt of a Contest Prize, or costs or expenses relating to participation in the Contest or for the use and enjoyment of any Contest Prizes, or for any other reason, such taxes and/or expenses are the sole responsibility of the Participant. Netflix will issue Contest-related tax reporting, as may be required by applicable laws and regulations. As a condition of winning the Contest and receipt and use of any Contest Prize, Participants agree to self-report to applicable local taxing authorities, as may be required by local laws.

E. Rules Amendment and Interpretation. The Contest and its continuing terms and conditions, benefits and participation are offered to Participants at the sole discretion of Netflix and its affiliated companies. Netflix reserves the right to amend or interpret the Rules or official Contest communications and any element or elements arising under or relating to the Contest at any time, upon published notice to Participants on the Site. A Participant shall be deemed to have notice of any such amendments or interpretations upon publication of the same on the Site and shall be deemed to have accepted such amendments or interpretations by virtue of a Participant's continuing participation in the Contest. Should a Participant not wish to continue to participate in the Contest pursuant to the prevailing Rules, as amended or interpreted, a Participant may terminate participation in the Contest by abandoning their registration on the Site. All submitted material remain the property of Netflix.

F. Rule Compliance Interpretation; Adjudication and Remedial Process. Netflix reserves the right to make all decisions, in its discretion, arising out of or relating to instances of suspected abuse, fraud, error or anomalies in the operation of the Contest, including the Site, or any other interpretation, activity or suspected violation relating to the Contest, and such decisions by Netflix are final and binding. Aggrieved Participants further agree, prior to resort to formal dispute resolution specified below, to submit to Netflix via certified mail (The Netflix Prize, 100 Winchester Circle, Los Gatos, CA 95032) for internal adjudication a written statement concerning and fully describing any grievance for expeditious review and formal response by Netflix. The formal response and decision by Netflix shall be final and binding on a Participant, absent clear and convincing evidence that Netflix has acted in an arbitrary or capricious manner. Netflix's decision of a particular matter with an individual Participant shall not bind Netflix as to decisions or adjudications of similar future matters, if there is a rational basis for distinguishing the facts and circumstances of such matters. A Participant must first exhaust the internal adjudication process for grievances before proceeding to formal dispute resolution. The foregoing deference and evidentiary standards relating to Netflix's decision-making shall apply in the below-described formal dispute resolution.

Except where prohibited, Participants in the Contest agree that: (1) any and all disputes, claims, and causes of action arising out of or connected with this Contest, or any benefits received, or the administration of the Contest not resolved through internal adjudication shall be resolved individually, without resort to any form of class action, and exclusively by arbitration, to take place in the State of California pursuant to the Rules of the American Arbitration Association, then effective, (2) any and all claims, judgments and awards shall be limited to actual out-of-pocket costs incurred, including costs associated with participating in the Contest and in no event shall Participant be entitled to receive attorneys&rsrsquo; fees or other legal costs; and (3) under no circumstances will a Participant be permitted to obtain awards for and Participants hereby waive all rights to claim punitive, incidental and consequential damages and any other damages, other than for actual out-of-pocket expenses, and any and all rights to have damages multiplied or otherwise increased. Except where prohibited, all issues and questions concerning the construction, validity, interpretation and enforceability of these Rules, or the rights and obligations of Participant(s) and Netflix in connection with the Contest, shall be governed by, and construed in accordance with, the laws of the United States of America and the State of California, without regard for conflicts of law doctrine of any jurisdiction, and subject to resolution only in the City of San Jose, State of California, United States. By entry and participation in the Contest, Participants, for themselves and their schools/companies if applicable, agree that the City of San Jose, State of California, United State of America will be the exclusive forum for any formal dispute resolution.

G. NO WARRANTY. NETFLIX, ORGANIZERS AND THEIR SUBSIDIARIES, AFFILIATED COMPANIES AND ALL OF THEIR RESPECTIVE OFFICERS, DIRECTORS, EMPLOYEES, REPRESENTATIVES AND AGENTS (COLLECTIVELY, THE "RELEASED PARTIES") EXPRESSLY DISCLAIM ANY AND ALL WARRANTIES OR CONDITIONS OF ANY KIND (WHETHER EXPRESS, IMPLIED, STATUTORY OR OTHERWISE), INCLUDING BUT NOT LIMITED TO, IMPLIED WARRANTIES OR CONDITIONS OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. UNDER NO CIRCUMSTANCES SHALL RELEASED PARTIES BE HELD RESPONSIBLE OR LIABLE FOR A PARTICIPANT'S USE OF THE INFORMATION AND/OR PRODUCTS PROVIDED AND/OR MADE AVAILABLE THROUGH THE CONTEST OR FOR ERRORS OR ANOMALIES RESULTING IN THE UNINTENDED OR ERRONEOUS PARTICIPATION, AWARD OF CONTEST PRIZES OR OTHER BENEFITS UNDER THE CONTEST TO PARTICIPANTS. RELEASED PARTIES OFFER NO ASSURANCES, GUARANTEES OR WARRANTIES OR CONDITIONS THAT THE CONTEST OR CONTEST WEBSITES WILL BE UNINTERRUPTED OR ERROR-FREE AND DOES NOT GUARANTEE THE ACCURACY OR RELIABILITY OF ANY INFORMATION OBTAINED THROUGH THE CONTEST. RELEASED PARTIES ASSUME NO RESPONSIBILITY FOR ANY COMPUTER-RELATED DAMAGES DUE TO DOWNLOADING MATERIALS. RELEASED PARTIES WILL NOT BE LIABLE, AND ARE NOT RESPONSIBLE, FOR DAMAGES OF ANY KIND RELATED TO A PARTICIPANT'S PARTICIPATION OR INABILITY TO PARTICIPATE IN THE CONTEST, WHETHER THE DAMAGES ARE DIRECT, INDIRECT, INCIDENTAL, SPECIAL OR CONSEQUENTIAL. FURTHER, BY PARTICIPATING IN THE CONTEST AND/OR ACCEPTING A CONTEST PRIZE, A PARTICIPANT AGREES THAT RELEASED PARTIES SHALL NOT BE LIABLE FOR, AND WILL BE HELD HARMLESS BY PARTICIPANT AGAINST, ANY LIABILITY FOR ANY DAMAGE, INJURY OR LOSS TO PERSON (INCLUDING DEATH) OR PROPERTY DUE IN WHOLE OR IN PART, DIRECTLY OR INDIRECTLY, TO ACCEPTANCE, POSSESSION, USE OR MISUSE OF A CONTEST PRIZE, PARTICIPATION IN ANY CONTEST PRIZE-RELATED ACTIVITY, RELEASED PARTIES' USE OF ANY TENDERED ENTRY OR PARTICIPATION IN THIS CONTEST. SOME STATES DO NOT ALLOW THE LIMITATION OR EXCLUSION OF LIABILITY FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES; THEREFORE THE ABOVE LIMITATIONS OR EXCLUSIONS MAY NOT APPLY.

H. Indemnification. Participants agree to release, defend, indemnify and hold harmless Released Parties from and against, and accept all responsibility of any kind, including but not limited to financial, for any liability, claims, losses, damages or proceedings, including but not limited for death, (including reasonable attorneys' fees) relating to any actions taken by a Participant, including submission of Application algorithms and executables, or anyone else using a Participant's e-mail or team password or otherwise purporting to act on Participant's behalf in regard to the Contest (whether or not such use occurred with or without your permission). Participant also agrees to release, defend, indemnify and hold harmless the Released Parties from any and all liability, claims, losses, damages or proceedings, including but not limited for death (including reasonable attorneys' fees) relating to Prizes and any other matter in connection with a Participant's participation in the Contest or Netflix's or its designees or Netflix's or it's designees use of his/her Application.

These rules, terms and conditions specified here comprise the Contest's Official Rules and, together with the Contest Site's Terms of Use and official Contest communications or publications by Netflix, shall govern and apply to all participation and activity arising out of and relating to the Contest and use of the Site. These Rules cannot be modified or superseded except by Netflix, in its reasonable discretion.

# data[].attributes.attachment_link.description
We're quite curious, really. To the tune of one million dollars. Netflix is all about connecting people to the movies they love. To help customers find those movies, we've developed our world-class movie recommendation system: Cinematch. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies.

# data[].attributes.body
Artigo da NY Times Magazine de 2012. Interessante e mostra o quanto estamos atrasados na análise de dados na educação.

# data[].attributes.attachment_link.readable_text
Credit... Antonio Bolfo/Reportage for The New York Times

Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: "If we wanted to figure out if a customer is pregnant, even if she didn't want us to know, can you do that? "

Pole has a master's degree in statistics and another in economics, and has been obsessed with the intersection of data and human behavior most of his life. His parents were teachers in North Dakota, and while other kids were going to 4-H, Pole was doing algebra and writing computer programs. "The stereotype of a math nerd is true," he told me when I spoke with him last year. "I kind of like going out and evangelizing analytics."

As the marketers explained to Pole - and as Pole later explained to me, back when we were still speaking and before Target told him to stop - new parents are a retailer's holy grail. Most shoppers don't buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target - cleaning supplies, say, or new socks or a six-month supply of toilet paper. But Target sells everything from milk to stuffed animals to lawn furniture to electronics, so one of the company's primary goals is convincing customers that the only store they need is Target. But it's a tough message to get across, even with the most ingenious ad campaigns, because once consumers' shopping habits are ingrained, it's incredibly difficult to change them.

There are, however, some brief periods in a person's life when old routines fall apart and buying habits are suddenly in flux. One of those moments - the moment, really - is right around the birth of a child, when parents are exhausted and overwhelmed and their shopping patterns and brand loyalties are up for grabs. But as Target's marketers explained to Pole, timing is everything. Because birth records are usually public, the moment a couple have a new baby, they are almost instantaneously barraged with offers and incentives and advertisements from all sorts of companies. Which means that the key is to reach them earlier, before any other retailers know a baby is on the way. Specifically, the marketers said they wanted to send specially designed ads to women in their second trimester, which is when most expectant mothers begin buying all sorts of new things, like prenatal vitamins and maternity clothing. "Can you give us a list?" the marketers asked.

"We knew that if we could identify them in their second trimester, there's a good chance we could capture them for years," Pole told me. "As soon as we get them buying diapers from us, they're going to start buying everything else too. If you're rushing through the store, looking for bottles, and you pass orange juice, you'll grab a carton. Oh, and there's that new DVD I want. Soon, you'll be buying cereal and paper towels from us, and keep coming back."

The desire to collect information on customers is not new for Target or any other large retailer, of course. For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code - known internally as the Guest ID number - that keeps tabs on everything they buy. "If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we've sent you or visit our Web site, we'll record it and link it to your Guest ID," Pole said. "We want to know everything we can."

Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you've moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you've ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.) All that information is meaningless, however, without someone to analyze and make sense of it. That's where Andrew Pole and the dozens of other members of Target's Guest Marketing Analytics department come in.

Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a "predictive analytics" department devoted to understanding not just consumers' shopping habits but also their personal habits, so as to more efficiently market to them. "But Target has always been one of the smartest at this," says Eric Siegel, a consultant and the chairman of a conference called Predictive Analytics World. "We're living through a golden age of behavioral research. It's amazing how much we can figure out about how people think now."

The reason Target can snoop on our shopping habits is that, over the past two decades, the science of habit formation has become a major field of research in neurology and psychology departments at hundreds of major medical centers and universities, as well as inside extremely well financed corporate labs. "It's like an arms race to hire statisticians nowadays," said Andreas Weigend, the former chief scientist at Amazon.com. "Mathematicians are suddenly sexy." As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions.

This research is also transforming our understanding of how habits function across organizations and societies. A football coach named Tony Dungy propelled one of the worst teams in the N.F.L. to the Super Bowl by focusing on how his players habitually reacted to on-field cues. Before he became Treasury secretary, Paul O'Neill overhauled a stumbling conglomerate, Alcoa, and turned it into a top performer in the Dow Jones by relentlessly attacking one habit - a specific approach to worker safety - which in turn caused a companywide transformation. The Obama campaign has hired a habit specialist as its "chief scientist" to figure out how to trigger new voting patterns among different constituencies.

Researchers have figured out how to stop people from habitually overeating and biting their nails. They can explain why some of us automatically go for a jog every morning and are more productive at work, while others oversleep and procrastinate. There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.

Inside the brain-and-cognitive-sciences department of the Massachusetts Institute of Technology are what, to the casual observer, look like dollhouse versions of surgical theaters. There are rooms with tiny scalpels, small drills and miniature saws. Even the operating tables are petite, as if prepared for 7-year-old surgeons. Inside those shrunken O.R.'s, neurologists cut into the skulls of anesthetized rats, implanting tiny sensors that record the smallest changes in the activity of their brains.

An M.I.T. neuroscientist named Ann Graybiel told me that she and her colleagues began exploring habits more than a decade ago by putting their wired rats into a T-shaped maze with chocolate at one end. The maze was structured so that each animal was positioned behind a barrier that opened after a loud click. The first time a rat was placed in the maze, it would usually wander slowly up and down the center aisle after the barrier slid away, sniffing in corners and scratching at walls. It appeared to smell the chocolate but couldn't figure out how to find it. There was no discernible pattern in the rat's meanderings and no indication it was working hard to find the treat.

The probes in the rats' heads, however, told a different story. While each animal wandered through the maze, its brain was working furiously. Every time a rat sniffed the air or scratched a wall, the neurosensors inside the animal's head exploded with activity. As the scientists repeated the experiment, again and again, the rats eventually stopped sniffing corners and making wrong turns and began to zip through the maze with more and more speed. And within their brains, something unexpected occurred: as each rat learned how to complete the maze more quickly, its mental activity decreased. As the path became more and more automatic - as it became a habit - the rats started thinking less and less.

This process, in which the brain converts a sequence of actions into an automatic routine, is called "chunking." There are dozens, if not hundreds, of behavioral chunks we rely on every day. Some are simple: you automatically put toothpaste on your toothbrush before sticking it in your mouth. Some, like making the kids' lunch, are a little more complex. Still others are so complicated that it's remarkable to realize that a habit could have emerged at all.

Take backing your car out of the driveway. When you first learned to drive, that act required a major dose of concentration, and for good reason: it involves peering into the rearview and side mirrors and checking for obstacles, putting your foot on the brake, moving the gearshift into reverse, removing your foot from the brake, estimating the distance between the garage and the street while keeping the wheels aligned, calculating how images in the mirrors translate into actual distances, all while applying differing amounts of pressure to the gas pedal and brake.

Now, you perform that series of actions every time you pull into the street without thinking very much. Your brain has chunked large parts of it. Left to its own devices, the brain will try to make almost any repeated behavior into a habit, because habits allow our minds to conserve effort. But conserving mental energy is tricky, because if our brains power down at the wrong moment, we might fail to notice something important, like a child riding her bike down the sidewalk or a speeding car coming down the street. So we've devised a clever system to determine when to let a habit take over. It's something that happens whenever a chunk of behavior starts or ends - and it helps to explain why habits are so difficult to change once they're formed, despite our best intentions.

To understand this a little more clearly, consider again the chocolate-seeking rats. What Graybiel and her colleagues found was that, as the ability to navigate the maze became habitual, there were two spikes in the rats' brain activity - once at the beginning of the maze, when the rat heard the click right before the barrier slid away, and once at the end, when the rat found the chocolate. Those spikes show when the rats' brains were fully engaged, and the dip in neural activity between the spikes showed when the habit took over. From behind the partition, the rat wasn't sure what waited on the other side, until it heard the click, which it had come to associate with the maze. Once it heard that sound, it knew to use the "maze habit," and its brain activity decreased. Then at the end of the routine, when the reward appeared, the brain shook itself awake again and the chocolate signaled to the rat that this particular habit was worth remembering, and the neurological pathway was carved that much deeper.

The process within our brains that creates habits is a three-step loop. First, there is a cue, a trigger that tells your brain to go into automatic mode and which habit to use. Then there is the routine, which can be physical or mental or emotional. Finally, there is a reward, which helps your brain figure out if this particular loop is worth remembering for the future. Over time, this loop - cue, routine, reward; cue, routine, reward - becomes more and more automatic. The cue and reward become neurologically intertwined until a sense of craving emerges. What's unique about cues and rewards, however, is how subtle they can be. Neurological studies like the ones in Graybiel's lab have revealed that some cues span just milliseconds. And rewards can range from the obvious (like the sugar rush that a morning doughnut habit provides) to the infinitesimal (like the barely noticeable - but measurable - sense of relief the brain experiences after successfully navigating the driveway). Most cues and rewards, in fact, happen so quickly and are so slight that we are hardly aware of them at all. But our neural systems notice and use them to build automatic behaviors.

Habits aren't destiny - they can be ignored, changed or replaced. But it's also true that once the loop is established and a habit emerges, your brain stops fully participating in decision-making. So unless you deliberately fight a habit - unless you find new cues and rewards - the old pattern will unfold automatically.

"We've done experiments where we trained rats to run down a maze until it was a habit, and then we extinguished the habit by changing the placement of the reward," Graybiel told me. "Then one day, we'll put the reward in the old place and put in the rat and, by golly, the old habit will re-emerge right away. Habits never really disappear."

Luckily, simply understanding how habits work makes them easier to control. Take, for instance, a series of studies conducted a few years ago at Columbia University and the University of Alberta. Researchers wanted to understand how exercise habits emerge. In one project, 256 members of a health-insurance plan were invited to classes stressing the importance of exercise. Half the participants received an extra lesson on the theories of habit formation (the structure of the habit loop) and were asked to identify cues and rewards that might help them develop exercise routines.

The results were dramatic. Over the next four months, those participants who deliberately identified cues and rewards spent twice as much time exercising as their peers. Other studies have yielded similar results. According to another recent paper, if you want to start running in the morning, it's essential that you choose a simple cue (like always putting on your sneakers before breakfast or leaving your running clothes next to your bed) and a clear reward (like a midday treat or even the sense of accomplishment that comes from ritually recording your miles in a log book). After a while, your brain will start anticipating that reward - craving the treat or the feeling of accomplishment - and there will be a measurable neurological impulse to lace up your jogging shoes each morning.

Our relationship to e-mail operates on the same principle. When a computer chimes or a smartphone vibrates with a new message, the brain starts anticipating the neurological "pleasure" (even if we don't recognize it as such) that clicking on the e-mail and reading it provides. That expectation, if unsatisfied, can build until you find yourself moved to distraction by the thought of an e-mail sitting there unread - even if you know, rationally, it's most likely not important. On the other hand, once you remove the cue by disabling the buzzing of your phone or the chiming of your computer, the craving is never triggered, and you'll find, over time, that you're able to work productively for long stretches without checking your in-box.

Video

February 16, 2012 - In a preview of this Sunday's New York Times Magazine, Charles Duhigg details how some retailers profit by predicting major changes in your life.

Some of the most ambitious habit experiments have been conducted by corporate America. To understand why executives are so entranced by this science, consider how one of the world's largest companies, Procter & Gamble, used habit insights to turn a failing product into one of its biggest sellers. P.& G. is the corporate behemoth behind a whole range of products, from Downy fabric softener to Bounty paper towels to Duracell batteries and dozens of other household brands. In the mid-1990s, P.& G.'s executives began a secret project to create a new product that could eradicate bad smells. P.& G. spent millions developing a colorless, cheap-to-manufacture liquid that could be sprayed on a smoky blouse, stinky couch, old jacket or stained car interior and make it odorless. In order to market the product - Febreze - the company formed a team that included a former Wall Street mathematician named Drake Stimson and habit specialists, whose job was to make sure the television commercials, which they tested in Phoenix, Salt Lake City and Boise, Idaho, accentuated the product's cues and rewards just right.

The first ad showed a woman complaining about the smoking section of a restaurant. Whenever she eats there, she says, her jacket smells like smoke. A friend tells her that if she uses Febreze, it will eliminate the odor. The cue in the ad is clear: the harsh smell of cigarette smoke. The reward: odor eliminated from clothes. The second ad featured a woman worrying about her dog, Sophie, who always sits on the couch. "Sophie will always smell like Sophie," she says, but with Febreze, "now my furniture doesn't have to." The ads were put in heavy rotation. Then the marketers sat back, anticipating how they would spend their bonuses. A week passed. Then two. A month. Two months. Sales started small and got smaller. Febreze was a dud.

The panicked marketing team canvassed consumers and conducted in-depth interviews to figure out what was going wrong, Stimson recalled. Their first inkling came when they visited a woman's home outside Phoenix. The house was clean and organized. She was something of a neat freak, the woman explained. But when P.& G.'s scientists walked into her living room, where her nine cats spent most of their time, the scent was so overpowering that one of them gagged.

According to Stimson, who led the Febreze team, a researcher asked the woman, "What do you do about the cat smell?"

"It's usually not a problem," she said.

"Do you smell it now?"

"No," she said. "Isn't it wonderful? They hardly smell at all!"

A similar scene played out in dozens of other smelly homes. The reason Febreze wasn't selling, the marketers realized, was that people couldn't detect most of the bad smells in their lives. If you live with nine cats, you become desensitized to their scents. If you smoke cigarettes, eventually you don't smell smoke anymore. Even the strongest odors fade with constant exposure. That's why Febreze was a failure. The product's cue - the bad smells that were supposed to trigger daily use - was hidden from the people who needed it the most. And Febreze's reward (an odorless home) was meaningless to someone who couldn't smell offensive scents in the first place.

P.& G. employed a Harvard Business School professor to analyze Febreze's ad campaigns. They collected hours of footage of people cleaning their homes and watched tape after tape, looking for clues that might help them connect Febreze to people's daily habits. When that didn't reveal anything, they went into the field and conducted more interviews. A breakthrough came when they visited a woman in a suburb near Scottsdale, Ariz., who was in her 40s with four children. Her house was clean, though not compulsively tidy, and didn't appear to have any odor problems; there were no pets or smokers. To the surprise of everyone, she loved Febreze.

"I use it every day," she said.

"What smells are you trying to get rid of?" a researcher asked.

"I don't really use it for specific smells," the woman said. "I use it for normal cleaning - a couple of sprays when I'm done in a room."

The researchers followed her around as she tidied the house. In the bedroom, she made her bed, tightened the sheet's corners, then sprayed the comforter with Febreze. In the living room, she vacuumed, picked up the children's shoes, straightened the coffee table, then sprayed Febreze on the freshly cleaned carpet.

"It's nice, you know?" she said. "Spraying feels like a little minicelebration when I'm done with a room." At the rate she was going, the team estimated, she would empty a bottle of Febreze every two weeks.

When they got back to P.& G.'s headquarters, the researchers watched their videotapes again. Now they knew what to look for and saw their mistake in scene after scene. Cleaning has its own habit loops that already exist. In one video, when a woman walked into a dirty room (cue), she started sweeping and picking up toys (routine), then she examined the room and smiled when she was done (reward). In another, a woman scowled at her unmade bed (cue), proceeded to straighten the blankets and comforter (routine) and then sighed as she ran her hands over the freshly plumped pillows (reward). P.& G. had been trying to create a whole new habit with Febreze, but what they really needed to do was piggyback on habit loops that were already in place. The marketers needed to position Febreze as something that came at the end of the cleaning ritual, the reward, rather than as a whole new cleaning routine.

The company printed new ads showing open windows and gusts of fresh air. More perfume was added to the Febreze formula, so that instead of merely neutralizing odors, the spray had its own distinct scent. Television commercials were filmed of women, having finished their cleaning routine, using Febreze to spritz freshly made beds and just-laundered clothing. Each ad was designed to appeal to the habit loop: when you see a freshly cleaned room (cue), pull out Febreze (routine) and enjoy a smell that says you've done a great job (reward). When you finish making a bed (cue), spritz Febreze (routine) and breathe a sweet, contented sigh (reward). Febreze, the ads implied, was a pleasant treat, not a reminder that your home stinks.

And so Febreze, a product originally conceived as a revolutionary way to destroy odors, became an air freshener used once things are already clean. The Febreze revamp occurred in the summer of 1998. Within two months, sales doubled. A year later, the product brought in $230 million. Since then Febreze has spawned dozens of spinoffs - air fresheners, candles and laundry detergents - that now account for sales of more than $1 billion a year. Eventually, P.& G. began mentioning to customers that, in addition to smelling sweet, Febreze can actually kill bad odors. Today it's one of the top-selling products in the world.

Andrew Pole was hired by Target to use the same kinds of insights into consumers' habits to expand Target's sales. His assignment was to analyze all the cue-routine-reward loops among shoppers and help the company figure out how to exploit them. Much of his department's work was straightforward: find the customers who have children and send them catalogs that feature toys before Christmas. Look for shoppers who habitually purchase swimsuits in April and send them coupons for sunscreen in July and diet books in December. But Pole's most important assignment was to identify those unique moments in consumers' lives when their shopping habits become particularly flexible and the right advertisement or coupon would cause them to begin spending in new ways.

In the 1980s, a team of researchers led by a U.C.L.A. professor named Alan Andreasen undertook a study of peoples' most mundane purchases, like soap, toothpaste, trash bags and toilet paper. They learned that most shoppers paid almost no attention to how they bought these products, that the purchases occurred habitually, without any complex decision-making. Which meant it was hard for marketers, despite their displays and coupons and product promotions, to persuade shoppers to change.

But when some customers were going through a major life event, like graduating from college or getting a new job or moving to a new town, their shopping habits became flexible in ways that were both predictable and potential gold mines for retailers. The study found that when someone marries, he or she is more likely to start buying a new type of coffee. When a couple move into a new house, they're more apt to purchase a different kind of cereal. When they divorce, there's an increased chance they'll start buying different brands of beer.

Consumers going through major life events often don't notice, or care, that their shopping habits have shifted, but retailers notice, and they care quite a bit. At those unique moments, Andreasen wrote, customers are "vulnerable to intervention by marketers." In other words, a precisely timed advertisement, sent to a recent divorcee or new homebuyer, can change someone's shopping patterns for years.

And among life events, none are more important than the arrival of a baby. At that moment, new parents' habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions.

The only problem is that identifying pregnant customers is harder than it sounds. Target has a baby-shower registry, and Pole started there, observing how shopping habits changed as a woman approached her due date, which women on the registry had willingly disclosed. He ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole's colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.

As Pole's computers crawled through the data, he was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a "pregnancy prediction" score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.

One Target employee I spoke to provided a hypothetical example. Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There's, say, an 87 percent chance that she's pregnant and that her delivery date is sometime in late August. What's more, because of the data attached to her Guest ID number, Target knows how to trigger Jenny's habits. They know that if she receives a coupon via e-mail, it will most likely cue her to buy online. They know that if she receives an ad in the mail on Friday, she frequently uses it on a weekend trip to the store. And they know that if they reward her with a printed receipt that entitles her to a free cup of Starbucks coffee, she'll use it when she comes back again.

In the past, that knowledge had limited value. After all, Jenny purchased only cleaning supplies at Target, and there were only so many psychological buttons the company could push. But now that she is pregnant, everything is up for grabs. In addition to triggering Jenny's habits to buy more cleaning products, they can also start including offers for an array of products, some more obvious than others, that a woman at her stage of pregnancy might need.

Pole applied his program to every regular female shopper in Target's national database and soon had a list of tens of thousands of women who were most likely pregnant. If they could entice those women or their husbands to visit Target and buy baby-related products, the company's cue-routine-reward calculators could kick in and start pushing them to buy groceries, bathing suits, toys and clothing, as well. When Pole shared his list with the marketers, he said, they were ecstatic. Soon, Pole was getting invited to meetings above his paygrade. Eventually his paygrade went up.

At which point someone asked an important question: How are women going to react when they figure out how much Target knows?

"If we send someone a catalog and say, 'Congratulations on your first child!' and they've never told us they're pregnant, that's going to make some people uncomfortable," Pole told me. "We are very conservative about compliance with all privacy laws. But even if you're following the law, you can do things where people get queasy."

About a year after Pole created his pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

Video

Back

transcript

How to Break the Cookie Habit

Charles Duhigg explains the science of habits.

N/A

Charles Duhigg explains the science of habits.

"My daughter got this in the mail!" he said. "She's still in high school, and you're sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?"

The manager didn't have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man's daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. "I had a talk with my daughter," he said. "It turns out there's been some activities in my house I haven't been completely aware of. She's due in August. I owe you an apology."

When I approached Target to discuss Pole's work, its representatives declined to speak with me. "Our mission is to make Target the preferred shopping destination for our guests by delivering outstanding value, continuous innovation and exceptional guest experience," the company wrote in a statement. "We've developed a number of research tools that allow us to gain insights into trends and preferences within different demographic segments of our guest population." When I sent Target a complete summary of my reporting, the reply was more terse: "Almost all of your statements contain inaccurate information and publishing them would be misleading to the public. We do not intend to address each statement point by point." The company declined to identify what was inaccurate. They did add, however, that Target "is in compliance with all federal and state laws, including those related to protected health information."

When I offered to fly to Target's headquarters to discuss its concerns, a spokeswoman e-mailed that no one would meet me. When I flew out anyway, I was told I was on a list of prohibited visitors. "I've been instructed not to give you access and to ask you to leave," said a very nice security guard named Alex.

Using data to predict a woman's pregnancy, Target realized soon after Pole perfected his model, could be a public-relations disaster. So the question became: how could they get their advertisements into expectant mothers' hands without making it appear they were spying on them? How do you take advantage of someone's habits without letting them know you're studying their lives?

Before I met Andrew Pole, before I even decided to write a book about the science of habit formation, I had another goal: I wanted to lose weight.

I had got into a bad habit of going to the cafeteria every afternoon and eating a chocolate-chip cookie, which contributed to my gaining a few pounds. Eight, to be precise. I put a Post-it note on my computer reading "NO MORE COOKIES." But every afternoon, I managed to ignore that note, wander to the cafeteria, buy a cookie and eat it while chatting with colleagues. Tomorrow, I always promised myself, I'll muster the willpower to resist.

Tomorrow, I ate another cookie.

When I started interviewing experts in habit formation, I concluded each interview by asking what I should do. The first step, they said, was to figure out my habit loop. The routine was simple: every afternoon, I walked to the cafeteria, bought a cookie and ate it while chatting with friends.

Next came some less obvious questions: What was the cue? Hunger? Boredom? Low blood sugar? And what was the reward? The taste of the cookie itself? The temporary distraction from my work? The chance to socialize with colleagues?

Rewards are powerful because they satisfy cravings, but we're often not conscious of the urges driving our habits in the first place. So one day, when I felt a cookie impulse, I went outside and took a walk instead. The next day, I went to the cafeteria and bought a coffee. The next, I bought an apple and ate it while chatting with friends. You get the idea. I wanted to test different theories regarding what reward I was really craving. Was it hunger? (In which case the apple should have worked.) Was it the desire for a quick burst of energy? (If so, the coffee should suffice.) Or, as turned out to be the answer, was it that after several hours spent focused on work, I wanted to socialize, to make sure I was up to speed on office gossip, and the cookie was just a convenient excuse? When I walked to a colleague's desk and chatted for a few minutes, it turned out, my cookie urge was gone.

All that was left was identifying the cue.

Deciphering cues is hard, however. Our lives often contain too much information to figure out what is triggering a particular behavior. Do you eat breakfast at a certain time because you're hungry? Or because the morning news is on? Or because your kids have started eating? Experiments have shown that most cues fit into one of five categories: location, time, emotional state, other people or the immediately preceding action. So to figure out the cue for my cookie habit, I wrote down five things the moment the urge hit:

Where are you? (Sitting at my desk.)

What time is it? (3:36 p.m.)

What's your emotional state? (Bored.)

Who else is around? (No one.)

What action preceded the urge? (Answered an e-mail.)

The next day I did the same thing. And the next. Pretty soon, the cue was clear: I always felt an urge to snack around 3:30.

Once I figured out all the parts of the loop, it seemed fairly easy to change my habit. But the psychologists and neuroscientists warned me that, for my new behavior to stick, I needed to abide by the same principle that guided Procter & Gamble in selling Febreze: To shift the routine - to socialize, rather than eat a cookie - I needed to piggyback on an existing habit. So now, every day around 3:30, I stand up, look around the newsroom for someone to talk to, spend 10 minutes gossiping, then go back to my desk. The cue and reward have stayed the same. Only the routine has shifted. It doesn't feel like a decision, any more than the M.I.T. rats made a decision to run through the maze. It's now a habit. I've lost 21 pounds since then (12 of them from changing my cookie ritual).

After Andrew Pole built his pregnancy-prediction model, after he identified thousands of female shoppers who were most likely pregnant, after someone pointed out that some of those women might be a little upset if they received an advertisement making it obvious Target was studying their reproductive status, everyone decided to slow things down.

The marketing department conducted a few tests by choosing a small, random sample of women from Pole's list and mailing them combinations of advertisements to see how they reacted.

"We have the capacity to send every customer an ad booklet, specifically designed for them, that says, 'Here's everything you bought last week and a coupon for it,' " one Target executive told me. "We do that for grocery products all the time." But for pregnant women, Target's goal was selling them baby items they didn't even know they needed yet.

"With the pregnancy products, though, we learned that some women react badly," the executive said. "Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We'd put an ad for a lawn mower next to diapers. We'd put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.

"And we found out that as long as a pregnant woman thinks she hasn't been spied on, she'll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don't spook her, it works."

In other words, if Target piggybacked on existing habits - the same cues and rewards they already knew got customers to buy cleaning supplies or socks - then they could insert a new routine: buying baby products, as well. There's a cue ("Oh, a coupon for something I need!") a routine ("Buy! Buy! Buy!") and a reward ("I can take that off my list"). And once the shopper is inside the store, Target will hit her with cues and rewards to entice her to purchase everything she normally buys somewhere else. As long as Target camouflaged how much it knew, as long as the habit felt familiar, the new behavior took hold.

Soon after the new ad campaign began, Target's Mom and Baby sales exploded. The company doesn't break out figures for specific divisions, but between 2002 - when Pole was hired - and 2010, Target's revenues grew from $44 billion to $67 billion. In 2005, the company's president, Gregg Steinhafel, boasted to a room of investors about the company's "heightened focus on items and categories that appeal to specific guest segments such as mom and baby."

Pole was promoted. He has been invited to speak at conferences. "I never expected this would become such a big deal," he told me the last time we spoke.

A few weeks before this article went to press, I flew to Minneapolis to try and speak to Andrew Pole one last time. I hadn't talked to him in more than a year. Back when we were still friendly, I mentioned that my wife was seven months pregnant. We shop at Target, I told him, and had given the company our address so we could start receiving coupons in the mail. As my wife's pregnancy progressed, I noticed a subtle upswing in the number of advertisements for diapers and baby clothes arriving at our house.

Pole didn't answer my e-mails or phone calls when I visited Minneapolis. I drove to his large home in a nice suburb, but no one answered the door. On my way back to the hotel, I stopped at a Target to pick up some deodorant, then also bought some T-shirts and a fancy hair gel. On a whim, I threw in some pacifiers, to see how the computers would react. Besides, our baby is now 9 months old. You can't have too many pacifiers.

When I paid, I didn't receive any sudden deals on diapers or formula, to my slight disappointment. It made sense, though: I was shopping in a city I never previously visited, at 9:45 p.m. on a weeknight, buying a random assortment of items. I was using a corporate credit card, and besides the pacifiers, hadn't purchased any of the things that a parent needs. It was clear to Target's computers that I was on a business trip. Pole's prediction calculator took one look at me, ran the numbers and decided to bide its time. Back home, the offers would eventually come. As Pole told me the last time we spoke: "Just wait. We'll be sending you coupons for things you want before you even know you want them."

# data[].attributes.attachment_link.description
Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: "If we wanted to figure out if a customer is pregnant, even if she didn't want us to know, can you do that?

# data[].attributes.headline
Perpetrating the fog - Analytics in Learning and Education

# data[].attributes.subject
Perpetrating the fog - Analytics in Learning and Education

# data[].attributes.body
Não é suficiente considerar o big data e a análise como úteis apenas para avaliar o que os alunos fizeram e para prever o que farão no futuro. A análise na educação deve ser transformadora.

# data[].attributes.attachment_link.readable_text
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.

Although a majority of Learning Analytics literature has started to adopt the aforementioned definition, the definition and aims of Learning Analytics are still contested.

George Siemens is a writer, theorist, speaker, and researcher on learning, networks, technology, analytics and visualization, openness, and organizational effectiveness in digital environments. He is the originator of Connectivism theory and author of the article Connectivism: A Learning Theory for the Digital Age and the book Knowing Knowledge - an exploration of the impact of the changed context and characteristics of knowledge.[6][7] He is the founding President of the Society for Learning Analytics Research (SoLAR).

One earlier definition discussed by the community suggested that Learning Analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning. But this definition has been criticised by George Siemens[non-primary source needed ] and Mike Sharkey.[non-primary source needed ]

Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. It uses a general morphological analysis (GMA) to divide the domain into six "critical dimensions".

The broader term " Analytics" has been defined as the science of examining data to draw conclusions and, when used in decision-making, to present paths or courses of action. From this perspective, Learning Analytics has been defined as a particular case of Analytics, in which decision-making aims to improve learning and education. During the 2010s, this definition of analytics has gone further to incorporate elements of operations research such as decision trees and strategy maps to establish predictive models and to determine probabilities for certain courses of action.

Another approach for defining Learning Analytics is based on the concept of Analytics interpreted as the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data. From this point of view, Learning Analytics emerges as a type of Analytics (as a process), in which the data, the problem definition and the insights are learning-related.

In 2016, a research jointly conducted by the New Media Consortium (NMC) and the EDUCAUSE Learning Initiative (ELI) -an EDUCAUSE Program- describes six areas of emerging technology that will have had significant impact on higher education and creative expression by the end of 2020. As a result of this research, Learning analytics was defined as an educational application of web analytics aimed at learner profiling, a process of gathering and analyzing details of individual student interactions in online learning activities.

Dragan Gašević is a pioneer and leading researcher in learning analytics. He is a founder and past President (2015-2017) of the Society for Learning Analytics Research (SoLAR).

In 2017, Gašević, Коvanović, and Joksimović proposed a consolidated model of learning analytics. The model posits that learning analytics is defined at the intersection of three disciplines: data science, theory, and design. Data science offers computational methods and techniques for data collection, pre-processing, analysis, and presentation. Theory is typically drawn from the literature in the learning sciences, education, psychology, sociology, and philosophy. The design dimension of the model includes: learning design, interaction design, and study design. In 2015, Gašević, Dawson, and Siemens argued that computational aspects of learning analytics need to be linked with the existing educational research in order for Learning Analytics to deliver its promise to understand and optimize learning.

Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that "...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior". They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks questions whether this distinction exists in the literature. Brooks instead proposes that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems (e.g. learning content management systems).

Regardless of the differences between the LA and EDM communities, the two areas have significant overlap both in the objectives of investigators as well as in the methods and techniques that are used in the investigation. In the MS program offering in learning analytics at Teachers College, Columbia University, students are taught both EDM and LA methods.

Learning Analytics, as a field, has multiple disciplinary roots. While the fields of artificial intelligence (AI), statistical analysis, machine learning, and business intelligence offer an additional narrative, the main historical roots of analytics are the ones directly related to human interaction and the education system. More in particular, the history of Learning Analytics is tightly linked to the development of four Social Sciences' fields that have converged throughout time. These fields pursued, and still do, four goals:

1. Definition of Learner, in order to cover the need of defining and understanding a learner.

2. Knowledge trace, addressing how to trace or map the knowledge that occurs during the learning process.

3. Learning efficiency and personalization, which refers to how to make learning more efficient and personal by means of technology.

4. Learner - content comparison, in order to improve learning by comparing the learner's level of knowledge with the actual content that needs to master.(Siemens, George (2013-03-17). Intro to Learning Analytics. LAK13 open online course for University of Texas at Austin & Edx. 11 minutes in.)

A diversity of disciplines and research activities have influenced in these 4 aspects throughout the last decades, contributing to the gradual development of learning analytics. Some of most determinant disciplines are Social Network Analysis, User Modelling, Cognitive modelling, Data Mining and E-Learning. The history of Learning Analytics can be understood by the rise and development of these fields.

Social network analysis ( SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them.[citation needed ] Social network analysis is prominent in Sociology, and its development has had a key role in the emergence of Learning Analytics. One of the first examples or attempts to provide a deeper understanding of interactions is by Austrian-American Sociologist Paul Lazarsfeld. In 1944, Lazarsfeld made the statement of "who talks to whom about what and to what effect". That statement forms what today is still the area of interest or the target within social network analysis, which tries to understand how people are connected and what insights can be derived as a result of their interactions, a core idea of Learning Analytics.

Citation analysis

American linguist Eugene Garfield was an early pioneer in analytics in science. In 1955, Garfield led the first attempt to analyse the structure of science regarding how developments in science can be better understood by tracking the associations ( citations) between articles (how they reference one another, the importance of the resources that they include, citation frequency, etc). Through tracking citations, scientists can observe how research is disseminated and validated. This was the basic idea of what eventually became a " page rank", which in the early days of Google (beginning of the 21st century) was one of the key ways of understanding the structure of a field by looking at page connections and the importance of those connections. The algorithm PageRank -the first search algorithm used by Google- was based on this principle. American computer scientist Larry Page, Google's co-founder, defined PageRank as " an approximation of the importance" of a particular resource. Educationally, citation or link analysis is important for mapping knowledge domains.

The essential idea behind these attempts is the realization that, as data increases, individuals, researchers or business analysts need to understand how to track the underlying patterns behind the data and how to gain insight from them. And this is also a core idea in Learning Analytics.

Digitalization of Social network analysis

During the early 1970s, pushed by the rapid evolution in technology, Social network analysis transitioned into analysis of networks in digital settings.

1. Milgram's 6 degrees experiment. In 1967, American social psychologist Stanley Milgram and other researchers examined the average path length for social networks of people in the United States, suggesting that human society is a small-world-type network characterized by short path-lengths.

2. . American Sociologist Mark Granovetter's work on the strength of what is known as weak ties; his 1973 article "The Strength of Weak Ties" is one of the most influential and most cited articles in Social Sciences.

3. Networked individualism. Towards the end of the 20th century, Sociologist Barry Wellman's research extensively contributed the theory of social network analysis. In particular, Wellman observed and described the rise of "networked individualism" - the transformation from group-based networks to individualized networks.

During the first decade of the century, Professor Caroline Haythornthwaite explored the impact of media type on the development of social ties, observing that human interactions can be analyzed to gain novel insight not from strong interactions (i.e. people that are strongly related to the subject) but, rather, from weak ties. This provides Learning Analytics with a central idea: apparently un-related data may hide crucial information. As an example of this phenomenon, an individual looking for a job will have a better chance of finding new information through weak connections rather than strong ones. (Siemens, George (2013-03-17). Intro to Learning Analytics. LAK13 open online course for University of Texas at Austin & Edx. 11 minutes in.)

Her research also focused on the way that different types of media can impact the formation of networks. Her work highly contributed to the development of social network analysis as a field. Important ideas were inherited by Learning Analytics, such that a range of metrics and approaches can define the importance of a particular node, the value of information exchange, the way that clusters are connected to one another, structural gaps that might exist within those networks, etc.

The application of social network analysis in digital learning settings has been pioneered by Professor Shane P. Dawson. He has developed a number of software tools, such as Social Networks Adapting Pedagogical Practice (SNAPP) for evaluating the networks that form in [learning management systems] when students engage in forum discussions.

The main goal of user modelling is the customization and adaptation of systems to the user's specific needs, especially in their interaction with computing systems. The importance of computers being able to respond individually to into people was starting to be understood in the decade of 1970s. Dr Elaine Rich in 1979 predicted that "computers are going to treat their users as individuals with distinct personalities, goals, and so forth". This is a central idea not only educationally but also in general web use activity, in which personalization is an important goal.

User modelling has become important in research in human-computer interactions as it helps researchers to design better systems by understanding how users interact with software. Recognizing unique traits, goals, and motivations of individuals remains an important activity in learning analytics.

Personalization and adaptation of learning content is an important present and future direction of learning sciences, and its history within education has contributed to the development of learning analytics. Hypermedia is a nonlinear medium of information that includes graphics, audio, video, plain text and hyperlinks. The term was first used in a 1965 article written by American Sociologist Ted Nelson. Adaptive hypermedia builds on user modelling by increasing personalization of content and interaction. In particular, adaptive hypermedia systems build a model of the goals, preferences and knowledge of each user, in order to adapt to the needs of that user. From the end of the 20th century onwards, the field grew rapidly, mainly due to that the internet boosted research into adaptivity and, secondly, the accumulation and consolidation of research experience in the field. In turn, Learning Analytics has been influenced by this strong development.

Education/cognitive modelling

Education/cognitive modelling has been applied to tracing how learners develop knowledge. Since the end of the 1980s and early 1990s, computers have been used in education as learning tools for decades. In 1989, Hugh Burns argued for the adoption and development of intelligent tutor systems that ultimately would pass three levels of "intelligence": domain knowledge, learner knowledge evaluation, and pedagogical intervention. During the 21st century, these three levels have remained relevant for researchers and educators.

In the decade of 1990s, the academic activity around cognitive models focused on attempting to develop systems that possess a computational model capable of solving the problems that are given to students in the ways students are expected to solve the problems. Cognitive modelling has contributed to the rise in popularity of intelligent or cognitive tutors. Once cognitive processes can be modelled, software (tutors) can be developed to support learners in the learning process. The research base on this field became, eventually, significantly relevant for learning analytics during the 21st century.

While big data analytics has been more and more widely applied in education, Wise and Shaffer addressed the importance of theory-based approach in the analysis. Epistemic Frame Theory conceptualized the "ways of thinking, acting, and being in the world" in a collaborative learning environment. Specifically, the framework is based on the context of Community of Practice (CoP), which is a group of learners, with common goals, standards and prior knowledge and skills, to solve a complex problem. Due to the essence of CoP, it is important to study the connections between elements (learners, knowledge, concepts, skills and so on). To identify the connections, the co-occurrences of elements in learners' data are identified and analyzed.

Shaffer and Ruis pointed out the concept of closing the interpretive loop, by emphasizing the transparency and validation of model, interpretation and the original data. The loop can be closed by a good theoretical sound analytics approaches, Epistemic Network Analysis.

In a discussion of the history of analytics, Adam Cooper highlights a number of communities from which learning analytics has drawn techniques, mainly during the first decades of the 21st century, including:

1. Statistics, which are a well established means to address hypothesis testing.

2. Business intelligence, which has similarities with learning analytics, although it has historically been targeted at making the production of reports more efficient through enabling data access and summarising performance indicators.

3. Web analytics, tools such as Google Analytics report on web page visits and references to websites, brands and other key terms across the internet. The more "fine grain" of these techniques can be adopted in learning analytics for the exploration of student trajectories through learning resources (courses, materials, etc.).

4. Operational research, which aims at highlighting design optimisation for maximising objectives through the use of mathematical models and statistical methods. Such techniques are implicated in learning analytics which seek to create models of real world behaviour for practical application.

5. Artificial intelligence methods (combined with machine learning techniques built on data mining) are capable of detecting patterns in data. In learning analytics such techniques can be used for intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as "suggested course" systems modelled on collaborative filtering techniques.

6. Information visualization, which is an important step in many analytics for sensemaking around the data provided, and is used across most techniques (including those above).

The first graduate program focused specifically on learning analytics was created by Ryan S. Baker and launched in the Fall 2015 semester at Teachers College, Columbia University. The program description states that

"(...)data about learning and learners are being generated today on an unprecedented scale. The fields of learning analytics (LA) and educational data mining (EDM) have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators. As one of world's leading teaching and research institutions in education, psychology, and health, we are proud to offer an innovative graduate curriculum dedicated to improving education through technology and data analysis."

Masters programs are now offered at several other universities as well, including the University of Texas at Arlington, the University of Wisconsin, and the University of Pennsylvania.

Methods for learning analytics include:

Content analysis, particularly of resources which students create (such as essays).

Discourse analytics, which aims to capture meaningful data on student interactions which (unlike social network analytics) aims to explore the properties of the language used, as opposed to just the network of interactions, or forum-post counts, etc.

Social learning analytics, which is aimed at exploring the role of social interaction in learning, the importance of learning networks, discourse used to sensemake, etc.

Disposition analytics, which seeks to capture data regarding student's dispositions to their own learning, and the relationship of these to their learning. For example, "curious" learners may be more inclined to ask questions, and this data can be captured and analysed for learning analytics.

Epistemic Network Analysis, which is an analytics technique that models the co-occurrence of different concepts and elements in the learning process. For example, the online discourse data can be segmented as turn of talk. By coding students' different behaviors of collaborative learning, we could apply ENA to identify and quantify the co-occurrence of different behaviors for any individual in the group.

Learning Applications can be and has been applied in a noticeable number of contexts.

Analytics have been used for:

Prediction purposes, for example to identify "at risk" students in terms of drop out or course failure.

Personalization & adaptation, to provide students with tailored learning pathways, or assessment materials.

Intervention purposes, providing educators with information to intervene to support students.

Information visualization, typically in the form of so-called learning dashboards which provide overview learning data through data visualisation tools.

There is a broad awareness of analytics across educational institutions for various stakeholders, but that the way learning analytics is defined and implemented may vary, including:

1. for individual learners to reflect on their achievements and patterns of behaviour in relation to others. Particularly, the following areas can be set out for measuring, monitoring, analyzing and changing to optimize student performance:

1. Monitoring individual student performance

2. Disaggregating student performance by selected characteristics such as major, year of study, ethnicity, etc.

3. Identifying outliers for early intervention

4. Predicting potential so that all students achieve optimally

5. Preventing attrition from a course or program

6. Identifying and developing effective instructional techniques

7. Analyzing standard assessment techniques and instruments (i.e. departmental and licensing exams)

8. Testing and evaluation of curricula.

2. as predictors of students requiring extra support and attention;

3. to help teachers and support staff plan supporting interventions with individuals and groups;

4. for functional groups such as course teams seeking to improve current courses or develop new curriculum offerings; and

5. for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.

Some motivations and implementations of analytics may come into conflict with others, for example highlighting potential conflict between analytics for individual learners and organisational stakeholders.

Much of the software that is currently used for learning analytics duplicates functionality of web analytics software, but applies it to learner interactions with content. Social network analysis tools are commonly used to map social connections and discussions. Some examples of learning analytics software tools include:

BEESTAR INSIGHT: a real-time system that automatically collects student engagement and attendance, and provides analytics tools and dashboards for students, teachers and management[non-primary source needed ]

LOCO-Analyst: a context-aware learning tool for analytics of learning processes taking place in a web-based learning environment

SAM: a Student Activity Monitor intended for personal learning environments[non-primary source needed ]

SNAPP: a learning analytics tool that visualizes the network of interactions resulting from discussion forum posts and replies[non-primary source needed ]

Solutionpath StREAM: A leading UK based real-time system that leverage predictive models to determine all facets of student engagement using structured and unstructured sources for all institutional roles[non-primary source needed ]

Student Success System: a predictive learning analytics tool that predicts student performance and plots learners into risk quadrants based upon engagement and performance predictions, and provides indicators to develop understanding as to why a learner is not on track through visualizations such as the network of interactions resulting from social engagement (e.g. discussion posts and replies), performance on assessments, engagement with content, and other indicators[non-primary source needed ]

Epistemic Network Analysis (ENA) web tool: An interactive online tool that allow researchers to upload the coded dataset and create the model by specifying units, conversations and codes. Useful functions within the online tool includes mean rotation for comparison between two groups, specifying the sliding window size for connection accumulation, weighed or unweighted models, and parametric and non-parametric statistical testings with suggested write-up and so on. The web tool is stable and open source.

The ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for learning analytics, with concerns raised regarding:

Communications around the scope and role of learning analytics

The necessary role of human feedback and error-correction in learning analytics systems

Data sharing between systems, organisations, and stakeholders

Trust in data clients

As Kay, Kom and Oppenheim point out, the range of data is wide, potentially derived from:

Recorded activity: student records, attendance, assignments, researcher information (CRIS)

Systems interactions: VLE, library / repository search, card transactions

Feedback mechanisms: surveys, customer care

External systems that offer reliable identification such as sector and shared services and social networks

Thus the legal and ethical situation is challenging and different from country to country, raising implications for:

Variety of data: principles for collection, retention and exploitation

Education mission: underlying issues of learning management, including social and performance engineering

Motivation for development of analytics: mutuality, a combination of corporate, individual and general good

Customer expectation: effective business practice, social data expectations, cultural considerations of a global customer base.

Obligation to act: duty of care arising from knowledge and the consequent challenges of student and employee performance management

In some prominent cases like the inBloom disaster, even full functional systems have been shut down due to lack of trust in the data collection by governments, stakeholders and civil rights groups. Since then, the learning analytics community has extensively studied legal conditions in a series of experts workshops on "Ethics & Privacy 4 Learning Analytics" that constitute the use of trusted learning analytics.[non-primary source needed ] Drachsler & Greller released an 8-point checklist named DELICATE that is based on the intensive studies in this area to demystify the ethics and privacy discussions around learning analytics.

1. D-etermination: Decide on the purpose of learning analytics for your institution.

2. E-xplain: Define the scope of data collection and usage.

3. L-egitimate: Explain how you operate within the legal frameworks, refer to the essential legislation.

4. I-nvolve: Talk to stakeholders and give assurances about the data distribution and use.

5. C-onsent: Seek consent through clear consent questions.

6. A-nonymise: De-identify individuals as much as possible

7. T-echnical aspects: Monitor who has access to data, especially in areas with high staff turn-over.

8. E-xternal partners: Make sure externals provide highest data security standards

It shows ways to design and provide privacy conform learning analytics that can benefit all stakeholders. The full DELICATE checklist is publicly available.

Privacy management practices of students have shown discrepancies between one's privacy beliefs and one's privacy related actions. Learning analytic systems can have default settings that allow data collection of students if they do not choose to opt-out. Some online education systems such as edX or Coursera do not offer a choice to opt-out of data collection. In order for certain learning analytics to function properly, these systems utilize cookies to collect data.

In 2012, a systematic overview on learning analytics and its key concepts was provided by Professor Mohamed Chatti and colleagues through a reference model based on four dimensions, namely:

Chatti, Muslim and Schroeder note that the aim of open learning analytics (OLA) is to improve learning effectiveness in lifelong learning environments. The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model.

For general audience introductions, see:

The Educause learning initiative briefing (2011)

The Educause review on learning analytics (2011)

The UNESCO learning analytics policy brief (2012)

The NMC Horizon Report: 2016 Higher Education Edition

# data[].attributes.attachment_link.description
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis.

# data[].attributes.body
Um recurso importante do ds106 é a produção e o uso de avaliação renovável. Até a presente data, milhares de tarefas renováveis ​​com foco em histórias digitais foram enviadas para o Banco de Avaliações do ds106 que, em 2018, incluiu 1.157 tarefas ds106 e 15.052 exemplos criados a partir deles.

# data[].attributes.attachment_link.title
ds106 Assignments:community made creative assignments for ds106

# data[].attributes.attachment_link.description
This site contains assignments for and created by participants in the most kick butt online open course anywhere, ds106, Digital Storytelling ( more...). As of Aug 4, 2019 this collection includes 1172 ds106 assignments and 15939 examples created from them. Check out our Featured Assignments Do you feel lucky?

# data[].attributes.headline
Blog - Exemplos de atividades não descartáveis em um curso de Introdução à Filosofia! 

# data[].attributes.subject
Blog - Exemplos de atividades não descartáveis em um curso de Introdução à Filosofia! 

# data[].attributes.body
Ideias bem legais que servem para vários contextos.

# data[].attributes.attachment_link.readable_text
Disposable assignments

In the past couple of years I've really been grabbed by the issue of "disposable assignments," as discussed by David Wiley here:

These are assignments that students complain about doing and faculty complain about grading. They're assignments that add no value to the world - after a student spends three hours creating it, a teacher spends 30 minutes grading it, and then the student throws it away. Not only do these assignments add no value to the world, they actually suck value out of the world.

A non-disposable assignment, then, is one that adds value to the world beyond simply being something the students have to do to get a grade. A similar idea is expressed by Derek Bruff in a post on the idea of "students as producers" -treating students as producers of knowledge, rather than only as consumers: Bruff talks about students creating work for "authentic audiences," beyond just the teacher.

Wiley gives an example of a non-disposable assignment: students taking instructional materials in the course (which are openly licensed) and revising/remixing them to create tutorials for future students in the course. Other examples can be found in this growing list of examples of open pedagogy. One that I often hear about is asking students to edit or create Wikipedia articles. Or students could post their work more locally, but still have it be publicly visible, such as what Judy Chan does with her students' research projects at UBC (click on "team projects" in the various years). Simon Bates has his physics students create learning objects to help their peers (see this story for more).

Students as producers in philosophy courses

I have already started to ask students to do some activities that could add value to the world, whether to their fellow students and/or beyond.

In a second-year moral theory course I asked them to sign up for 1-2 days on which to do "reading notes" on the class wiki page: they had to outline one of the main arguments in the text assigned for that day and write down questions for their small group to discuss. You can see those here (organized by group number).

In a first-year, introduction to philosophy course I have asked students to:

blog about what they think philosophy is, both at the beginning and end of the course-this, I thought, could provide some interesting information to others about what our students think "philosophy" is. I don't have those blog posts visible anymore because I didn't ask students if I could keep them posted after the course was finished (d'oh!!).

write a blog post describing how they see philosophical activity going on in the world around them, beyond the class-I thought this could be useful to show the range of what can count as philosophical activity. I do still have those posts up (but not for long, because again I forgot to ask for permission to keep the posts up after the course is finished...I will do that this term!): http://blogs.ubc.ca/phil102 (click on "philosophy in the world")

But now that I'm working on my Intro to Philosophy course for Fall 2015 ( see planning doc here), I'm trying to think through some other options for assignments with authentic audiences and that add value to the world. Here are some ideas (not that I'm going to implement all of these; I'm just brainstorming).

Editing Wikipedia articles on philosophy

This is a big task; it requires that students learn how to do so (not just technologically, but in terms of the rules and practices of Wikipedia), plus determining which articles need editing, etc.

I would prefer to start with students creating Wikipedia-style articles on philosophers or texts on the UBC Wiki first. Then other students (in future classes) could edit those, and then maybe eventually we could move to doing something on Wikipedia itself (the content would be good, and maybe students would be motivated to move some of it over to Wikipedia at that point).

Creating tutorials or other "learning objects" for their fellow students and for the public

As noted above, Simon Bates does this in his Physics 101 course, and I can pretty easily see how one might ask students to do so for basic physics concepts. But why not do so for some basic philosophy concepts too?
e.g., find something you find difficult in the course, and once you feel you have a handle on it, create something to help other students

could be done in groups (probably best, with a large class like Intro to Phil (150 students))

could be text based, but better if also incorporates some other kinds of visual or auditory elements (e.g., a video, or incorporating images, or slides or something)

Creating study questions or suggestions of "what to focus on" for the readings

students often get lost in reading primary philosophical texts, and I haven't yet managed to write up study questions or suggestions for what to focus on for each reading. This would definitely be useful to other students.

But wouldn't it be cruel to ask students to do this for later students when I haven't done it for them myself? and do I have time to do this before the Fall term this year? Unfortunately not.

Creating lists of "common problems" or advice for writing, after doing peer review of each others' work and self-reflecting on their own
I do provide quite a lot of writing advice to students, but I wonder if advice coming from students' direct experience in my courses might be helpful to later students?

Creating possible exam questions

I ask students to do this informally, in groups, as part of the review for the final exam. But why not formalize this somehow so their suggestions are posted publicly? The course page on the UBC Wiki seems like a good place, at least to start. Then students could see them from year to year.

A number of instructors at UBC use PeerWise as a tool for students to ask and answer questions. It seems like an interesting thing, but:

It's not public; but it could be used to generate questions and then the best ones could be made public somewhere

It's limited to multiple choice questions, which I hardly ever use (and never on exams)

Those are my ideas for now. Have any others? Or comments on any of this? Please comment, below!

# data[].attributes.attachment_link.title
disposable assignments - You're the Teacher

# data[].attributes.attachment_link.description
In the past couple of years I've really been grabbed by the issue of "disposable assignments," as discussed by David Wiley here: These are assignments that students complain about doing and faculty complain about grading.

# data[].attributes.body
Eu nunca havia ouvido a expressão "avaliações renováveis" (renewable assessment) até junho/2020.
A crítica é que boa parte das avaliações, após serem corrigidas, vão para o lixo, pois não tem utilidade alguma para a escola ou para o aluno. Sugere-se então a elaboração de atividades com algum valor ou utilidade para o mundo para além do objetivo de aprendizagem estabelecido na aula.

# data[].attributes.attachment_link.readable_text
For some time now I've been critical of "disposable assessments." An assessment can be characterized as "disposable" if everyone understands that its ultimate destiny is the garbage can. Take an all-too-typical example:

Faculty member assigns student to write a two page compare and contrast essay

Student writes the paper and submits it to faculty

Faculty grades the paper and returns it to student

Student checks what grade they received, briefly peruses any written comments, and then throws the paper away

(This example assumes physical paper, but the principles are exactly the same in the context of assessments submitted, graded, and returned electronically.)

A "renewable assessment" differs in that the student's work won't be discarded at the end of the process, but will instead add value to the world in some way. Take, for example, the Murder, Madness, and Mayhem assessments from 2008:

The University of British Columbia's class SPAN312 ("Murder, Madness, and Mayhem: Latin American Literature in Translation") contributed to Wikipedia during Spring 2008. Our collective goals were to bring a selection of articles on Latin American literature to featured article status (or as near as possible). By project's end, we had contributed three featured articles and eight good articles. None of these articles was a good article at the outset; two did not even exist.

Rather than writing essays to submit to their instructor and then throw away, these students contributed good quality research and writing to Wikipedia, where others will be able to benefit from their work for years to come. That's the core idea between renewable assessments like Murder, Madness, and Mayhem, or Project Management for Instructional Designers, or Blogs vs Wikis, or the DS106 Assignment Bank, or The Open Anthology of Earlier American Literature, and many of the other examples listed by the community here.

In many ways, I think the most powerful part of renewable assignments is the idea that everyone wants their work to matter. No one wants to struggle for hours or days on something they know will be thrown away almost as soon as it is finished. Given the opportunity, people want to contribute something, to give something back, to pay it forward, to make the world a better place, to make a difference. Few right thinking person will invest their heart and soul in work that is academic in the way that non-faculty use the term - "not of practical relevance; of only theoretical interest. The debate has been largely academic."

It's no wonder people hate homework so much. They don't hate learning - they hate wasting time and energy and effort. Try to imagine dedicating large swaths of your day to work you knew would never be seen, would never matter, and would literally end up in the garbage can. Maybe you don't have to imagine - maybe some part of your work day is actually like that. If so, you may know the despair of looking forward and seeing only piles of work that don't matter. And that's how students frequently feel. Your results may vary, but I estimate that the 20 million postsecondary students in the US spend over 150M hours per year on disposable assessments. Every year. Year after year. When time is being used so poorly at such scale, I can't believe it doesn't negatively impact society.

Replacing disposable assessments with renewable assessments goes a long way toward re-humanizing education, giving students a reason to care about and truly invest in their work. Without this broader motivating context, students are just waxing cars, sanding decks, and painting fences.

"You promise learn. I say, you do. No question. That your part."

Research on Renewable Assessments

A change of this magnitude - and really, any change in assessment strategy - deserves to be well understood. So how do we conceptualize research about renewable assessments (and perhaps other forms of open pedagogy)? What kinds of questions are appropriate and useful to ask in this context?

My colleagues in the Open Education Group and I like to say that when you're considering the outcomes of research on OER adoption, there are "two ways to win." First, think about three possible outcomes of OER adoption in terms of change in cost and change in learning:

Students save money and learn less

Students save money and learn the same amount

Students save money and learn more

When OER is adopted in place of commercial resources, students save a substantial amount of money. But what happens to learning? Two of the three possible outcomes are "wins" for OER - the same amount of learning for less money is a win, and more learning for less money is a win. Hence our "two ways to win" mantra.

Are there parallels to this set of questions in the assessment context? I believe so. Instead of cost and learning, I think we should begin by examining the value students recognize in their work and the amount of learning these new assessments support. I realize this requires some additional explanation.

[We now interrupt this essay with a brief, unscheduled rant. The overwhelming majority of assessments used by faculty to assign grades to students and, in a very real sense, determine some of their future life prospects, are created by faculty with no training in psychometrics. These assessments are never evaluated in terms of the reliability and validity of their results. (The test item banks and other assessments that commercial publishers provide with textbooks are also almost never subjected to this level of rigor in their design.) To say that the current state of play among faculty is a widespread ignoring of issues of reliability and validity in assessment is to give faculty too much credit. I would wager that over 90% of faculty don't know these are technical terms in the assessment context, that over 99% of faculty couldn't properly define the terms in this context, and that over 99.99% of faculty couldn't describe a reasonable process for establishing the reliability and validity of an assessment's results. So the first person who objects to the idea of renewable assessments on the grounds that they "might not be as good" as the assessments they've traditionally used has some serious explaining to do.]

In the early days of OER adoption, we found that there are ways of adopting OER that actually cost more than using commercial materials. (See Wiley, Hilton, Ellington, and Hall ( 2012) for an example of how a poorly planned print-on-demand strategy can make OER more expensive than publisher textbooks.) In similar fashion, I think it's reasonable to anticipate that in the early days of renewable assessment design we'll see assessments that students find no more motivating than their disposable counterparts. Just as we spent time in the early years of OER adoption research specifically investigating the whether-or-nots and hows of cost savings, we'll need to spend time in the early years of renewable assessment design specifically investigating the value students find in doing this work, how motivating or engaging they find it, etc. Just writing that sentence I can see there's still some construct clarification to do here.

As we work to establish common patters for designing renewable assessments that students find significantly more valuable to do than their disposable counterparts (just as we found OER adoption patterns that consistently save students money), we can also ask questions about the assessments are functioning. At a minimum we can begin by asking questions about outcome alignment. For example, should a rubric for grading a renewable assessment differ from the rubric used to grade the disposable assessment it is replacing? If so, how? We'll have to guard carefully against "construct irrelevance creep" in rubrics for renewable assessments. For example, it might be tempting to award points for a renewable assessment published on YouTube based on how many views or likes it gets. Unless the course context is marketing with social media, this is likely completely irrelevant to the learning outcomes we ought to be assessing in introductory sociology or biology. If I replace a two page compare and contrast essay with a renewable assessment, should it not assess the same (or very highly overlapping) set of learning outcomes? Establishing some degree of comparability in what is assessed and the rigor with which it is assessed will be key to persuading faculty to abandon disposable assessments for renewable assessment strategies.

If you take the (inexplicably radical) position that assessments can be a productive part of learning and not just an autopsy of the learning process, we might also hypothesize better learning outcomes for students whose faculty use renewable assessments strategies. (Establishing the comparability described in the previous paragraph will also be helpful here.) Given a disposable assessment and a renewable assessment that both assess the same learning outcomes, might we hypothesize that students who find the renewable assessment work valuable, and consequently invest more time and effort in it, will display higher levels of mastery on the outcomes we care about? While only a hypothesis, it appears reasonable on its surface. And I have a few years of anecdotal evidence that give me confidence that it's a hypothesis worth testing.

Looking for a topic for that dissertation or for your next journal article? You might think about attacking questions like:

Do students assigned renewable assessments find them more valuable, interesting, motivating, or rewarding than traditional assessments? Why or why not?

Do students assigned renewable assessments demonstrate greater mastery of learning outcomes than students assigned traditional assessments? Why or why not?

And what do you suppose will be the result of research into these and similar questions? Again, going back to the OEG mantra, there are two ways to win:

Assessments that students find significantly more rewarding to do that result in lower levels of mastery,

Assessments that students find significantly more rewarding to do that result in the same level of mastery, and

Assessments that students find significantly more rewarding to do that result in higher levels of mastery.

Renewable Assessments and Open

Open licenses allow faculty and students to revise and remix materials (both content and assessments) in a broad range of ways. As you look through the examples of renewable assessments above, you will see that many of them involve revising and remixing - demonstrating that renewable assessments are enabled by the 5R permissions granted by open licenses. (It's true that a student could do a renewable assessment completely "from scratch," but that doesn't appear to be the way they've worked to date.) In other words, "open" makes possible renewable assessments that would otherwise be illegal. This is why I think renewable assessments are the best examples of open pedagogy we have now. You might argue that a student could use a range of copyrighted materials in a homework assignment and claim it was a Fair Use. However, I suspect many people would hesitate to share this kind of material broadly given the ambiguities of Fair Use, which kind of undermines the "give something back" philosophy underlying renewable assessments. And without providing the permissions for others to revise, remix, build on, and improve the work, it's difficult to really call it "renewable."

Students are the authors and, thanks to Berne, the copyright holders of the homework and other artifacts they create as part of their education. There is no morally or ethically appropriate scenario in which faculty require students to openly license their homework or other creations as part of an assignment. However, faculty can espouse the benefits of openness and advocate for students to license their works under a Creative Commons license. This advocacy will be significantly more effective (and less hypocritical) if the faculty member is using OER in the class and can point to OER they have created and shared.

If some portion of the over 150M hours higher ed students currently spend on disposable assessments can be spent on renewable assessments instead, and if some portion of those students choose to openly license their work, questions about the sustainability and maintainability of the OER ecosystem can be answered. Over time, we could see a transition to a place where the majority of content and assessments a learner encounters were created "by students, for students" with editorial support from faculty (what we used to call "grading"). What an incredible, inspiring, sustainable world that would be...

Let's create and share more renewable assessments as OER - open renewable assessments - that others can adopt, improve, and share broadly. And let's get this research going.

# data[].attributes.attachment_link.description
For some time now I've been critical of "disposable assessments." An assessment can be characterized as "disposable" if everyone understands that its ultimate destiny is the garbage can.

# data[].attributes.headline
Blog - Projeto anual de Ed-Tech da jornalista Audrey Watters

# data[].attributes.subject
Blog - Projeto anual de Ed-Tech da jornalista Audrey Watters

# data[].attributes.body
Vale a pena dar uma olhada! Nos comentários coloquei os links para as paginas dos anos anteriores.

# data[].attributes.attachment_link.provider_name
The Stories We Were Told about Education Technology (2019)

# data[].attributes.attachment_link.readable_text
2019 marks the tenth year in which I've reviewed the most important "trends" in education technology from the previous twelve months. (You can look at the topics I've covered in previous years here on research.hackeducation.com.)

I barely made it to ten. (Sorry. I am writing a goddamn book.)
Image credits: Bryan Mathers

# data[].attributes.attachment_link.title
The Stories We Were Told about Education Technology (2019)

# data[].attributes.attachment_link.description
2019 marks the tenth year in which I've reviewed the most important "trends" in education technology from the previous twelve months. (You can look at the topics I've covered in previous years here on research.hackeducation.com.) I barely made it to ten. (Sorry. I am writing a goddamn book.)

# data[].attributes.body
Vale a pena assistir a esta palestra de 2012 de Sebastian Thrun, professor de Stanford e co-fundador da Udacity.

# data[].attributes.headline
MIT Escritório de Inovação Educacional e Tecnologia

# data[].attributes.subject
MIT Escritório de Inovação Educacional e Tecnologia

# data[].attributes.attachment_link.readable_text
The Office of Educational Innovation and Technology works with faculty, staff and students to enable and promote the development and dissemination of innovative uses of technology in teaching and learning. This is accomplished through an innovation cycle which involves:

Investigating new and emerging technologies, and exploring opportunities for their adoption to support education at MIT.

Establishing links with MIT departments to monitor emerging innovations and IT needs.

Assisting faculty in locating, building and integrating technologies that support a wide range of pedagogical models.

Supporting MIT initiatives through the development, adoption & implementation of sustainable & educationally valuable software tools.

Implementing test-beds for promising educational technologies and new services, to advance teaching and learning.

Developing plans for the incubation, early implementation, and the transitioning of delivery systems to long-term core service providers.

Engaging with interested MIT departments, labs, and centers to integrate technology into the curriculum.

Working closely with the Teaching and Learning Lab and the Office of Faculty Support to enable and support Educational Innovation at MIT.

# data[].attributes.attachment_link.description
The Office of Educational Innovation and Technology works with faculty, staff and students to enable and promote the development and dissemination of innovative uses of technology in teaching and learning. This is accomplished through an innovation cycle which involves: Investigating new and emerging technologies, and exploring opportunities for their adoption to support education at MIT.

# data[].attributes.headline
Revista - Os cursos online significam o fim da universidade tradicional?

# data[].attributes.subject
Revista - Os cursos online significam o fim da universidade tradicional?

# data[].attributes.body
Edição, música, compras, jornalismo - tudo revolucionado pela internet. Próximo na fila? Educação. Agora, os acadêmicos dos EUA estão oferecendo aulas de nível internacional - gratuitamente - para qualquer pessoa que possa fazer logon, em qualquer lugar do mundo. Seria o fim da vida no campus?

# data[].attributes.attachment_link.readable_text
Two years ago, I sat in the back seat of a Toyota Prius in a rooftop car park in California and gripped the door handle as the car roared away from the kerb, headed straight towards the roof's edge and then at the last second sped around a corner without slowing down. There was no one in the driver's seat.

It was the prototype of Google's self-driving car and it felt a bit like being Buck Rogers and catapulted into another century. Later, I listened to Sebastian Thrun, a German-born professor of artificial intelligence at Stanford University, explain how he'd built it, how it had already clocked up 200,000 miles driving around California, and how one day he believed it would mean that there would be no traffic accidents.

A few months later, the New York Times revealed that Thrun was the head of Google's top-secret experimental laboratory Google X, and was developing, among other things, Google Glasses - augmented reality spectacles. And then, a few months after that, I came across Thrun again.

The self-driving car, the glasses, Google X, his prestigious university position - they'd all gone. He'd resigned his tenure from Stanford, and was working just a day a week at Google. He had a new project. Though he didn't call it a project. "It's my mission now," he said. "This is the future. I'm absolutely convinced of it."

The future that Thrun believes in, that has excited him more than self-driving cars, or sci-fi-style gadgets, is education. Specifically, massive online education free to all. The music industry, publishing, transportation, retail - they've all experienced the great technological disruption. Now, says Thrun, it's education's turn.

"It's going to change. There is no doubt about it." Specifically, Thrun believes, higher education is going to change. He has launched Udacity, an online university, and wants to provide mass high quality education for the world. For students in developing countries who can't get it any other way, or for students in the first world, who can but may choose not to. Pay thousands of pounds a year for your education? Or get it free online?

University, of course, is about so much more than the teaching. There's the socialising, of course, or, as we call it here in Britain, drinking. There's the living away from home and learning how to boil water stuff. And there's the all-important sex and catching a social disease stuff. But this is the way disruptions tend to work: they disrupt first, and figure out everything else at some unspecified time later.

Thrun's great revelation came just over a year ago at the same TED conference where he unveiled the self-driving car. "I heard Salman Khan talk about the Khan Academy and I was just blown away by it," he says. "And I still am." Salman Khan, a softly spoken 36-year-old former hedge fund analyst, is the founding father of what's being called the classroom revolution, and is feted by everyone from Bill Gates (who called him "the world's favourite teacher") down.

The Khan Academy, which he set up almost accidentally while tutoring his niece and nephew, now has 3,400 short videos or tutorials, most of which Khan made himself, and 10 million students. "I was blown away by it," says Thrun. "And frankly embarrassed that I was teaching 200 students. And he was teaching millions."

Thrun decided to open up his Stanford artificial intelligence class, CS221, to the world. Anybody could join, he announced. They'd do the same coursework as the Stanford students and at the end of it take the same exam.

CS221 is a demanding, difficult subject. On campus, 200 students enrolled, and Thrun thought they might pull in a few thousand on the web. By the time the course began, 160,000 had signed up. "It absolutely blew my mind," says Thrun. There were students from every single country in the world - bar North Korea. What's more, 23,000 students graduated. And all of the 400 who got top marks were students who'd done it online.

It was, says Thrun, his "wonderland" moment. Having taught a class of 160,000 students, he couldn't go back to being satisfied with 200. "I feel like there's a red pill and a blue pill," Thrun said in a speech a few months later. "I've taken the red pill, and I've seen wonderland. We can really change the world with education."

By the time I sign up to Udacity's beginners' course in computer science, how to build a search engine, 200,000 students have already graduated from it. Although when I say "graduate" I mean they were emailed a certificate. It has more than a touch of Gillian McKeith's PhD about it, though it seems employers are taking it seriously: a bunch of companies, including Google, are sponsoring Udacity courses and regularly cream off the top-scoring students and offer them jobs.

I may have to wait a while for that call, though I'm amazed at how easy Udacity videos are to follow (having tips and advice on search-engine building from Sergey Brin, Google's co-founder, doesn't hurt). Like the Khan Academy, it avoids full-length shots of the lecturer and just shows a doodling hand.

According to Brin, if you have basic programming ability - which we'll all have if we complete the course - and a bit of creativity, "you could come up with an idea that might just change the world". But then that's Silicon Valley for you.

What's intriguing is how this will translate into a British context. Because, of course, when it comes to revolutionising educational access, Britain has led the world. We've had the luxury of open access higher education for so long - more than 40 years now - that we're blasé about it. When the Open University was launched in 1969, it was both radical and democratic. It came about because of improvements in technology - television - and it's been at the forefront of educational innovation ever since. It has free content - on OpenLearn and iTunesU. But at its heart, it's no longer radically democratic. From this year, fees are £5,000.

In America, Thrun is not the only one to have taken the pills. A year on from the Stanford experiment, and the world of higher education and the future of universities is completely different. Thrun's wasn't the only class to go online last autumn. Two of his computer science colleagues, Andrew Ng and Daphne Koller, also took part, with equally mind-blowing results. They too have set up a website, Coursera. And while Udacity is developing its own courses, Coursera is forming partnerships with universities to offer existing ones. When I met Koller in July, shortly after the website's launch, four universities had signed up - Stanford, Princeton, Michigan and Pennsylvania.

Just four months later, it has 33 partner universities, 1.8 million students and is having venture capital thrown at it - $16m (£10m) in the first round. And it doesn't stop there. It's pretty remarkable that Coursera and Udacity were spun out of the same university, but also the same department (Thrun and Koller still supervise a PhD student together). And they have the dynamic entrepreneurial change-the-world quality that characterise the greatest and most successful Silicon Valley startups.

"We had a million users faster than Facebook, faster than Instagram," says Koller. "This is a wholesale change in the educational ecosystem."

But they're not alone. Over at Massachusetts Institute of Technology, Anant Argarwal, another professor of computer science, who also cites Khan as his inspiration (and who was, in a neat twist, once his student), has launched edX, featuring content from MIT, Harvard, Berkeley and the University of Texas System.

Argarwal is not a man prone to understatement. This, he says, is the revolution. "It's going to reinvent education. It's going to transform universities. It's going to democratise education on a global scale. It's the biggest innovation to happen in education for 200 years." The last major one, he says, was "probably the invention of the pencil". In a decade, he's hoping to reach a billion students across the globe. "We've got 400,000 in four months with no marketing, so I don't think it's unrealistic."

More than 155,000 students took the first course he taught, including a whole class of children in Mongolia. "That was amazing!" says Argarwal. "And we discovered a protégé. One of his students, Batthushig, got a perfect score. He's a high school student. I can't overstate how hard this course was. If I took it today, I wouldn't get a perfect score. We're encouraging him to apply to MIT." This is the year, Argarwal says, that everything has changed. There's no going back. "This is the year of disruption."

A month ago, I signed up for one of the Coursera courses: an introduction to genetics and evolution, taught by Mohamed Noor, a professor at Duke University. Unlike Udacity's, Coursera's courses have a start date and run to a timetable. I quite fancied a University of Pennsylvania course on modern poetry but it had already started. This one was 10 weeks long, would feature "multiple mini-videos roughly 10-15 minutes in length", each of which would contain a number of quizzes, and there would also be three tests and a final exam.

It's just me, Noor, and my 36,000 classmates. We're from everywhere: Kazakhstan, Manila, Donetsk, Iraq. Even Middlesbrough. And while I watch the first videos and enjoy Noor's smiley enthusiasm, I'm not blown away.

They're just videos of lectures, really. There's coursework to do, but I am a journalist. I am impervious to a deadline until the cold sweat of impending catastrophe is upon me. I ignore it. And it's a week or so later when I go back and check out the class forum.

And that's when I have my being-blown-away moment. The traffic is astonishing. There are thousands of people asking - and answering - questions about dominant mutations and recombination. And study groups had spontaneously grown up: a Colombian one, a Brazilian one, a Russian one. There's one on Skype, and some even in real life too. And they're so diligent! If you are a vaguely disillusioned teacher, or know one, send them to Coursera: these are people who just want to learn.

Four weeks in, Noor announces that he's organising a Google hangout: it's where a limited number of people can talk via their webcams. But it's scheduled for 1am GMT on Sunday morning. I go to sleep instead. However I do watch the YouTube video of it the next day and it's fascinating viewing. Despite the time, Richard Herring, a train driver from Sheffield, is there, bright and alert and wanting to tell Noor how much he's enjoying the course.

"Richard!" says Noor. "Nice to meet you! Your posts are amazing. I often find that before I have a chance to go in and answer a question, somebody else has already answered it, and it's often Richard. Thank you."

"I just love science," says Richard. "I was never any good at school, but I've just picked it up along the way. It's a brilliant course. To get something like this without paying anything is marvellous. I'm loving it."

So is Sara Groborz, a graphic designer who was born in Poland but now lives in Britain. And then there's Naresh Ramesh, from Chennai, who's studying for a degree in biotechnology, and Maria, who lives in the US and is using the course to teach her students in a juvenile correction institute. Aline, a high school student in El Salvador, comes on. She took the course, she says, because she goes to a Catholic school where they don't teach evolution. "And you're the best teacher I've ever had!" she tells Noor.

How gratifying must it be to be a teacher on one of these courses? When I catch up by email with Noor the next day, he writes. "I'm absolutely LOVING it!" By phone, he says it's one of the most exciting things he's ever done.

What's more, it means that next semester he's going to be able to "flip the classroom". This is a concept that Khan has popularised and shown to be successful: students do the coursework at home by watching the videos, and then the homework in class, where they can discuss the problems with the instructor.

There are still so many issues to figure out with online education. Not least the fact that you don't get a degree out of it, although a university in the US has just announced that it will issue credit for it. At the moment, most people are doing courses for the sake of simply learning new stuff. "And a certificate, basically a pdf, which says this person may or may not be who they say they are," says Noor.

And while computers are excellent at grading maths questions, they're really much less hot at marking English literature essays. There's a preponderance of scientific and technical subjects, but the number of humanties courses is increasing with what Koller says is "surprisingly successful" peer assessment techniques. "It can't replace a one-to-one feedback from an expert in the field, but with the right guidance, peer assessment and crowd-sourcing really does work."

And in terms of content, the course I'm doing is pretty much the same as the one Noor's students take. At Duke, they have more interaction, and a hands-on lab environment, but they are also charged $40,000 a year for the privilege.

It's a lot of money. And it's this, that makes Udacity's and Coursera's and edX's courses so potentially groundbreaking. At the moment, they're all free. And while none of them can compete with traditional degrees, almost every other industry knows what happens when you give teenagers the choice between paying a lot of money for something or getting it for nothing.

Of course, education isn't quite an industry, but it is a business, or as Matt Grist, an education analyst from the thinktank Demos tells me, "a market", although he immediately apologises for saying this. "I know. It's terrible. That's the way we talk about it these days. I don't really like it, but I do it. But it is a market. And universities are high-powered businesses with massive turnovers. Some of the best institutions in Britain are global players these days."

Grist has been looking at the funding model of British universities, and sees trouble ahead. The massive rise in fees this year is just the start of it. "We've set off down this road now, and if you create competition and a market for universities, I think you're going to have to go further." He foresees the best universities becoming vastly more expensive, and the cheaper, more vocational ones "holding up". "It's the middle-tier, 1960s campus ones that I think are going to struggle."

When I ask Koller why education has suddenly become the new tech miracle baby, she describes it as "the perfect storm. It's like hurricane Sandy, all these things have come together at the same time. There's an enormous global need for high quality education. And yet it's becoming increasingly unaffordable. And at the same time, we have technological advances that make it possible to provide it at very low marginal cost."

And, in Britain, the storm is perhaps even more perfect. This is all happening at precisely the moment that students are having to pay up to £9,000 a year in fees and being forced to take on unprecedented levels of debt.

Students, whether they like it or not, have been turned into consumers. Education in Britain has, until now, been a very pure abstraction, a concept untainted by ideas of the market or value. But that, inevitably, is now changing. University applications by UK-born students this year were down almost 8%. "Though the number who turned up was much lower than that," Peter Lampl, the founder of the Sutton Trust, tells me. "They were 15% down."

The trust champions social mobility and nothing accelerates that more than university. "That's why we're so keen on it," says Lampl. "We're monitoring the situation. We don't know what the true impact of the fees will be yet. Or what the impact of coming out of university with £50,000 worth of debt will have on the rest of your life. "Will it delay you buying a house? Or starting a family? People compare it to the States, but in America one third of graduates have no debt, and two-thirds have an average of $25,000. This is on a completely different scale."

And it's amid this uncertainty and this market pressure that these massive open online courses - or Moocs as they're known in the jargon - may well come to play a role. There are so many intangible benefits to going to university. "I learned as much if not more from my fellow students than I did from the lectures," says Lampl. But they're the things - making life-long friends, joining a society, learning how to operate a washing machine - that are free. It's the education bit that's the expensive part. But what Udacity and the rest are showing is that it doesn't necessarily have to be."

The first British university to join the fray is Edinburgh. It's done a deal with Coursera and from January, will offer six courses, for which 100,000 students have already signed up. Or, to put this in context, four times as many undergraduates as are currently at the university.

It's an experiment, says Jeff Hayward, the vice-principal, a way of trying out new types of teaching "I'll be happy if we break even." At the moment Coursera doesn't charge students to receive a certificate of completion, but at some point it's likely to, and when it does, Edinburgh will get a cut.

But then Edinburgh already has an online model. More than 2,000 students studying for a masters at the university aren't anywhere near it; they're online. "And within a few years, we're ramping that up to 10,000," says Hayward.

For undergraduates, on the other hand, study is not really the point of university, or at least not the whole point. I know a student at Edinburgh called Hannah. "Do you have any lectures tomorrow?" I text her. "Only philosophy at 9am," she texts back. "So obviously I'm not going to that."

She's an example of someone who would be quite happy to pay half the fees, and do some of the lectures online. "God yes. Some of the lecturers are so crap, anyway. We had a tutorial group the other day, and he just sat there and read the paper and told us to get on with it."

Max Crema, the vice-president of the student union, tells me that he's already used online lectures from MIT to supplement his course. "Though that may be because I'm a nerd," he concedes. "The problem with lectures is that they are about 300 years out of date. They date back to the time when universities only had one book. That's why you still have academic positions called readers."

I trot off to one of them, an actual lecture in an actual lecture theatre, the old anatomy theatre, a steeply raked auditorium that's been in use since the 19th century when a dissecting table used to hold centre stage, whereas today there's just Mayank Dutia, professor of systems neurophysiology, talking about the inner ear.

He's one of the first academics signed up to co-deliver one of the Coursera courses come January, although he defends the real-life version too: "Universities are special places. You can't do what we do online. There's something very special in being taught by a world leader in the field. Or having a conversation with someone who's worked on a subject their whole lives. There's no substitute for this."

There isn't. But what the new websites are doing is raising questions about what a university is and what it's for. And how to pay for it. "Higher education is changing," says Hayward. "How do we fund mass global education? There are agonies all over the world about this question."

There are. And there's no doubting that this is something of a turning point. But it may have an impact closer to home too. Argarwal sees a future in which universities may offer "blended" models: a mixture of real-life and online teaching.

Coursera has already struck its first licensing deal. Antioch College, a small liberal arts institution in Ohio, has signed an agreement under which it will take content from Duke University and the University of Pennsylvania. And a startup called the Minerva Project is attempting to set up an online Ivy League university, and is going to encourage its students to live together in "dorm clusters" so that they'll benefit from the social aspects of university life. Seeing how the students on Coursera and Udacity organise themselves, it's not impossible to see how in the future, students could cluster together and take their courses online together. For free.

There's so much at stake. Not least the economies of dozens of smallish British cities, the "second-tier" universities that Matt Grist of Demos foresees could struggle in the brave new free education market world.

At Edinburgh, fees are having an effect - applications are down - but "most students seem to see it as mañana money," says Jeff Hayward. "It's still hypothetical at the moment."

But this is the first year of £9,000 fees. An English student at Edinburgh (it's free for Scottish students), where courses are four years, is looking at £36,000 of debt just for tuition. And maybe another £30,000 of living expenses on top of that.

These websites are barely months old. They're still figuring out the basics. Universities aren't going anywhere just yet. But who knows what they'll look like in 10 years' time? A decade ago, I thought newspapers would be here for ever. That nothing could replace a book. And that KITT, David Hasselhoff's self-driving car in Knight Rider was nothing more than a work of fantasy.
Topics

# data[].attributes.attachment_link.title
Do online courses spell the end for the traditional university?

# data[].attributes.attachment_link.description
Two years ago, I sat in the back seat of a Toyota Prius in a rooftop car park in California and gripped the door handle as the car roared away from the kerb, headed straight towards the roof's edge and then at the last second sped around a corner without slowing down.

# data[].attributes.headline
Blog - A inovação disruptiva vai "consertar" a educação?

# data[].attributes.subject
Blog - A inovação disruptiva vai "consertar" a educação?

# data[].attributes.body
Nos últimos anos, a inovação na educação a distância tem sido frequentemente enquadrada em uma narrativa de "educação  falida" que se tornou difundida, especialmente nos EUA. Farrow (2015) observa que 'as análises que enfatizam um estado de crise na educação são frequentemente acompanhadas pela ideia de algum tipo de salvação através da tecnologia' na forma de inovação disruptiva.

# data[].attributes.attachment_link.readable_text
This is what all education is like - FACT.

I've complained about the " education is broken" meme before - my reservations about it have been threefold:

i) It's just lazy - saying something is broken ( or dead) avoids having to do any subtle analysis and appeals to a simplistic viewpoint.

ii) It frames technological change as a crisis and not an opportunity - Mike Caulfield covered this better than me, but once you adopt a 'broken' metaphor then a whole set of language accompanies it which frames it as a negative problem to be fixed.

iii) It's suspicious - those who peddle the "education is broken" line usually have something to gain from its acceptance. Either they are directly selling a solution that will mend it (after all, a broken something needs to be fixed, see point ii) or they have individual prestige in being seen as someone who can at least see the means of fixing it.

I've been studying the Masters in History at the OU recently (it's an 18 month long haul, only 2 months in). So I thought I'd reflect on how 'broken' this experience feels. The answer is, well, not at all really. This isn't an advert for the OU, I'm sure the majority of reliable HE institutions would provide a similar experience. The point is that it isn't a radical course in terms of approach or technology, and so provides a reasonable experience of what education is really like I think. I'm sure there are plenty of worse examples, and grudgingly, maybe I'll concede there are some better ones, but this is what distance education in the 21st century roughly equates to, I'd hazard.

First of all we had to read quite a dense text ( Davidoff and Hall's Family Fortunes), we were provided with reviews around it and the first assignment was based around analysing its approach. The aim of this was, I think, to introduce us to the type of academic writing in history (I suspect it weeds out the people who really just want to do genealogy), and to think about the range of references and resources required to provide a comprehensive argument (Davidoff and Hall are exhaustive, and at times, exhausting in this respect).

The assignment was an old fashioned 2500 word essay. And it worked - I read the book in a much more analytical manner because of it, I followed up references, I considered the manner in which evidence is found and constructed to form a picture, I engaged with fellow students in the VLE (shhh).

We're now on to a more active block, looking at the range of online resources. This is enjoyable stuff, I really didn't know there was just soooo much stuff out there. I have been lost for a few evenings in the House of Commons Parliementary Papers, or the Proceedings of the Old Bailey, or the National Library of Wales, etc.

I'm not going to do a critique of the course, but studying it has reinforced my belief that "Education is broken" is an unhelpful stance to take. For a start it doesn't reflect the reality. I would urge anyone who uses this claim to study a course outside of their own domain of expertise and see how it feels (I'm obviously focusing on higher ed here, the claim is also frequently used for K12, which I can't claim expertise in). I think many protaganists are basing their claims on rather outdated views of what it feels like to be a student.

But more importantly, if one adopts the broken stance then you want to start afresh. In my experience this isn't beneficial. I can see how you could take the existing course and maybe add some new elements in to it, or campaign for open access to all historical archives for instance, explore new forms of assessment, find ways of making such courses more accessible and open (I didn't say MOOC, no sir), etc. These are opportunities to build on top of a successful approach, which seems a better, if less sexy approach, than demanding wholesale revolution with some glorious new leaders installed.

Reflecting on this has made me think of a fourth reason why I don't like the 'education is broken' rhetoric, and that is it is fundamentally cliquey or elitist. It's underlying message is "all these other people don't get technology like we do. That's why they're stuck in that old fashioned way of thinking which we know is hopelessly broken. Come join our new gang." But of course, if this new gang became the establishment they'd quickly abandon it. They don't really want educational change, they just want to feel cool for a while. Let them do that, we can do better.

# data[].attributes.attachment_link.description
This is what all education is like - FACT. I've complained about the "education is broken" meme before - my reservations about it have been threefold: i) It's just lazy - saying something is broken (or dead) avoids having to...

# data[].attributes.headline
Livro - Mito: a tecnologia é o aspecto mais importante da educação on-line

# data[].attributes.subject
Livro - Mito: a tecnologia é o aspecto mais importante da educação on-line

# data[].attributes.body
Muito do que se diz sobre a inovação no aprendizado on-line se concentra na tecnologia. Em um mundo de novas plataformas, ferramentas, aplicativos, hardwares e softwares brilhantes, pode ser difícil encontrar evidências que estejam atendendo às necessidades e acomodando as preferências de aprendizado de pessoas reais.

# data[].attributes.headline
Criar apresentações com quizzes e competições online

# data[].attributes.subject
Criar apresentações com quizzes e competições online

# data[].attributes.attachment_link.readable_text
Certain users navigate the internet using the keyboard rather than the mouse. Expert "power" users prefer keyboard commands for efficiency, while users with certain disabilities have no choice but to use the keyboard. For example, people with a motor impairment have difficulty with the fine motor movements required for using a mouse; blind users rely on assistive technology such as screen readers and can't see where to click the mouse.

The big difference between the keyboard and the mouse is that when users navigate through the keyboard, the access to the links on the screen is sequential: users must tab through all the links one by one before reaching a link of interest. In contrast, a mouse user can inspect the links available on the screen and move the cursor directly to the link he wants to click; thus, the mouse allows direct access to the links on the screen.

Even the most fancy or wonderful site is completely useless to someone who cannot access its controls and interact with it. Keyboard-friendly websites make these interactions possible for users who cannot use the mouse.

Test a Website's Keyboard Accessibility

For those of you reading this article on a desktop or laptop in Firefox, IE, Chrome, or Safari, please do this exercise.

1. Click in the browser address bar.

2. Take your hand off your mouse and use only your keyboard.

3. Using the Tab button, navigate until you've reached the link below. (You can use Shift+Tab to navigate back one step.)

Test Link, Doesn't Go Anywhere
You probably noticed 4 things:

1. You could follow along with where the keyboard focus was on the page.

2. You were able to select any link on the page.

3. Tabbing followed the same order as the page layout: left to right and top to bottom in columns.

4. It was a bit annoying to tab through the long navigation lists before getting to the test button. (But you could do it.)

This is a quick test to run on your website as well. When you do the test, follow the next 3 guidelines for making your website keyboard accessible.

Obvious Keyboard Focus

Imagine someone using their mouse to browse the internet without seeing the cursor on the screen. Impossible, right? This is what it's like if there's no visual indicator of where the keyboard focus currently is on the page.

Websites need to provide the same feedback for keyboard and mouse users. The keyboard focus is coded by default into all the common web browsers. Some browsers default to a thin grey dotted line, others to a blue solid border. (This difference doesn't matter: users will know the convention employed by their favorite browser.)

Removing the keyboard focus indicator is catastrophic for keyboard users. Commonly, the focus indicator is removed because it's thought to be an eye sore. If you don't like the indicator provided by the browser, don't remove it altogether; instead replace it with a design that is consistent with your site's look-and-feel. This CSS tutorial on how to add in a new focus style might be helpful.

All Interactive Elements Are Accessible

Keyboard users must be able to access all interactive elements, not just the main navigation or in-line links. This means that form elements, drop-down menus, buttons, dialog boxes, and other widgets all have to be accessible by tabbing through the interface.

The biggest problem is caused by JavaScript widgets with <div> or <span> elements. These can be assigned keyboard focus through tabindex. Alternatively, using only HTML links, buttons, and form fields will ensure that all elements can be tabbed to.

Drop-down menus can be difficult to use by keyboard if users can't scroll through the list separately from making a selection. This happens if the arrow-down button selects the first item from the list, instead of moving down through the list options. For example, in the dropdown below this would result in selecting Apples when hitting the arrow-down key once. Instead, the arrow-down key should let the user scroll between Apples, Bananas, and Oranges and use Enter to make their selection.

Consider a Skip Navigation Link

On most websites the tab order will be coded to reflect the page layout; that is top to bottom and left to right. This helps sighted keyboard users get around the page.

One of the pitfalls of tabbing along the page layout is that navigation comes first in the conventional webpage layout (which we strongly recommend retaining to leverage user expectations). On some pages that means that keyboard users might have to tab through 100+ navigation elements to get to the main content, which can be difficult (let alone time consuming).

Mouse users can simply disregard the main navigation and interact directly with any of the links in the main content area. Offering a Skip Navigation link provides some of that benefit to keyboard-only users.

Conclusion

People who are unable to use a mouse or see the screen need to access all website content and functionality through their keyboard alone. To make your website keyboard accessible it is important to allow users to: 1) follow along with where the keyboard focus is, 2) navigate to all interactive elements, and 3) bypass the navigation if there are many links.

# data[].attributes.attachment_link.description
Certain users navigate the internet using the keyboard rather than the mouse. Expert "power" users prefer keyboard commands for efficiency, while users with certain disabilities have no choice but to use the keyboard.