# data[].attributes.body
Learning analytics with Discovery Education

# data[].attributes.body
However, the Classroom API is a place to watch closely, or "play" with, as the capabilities for data and learning analytics develops in the near future:

# data[].attributes.attachment_link.description
Manage courses and rosters in Google Classroom with this REST API.

# data[].attributes.body
Also, for those in a GAfE district, recent blog posts have hinted to new upcoming data and learning analytics capabilities with the Google Classroom:

# data[].attributes.attachment_link.readable_text
Last year, we launched the Classroom API to make it easier for administrators to manage classes, and for developers to integrate their applications with Classroom. Since that time, hundreds of applications have integrated with Classroom to help teachers gamify their classes, improve students' writing skills, build interactive presentations and more.

Do more with coursework in the Classroom API
Today, we're introducing new coursework endpoints that allow developers to access assignments, grades and workflow. Learning tools can focus on creating great content and, in turn, use Classroom to manage the workflow for assignments created with this content. Gradebooks and reporting systems can now also sync grades with Classroom, eliminating the need for teachers to manually transfer grades.

Several partners have been helping to test the new functionality, including:

OpenEd, which provides a library of open education resources for K-12 teachers

Tynker, a creative computing platform for teaching students to code

GeoGebra, a visual mathematics platform that allows students and teachers to author interactive mathematics content

# data[].attributes.attachment_link.title
Build deeper integrations with Google Classroom

# data[].attributes.attachment_link.description
Last year, we launched the Classroom API to make it easier for administrators to manage classes, and for developers to integrate their applications with Classroom. Since that time, hundreds of applications have integrated with Classroom to help teachers gamify their classes, improve students' writing skills, build interactive presentations and more.

# data[].attributes.body
For those in a Google Apps for Education (GAfE) district, one aspect to keep an eye out for is the Google Analytics. A great place to begin is the free Analytics Academy:

# data[].attributes.attachment_link.description
Improve your Analytics skills with free online courses from Google.

# data[].attributes.body
1. Analytics provides insight into the learner's behaviors.

2. Allows educators to gain a better understanding of how to enhance and improve the learner experience.

3. Ultimately, the learner's experience is improved.

4. It can help identify at-risk learners, thus enabling timely interventions to increase student retention.

5. The data allows for budgeting more economically and purchasing more effectively.

6. Helps move towards personalized learning.

# data[].attributes.body
Learning analytics refers to the collection and measurement of data from learners, as well as the analysis and reporting of that data in order to understand and enhance student learning and the learning environment.

# data[].attributes.body
LeMo is another example of a learning analytics tool that can be used.

# data[].attributes.attachment_link.description
LeMo was developed during a research project at three universities of applied sciences in Berlin. The software, which is available as open source, supports teachers analysing and evaluating their courses. Interactive visualisations of online activities offer information on learners' activities, leading to a better understanding of learning situations.

# data[].attributes.body
Districts using Moodle have access to learning analytics, as demonstrated in this video:

# data[].attributes.body
KlassData is another example of learning analytics for education:

# data[].attributes.attachment_link.entities[].name
Will Learning Analytics Transform Higher Education

# data[].attributes.attachment_link.entities[].name
IBM Watson Analytics in Higher Education

# data[].attributes.attachment_link.title
KlassData: Learning Analytics for Education

# data[].attributes.attachment_link.description
Computers, tablets and a big array of digital tools are changing how the people learn, share and interact with the huge amount of available information. The learning process in virtual environments is more complex to analise, but the generated data unlocks the power of learning analytics and opens the door to personalized paths in education.

# data[].attributes.body
Districts with Compass Learning also have access to valuable learning analytics:

# data[].attributes.body
Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK

# data[].attributes.attachment_link.title
Journal of Interactive Media in Education

# data[].attributes.attachment_link.description
Article: Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK

# data[].attributes.body
Educational Data Mining and Learning Analytics

# data[].attributes.body
Q&A: How can I use analytics to benefit my students?

# data[].attributes.attachment_link.description
We negotiate with publishers, on behalf of the UK higher education sector, to procure and license affordable digital content

# data[].attributes.body
Learning Analytics: Benefits and Challenges in Higher Education

# data[].attributes.attachment_link.provider_name
The Council Community: A global alliance of leading schools of business and management

# data[].attributes.attachment_link.readable_text
By Community host Professor Dr. Dirk Ifenthaler, University of Mannheim
Learning analytics uses dynamic information about learners and learning environments, assessing, eliciting and analysing it for real-time modelling, prediction and optimisation of learning processes, learning environments and educational decision-making.

With the increased availability of vast administrative systems, academic and personal information within educational settings, and educational data management analysis and interpretation is becoming complex. Several concepts closely linked to processing such educational information are educational data mining, academic analytics and learning analytics. However, these concepts are often confused and lack universally agreed as well as applied definitions. Educational data mining (EDM) refers to the process of extracting useful information out of a large collection of complex educational datasets. Academic analytics (AA) is the identification of meaningful patterns in educational data in order to inform academic issues (e.g., retention, success rates) and produce actionable strategies (e.g., budgeting, human resources). Learning analytics (LA) emphasises insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems and social platforms. Such dynamic educational information is used for real-time interpretation, modelling, prediction and optimisation of learning processes, learning environments and educational decision-making.

Benefits of Learning Analytics

The benefits of learning analytics can be associated with four levels of stakeholders (see Figure 1): mega-level (governance), macro-level (institution), meso-level (curriculum, teacher/tutor), and micro-level (learner, OLE). An essential prerequisite for LA benefits, however, is the real-time access, analysis and modelling of relevant educational information.

The mega-level facilitates cross-institutional analytics by incorporating data from all levels of the learning analytics framework. Such rich datasets enable the identification and validation of patterns within and across institutions and therefore provide valuable insights for informing educational policymaking.

The macro-level enables institution-wide analytics for better understanding learner cohorts for optimising associated processes and allocating critical resources for reducing dropout and increasing retention as well as success rates.

The meso-level supports the curriculum and learning design as well as providing detailed insights about learning processes for course facilitators (i.e., teachers, tutors). This information can be used for improving the overall quality of courses (e.g., sequencing of learning processes, alignment with higher level outcomes) as well as enhancing learning materials (e.g., their alignment to anticipated learning outcomes and associated assessments).

The micro-level analytics supports the learner through recommendations and help functions implemented in the OLE. Learners benefit from such personalised and adaptive scaffolds and are expected to be more successful in reaching the learning outcomes.

Another critical component for improving the benefits of LA is information from the physical environment (e.g., learner's current emotional state) which is not directly linked with the educational data. Accordingly, data may be collected within the OLE through reactive prompts and linked with the available educational information.

More educational data does not always make better educational data. Hence, LA has its obvious limitations and data collected from various educational sources can have multiple meanings. Therefore, serious concerns and challenges are associated with the application of LA:

(1) Not all educational data is relevant and equivalent. Therefore, the validity of data and its analyses is critical for generating useful summative, real-time and predictive insights. This generates a new interdisciplinary research area for cognitive psychology, educational technology, learning design, psychometrics, data management, artificial intelligence, web development and statistics. The challenges are to investigate the complex processes within LA frameworks and to understand their immediate and long-term effects on learning and teaching processes.

(2) Ethical issues are associated with the use of educational data for LA. That implies how personal data is collected and stored as well as how it is analysed and presented to different stakeholders. Hence, procedures regulating access and usage of educational data need to come into operation before LA frameworks are implemented. This will also include transparency of applied algorithms and weighting of educational data for predictive modelling. Storing and processing anonymised personal data is only a small step towards a more comprehensive educational data governance structure for LA.

(3) Limited access to educational data generates disadvantages for involved stakeholders. For example, invalid forecasts may lead to inefficient decisions and unforseen problems. A misalignment of prior knowledge, learning pathways and learning outcomes could increase churn and the late identification of learners at risk may create dropouts. A definition of threshold standards for LA could prevent vast gaps between educational institutions and provide equal opportunities for all stakeholders.

(4) The preparation of stakeholders for applying insights from LA in a meaningful way is vital. Professional development for stakeholders ensures that issues are identified and benefits are transformed into meaningful action. Hence, the increased application of LA requires a new generation of experts with unique interdisciplinary competences. This will also require new infrastructures for administration and research in order to accelerate the understanding of LA.

(5) Information from distributed networks and unstructured data cannot be directly linked to educational data collected within an institution's environment. An aggregation of such data and uncontrolled relations to existing educational data increases the chance of critical biases as well as invalid analysis, predictions and decisions. The challenge is to develop mechanisms to filter biased information and warn stakeholders accordingly.

(6) An optimal sequence of data collection and economic response times (seconds, minutes, hours, days, weeks) of LA have yet to be determined. This includes the minimum requirements for making valid predictions and creating meaningful interventions. Missing data is a critical challenge for future LA algorithms.

(7) Besides the analysis of numerical data (e.g., click streams), a qualitative analysis of semantic rich data (e.g., content of discussion forums, responses to open-ended assessments) enables a better understanding of learners' knowledge and needs. An obvious requirement is the development of automated natural language processing (NLP) capabilities. The major challenge besides the development of real-time NLP is the validation of such algorithms and the link to quantitative educational data.

Future applications of LA presuppose a seamless and system-inherent analysis of learner's progression in order to continuously adapt the learning environment. LA provides the pedagogical and technological background for producing real-time interventions at all times during the learning process. It is expected that the availability of such personalised, dynamic and timely feedback supports the learner's self-regulated learning as well as increases their motivation and success. However, such automated systems may also hinder the development of competences such as critical thinking and autonomous learning.

It is expected that a holistic LA framework will provide evidence for increasing the overall quality of learning environments and facilitate the reform of learning and teaching in the 21st Century.

Bellin-Mularski, N., & Ifenthaler, D. (2014). Learning Analytics: Datenanalyse zur Untestützung von Lehren und Lernen in der Schule. SchulVerwaltung NRW, 25(11), 300-303.

Ifenthaler, D. (2014). Toward automated computer-based visualization and assessment of team-based performance. Journal of Educational Psychology, 106(3), 651-665. doi: 10.1037/a0035505

Ifenthaler, D., Adcock, A. B., Erlandson, B. E., Gosper, M., Greiff, S., & Pirnay-Dummer, P. (2014). Challenges for education in a connected world: Digital learning, data rich environments, and computer-based assessment - Introduction to the inaugural special issue of technology, knowledge and learning. Technology, Knowledge and Learning, 19(1-2), 121-126. doi: 10.1007/s10758-014-9228-2

Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1-2), 221-240. doi: 10.1007/s10758-014-9226-4

Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), Encyclopedia of educational technology (Vol. 2, pp. 447-451). Thousand Oaks, CA: Sage.

Ifenthaler, D., Bellin-Mularski, N., & Mah, D.-K. (2015). Internet: Its impact and its potential for learning and instruction. In J. M. Spector (Ed.), Encyclopedia of educational technology (Vol. 1, pp. 416-422). Thousand Oaks, CA: Sage.
Professor Dr. Dirk Ifenthaler, University of Mannheim The Council on Business & Society Global Alliance is an ongoing international dialogue between six of the world's leading business schools and an organiser of Forums focusing on issues at the crossroads of business and society - helps bring together business leaders, academics, students and journalists from around the world. #CouncilonBusinessandSociety
Visit the Council on Business & Society's website for an insight into its 2015 Forum:

# data[].attributes.attachment_link.title
Learning Analytics: Benefits and Challenges for Higher Education

# data[].attributes.attachment_link.description
By Community host Professor Dr. Dirk Ifenthaler, University of Mannheim Learning analytics uses dynamic information about learners and learning environments, assessing, eliciting and analysing it for real-time modelling, prediction and optimisation of learning processes, learning environments and educational decision-making. With the increased availability of vast administrative systems, academic and personal information within educational settings, and...

# data[].attributes.body
5 Reasons Why Learning Analytics Are Important For eLearning

# data[].attributes.attachment_link.readable_text
In this post I will highlight some of the most significant arguments for why Learning analytics have the power to improve eLearning in education and training. In addition, I will go over the basics of learning analytics, to offer an in depth look at how it can effectively enhance learners' overall eLearning experience.

How Learning Analytics Can Improve eLearning Design and Development

A major aspect (and a great challenge at the same time!) of instructional design and eLearning development is to know the behavior of your learners. This way, you can create an eLearning experience that offers the most benefit for the learners and ensure that every component of the eLearning course is helping them towards achieving their objectives and goals. One of the most effective ways to collect this invaluable data is through learning analytics. But why are learning analytics so important in eLearning, and how can they become a priceless resource for instructional designers?

The Basics of Learning Analytics

Learning Analytics are, in essence, the collection of data that is gathered while learners are engaging in the eLearning experience. Analytics also consist of the analysis and reporting of this information. Specifically concerning eLearning, vital pieces of data are recorded throughout the duration of the eLearning course such as learners' score on a particular test/exam, how quickly they are progressing through a module, how many times have they have logged in, whether they have participated in a discussion board, etc. At the same time, Learning Analytics also offer online facilitators and instructors a comprehensive look at how a learner is performing, if he/she may need additional help with a particular lesson or subject, and even if the learner is likely to pass or not an eLearning course. Such data can then be used to make educational analysis and predictions that help to determine which learning materials are appropriate, useful or irrelevant for the learners. This is primarily based upon learner's performance, skill level, and personal interests.

Why Learning Analytics Are So Important To Improve eLearning

Here are just a few of the most significant reasons for why learning analytics have the power to enhance eLearning experience and create more effective eLearning environments:

1. Helps to predict learners' performance
One of the most significant benefits of analytics is that they can provide insight into not only how a learner is performing today, but also about his/her future performance throughout the duration of the eLearning course. For example, online facilitators may foresee if a particular learner is likely not to pass the eLearning course, or if the learner is likely to pass the eLearning course if additional support is provided (such as further readings or tutoring sessions). Therefore, analytics can help to determine if learners may benefit from supplementary eLearning materials and/or peer/instructor aid throughout the eLearning course. This leads to higher grades and a more meaningful and complete eLearning experience.

2. Provides learners with a personalized eLearning experience
Through learning analytics, eLearning professionals and online instructors gain the ability to custom tailor eLearning experiences for each and every individual learner. If the data shows that a learner is taking a great deal of time to finish a particular eLearning module, then appropriate measures can be taken to offer learner more customized educational tools and eLearning course resources. For example, learners can be provided with links to sites that may help them to effectively comprehend the topic, or videos that allow them to learn through a more auditory/visual approach. No two learners are alike, and learning analytics gives eLearning professionals the power to ensure that no two eLearning experiences are alike either.

3. Increased learners' retention rates
Given that more learners have the opportunity to enhance their performance thanks to learning analytics data and intervention; fewer learners will drop out or fail the eLearning course. If a learner isn't faring well throughout the eLearning course, then he/she is less likely to be motivated to remain enrolled. As a result, a learner will simply stop participating, which means that institutions and/or organizations may see a steep decrease dropout rate and/or profits and learners simply won't benefit from the informative eLearning courses that are being provided.

4. Helps to improve future eLearning courses
Not only can learning analytics help current learners, but can also help future learners as well. For instance, if the data shows that a vast majority of learners are finding one particular aspect of the eLearning course too challenging, then the developers can change the difficulty level of that specific eLearning module. This will lead to more powerful and impactful eLearning environments tomorrow, thanks to the data that has been collected today.

5. If you can gain an in depth understanding of how the eLearning courses and their respective resources are being utilized, and how learners are actually acquiring information, and which aspects of the deliverable are successful (and which are falling short, for that matter), then you have the power to achieve higher quality eLearning at a lower cost. For example, if you determine, through analytics, that a particular section of the eLearning course simply isn't helping learners to achieve their learning goals, then you can devote your resources to either improving it or focus on another area that may be a more worthwhile investment.

Learning analytics, in many respects, have the ability to change the world of eLearning whether in educational institutions or in corporate training. With the data collected through analytics, instructional designers and eLearning professionals can offer learners the one key thing that all eLearning courses and training modules strive to offer: an unparalleled eLearning experience.

References:

# data[].attributes.attachment_link.title
5 Reasons Why Learning Analytics Are Important For eLearning - eLearning Industry

# data[].attributes.attachment_link.description
In this post I will highlight some of the most significant arguments for why Learning analytics have the power to improve eLearning in education and training. In addition, I will go over the basics of learning analytics, to offer an in depth look at how it can effectively enhance learners' overall eLearning experience.

# data[].attributes.body
TLT Symposium 2013

# data[].attributes.attachment_link.title
TLT Symposium 2013 Lunch Keynote (edited)

# data[].attributes.attachment_link.description
Interest in big data, data mining, and analytics is strong and growing in business and government. Recent reports by McKinsey, HBR, and Deloitte indicate that big data and analytics are just beginning beginning to make their impact in many sectors. The tools and methods of analytics are developing rapidly and are increasingly easier to use.

# data[].attributes.body
Penetrating the Fog: Analytics in Learning and Education

# data[].attributes.body
Analytics in Higher Education: Establishing a Common Language

# data[].attributes.headline
How to Personally Use Learning Analytics?

# data[].attributes.subject
How to Personally Use Learning Analytics?