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The following criteria are likely to be useful in evaluating the tool you select.

Name of the technology

Function of the technology and specific data analysis purpose

Compatibility with other teaching and learning technologies

Ease of use and technical ability required

Availability and organisational support

Value of the data to your specific needs

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Congratulations on making it to the end of the course!

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Congratulations on making it to the end of the course!

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Educational data, data literacy for teachers and the value of data analytics technologies;

Teaching analytics which refers to the methods and tools used to analyse educational design in order to improve learning conditions for targeted learners;

Learning analytics which refers tothe methods andtools that analyse the classroom delivery of a lesson plan, collect and process student data to identify individual students’ needs, and help the classroom teacher to better support individual students; and

Teacher inquiry as a method for data-driven reflection-on-action using teaching and learning analytics tools.

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How to use teaching and learning analytics to reflect on your teaching practice

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How to use teaching and learning analytics to reflect on your teaching practice

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Interested in asking:

Were there any elements of my lesson plan (educational resources or learning activities) that my students preferred or ignored? In particular, I would like to investigate this question regarding the educational resources and learning activities that students used in their home study.

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Teaching and learning analytics: A technology to support teacher inquiry

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Teaching and learning analytics: A technology to support teacher inquiry

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Teacher inquiry can be a complex and cumbersome process for individual teachers, particularly as often heavy workloads allow limited time for reflection on teaching practice. Teacher inquiry can also be challenging when it happens in isolation from other teachers.

Digital technologies can be used to support teacher inquiry (Lockyer et al, 2013). In particular, the synergy between teaching analytics and learning analytics has the potential to facilitate the efficient implementation of the full cycle of inquiry (Mor et al, 2015).

In the previous two modules, we demonstrated how classroom teachers can use:

Teaching Analytics to reflect on their teaching practice as they design lessons for classroom delivery, and

Learning Analytics to reflect on their teaching practice during classroom delivery.

In this module, we will show you how combining Teaching and Learning Analytics can support teacher inquiry. More specifically, Teaching and Learning Analytics (TLA) can work in combination by:

using the structured description and analysis of the teaching design provided by Teaching Analytics to help identify the inquiry problem, develop specific questions to guide inquiry, and to document the teaching design; and

using the data collection, processes and analytical capabilities of Learning Analytics to make sense of students’ data in relation to the teaching design elements, and help the teacher to take action.

This means that the concept of TLA can be embedded into the teacher inquiry cycle and used to support teachers to engage in continuous reflection, as follows:

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teacher inquiry typically engages the teacher in a cyclical process that follows a number of steps, including:

Identify a Problem to Investigate. The teacher identifies an issue of concern in relation to a specific aspect of their teaching design (e.g. lesson plan) that they wish to investigate/evaluate in order to possibly improve it.

Develop Inquiry Questions and Define Inquiry Method. The teacher develops specific questions to investigate the identified problem and defines the appropriate method to study these questions. This includes the definition of educational data that need to be collected, processed and analysed during the inquiry, and the diverse sources from which they can be collected.

Elaborate on and Document Teaching Design. The teacher defines and documents all elements of teaching design (e.g. lesson planning) to be implemented during the inquiry.

Implement Teaching Design and Collect Data. The teacher implements their teaching design (in the classroom) and collects the relevant educational data to analyse.

Process and Analyse Data. The teacher processes and analyses the collected data to obtain insights related to the defined inquiry questions.

Interpret Data and Take Action. The teacher makes an effort to interpret the analysed data in relation to their own conceptualisation of the identified problem, and then takes action in relation to their teaching design (e.g. refines or revises elements of their lesson plans).

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Teacher inquiry: A method for data driven reflective practice

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Teacher inquiry: A method for data driven reflective practice

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Teacher inquiry is recognised as a prominent method for data-driven reflection-on-action (Dana & Yendol-Hoppey, 2014). It is defined as:
“[a process] that is conducted by teachers, individually or collaboratively, with the primary aim of understanding teaching and learning in context” (Stremmel, 2007)

Marian Mohr and her colleagues identified teacher inquiry as an "intentional, systematic, public, voluntary, ethical and contextual" (Mohr et al, 2004) process undertaken by teachers to:

obtain a better insight into their classroom practice in an effort to improve it;

develop reflective practitioner competencies; and

contribute to school self-evaluation and improvement planning;

with the aim of supporting students' learning and wellbeing.
As we discussed previously, teacher inquiry (or teacher research) refers to the process where teachers build their own useful knowledge about teaching and learning through the deliberate and systematic study of their own practice (Check & Schutt, 2012). This involves asking relevant questions and using appropriate methods to investigate them.

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This type of reflection takes a more systematic approach in which practitioners intentionally review, analyse and evaluate their practice after it has been performed. This is typically carefully documented. For example, a teacher may collect data from the delivery of their lessons and use it to identify whether aspects of their teaching design led to students’ low engagement. Based on this analysis, they will then re-design parts of their lesson plan.

The key difference between these two types of reflection is that reflection-on-action engages the practitioner in explicitly investigating their practice with the ultimate goal of identifying areas which could be improved. As a result, this type of reflection can also be seen as a self-guided form of professional learning.

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This type of reflection takes place while the practice is executed and the practitioner reacts on-the-fly. For example, a teacher realising that their students demonstrate low engagement in the lesson would most likely take action to adjust their instruction and involve students more.

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Donald Schon (1983) identified two different types of reflective practice: reflection-in-action and reflection-on-action. These are illustrated below.

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Reflective teaching practice: A key issue in teachers' professional development

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Reflective teaching practice: A key issue in teachers' professional development

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Donald Schon (1983) introduced the concept of reflective practice for both individual professionals and organisations. The main thesis of reflective practice is that purposeful and systematic reflection on experience is key for continuous learning. As a result, reflective practice has been identified as an important instrument for practice-based professional development as well as organisational learning and improvement.
Reflective teaching practice has been defined as:
“The means by which learning, renewal, and growth continue throughout the development of career” (Barr et al., 2005)

“A means by which practitioners can develop a greater level of self-awareness about the nature and impact of their performance, an awareness that creates opportunities for professional growth and development” (Osterman & Kottkamp, 1993)

“[A process that] involves thinking about and critically analyzing one's actions with the goal of improving one's professional practice.” (Imel, 1992).

This means that reflective teaching practice:

involves the self-evaluation of a classroom teacher’s own teaching practice, based on observations and data collected targeting to improve it; and

is related to continuous individual teacher professional development, as well as school improvement.

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Module 4 - Teaching and learning analytics to support teacher inquiry 

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Module 4 - Teaching and learning analytics to support teacher inquiry 

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In general, Teacher Inquiry (or teacher research) refers to the process where teachers build useful knowledge about teaching and learning in their classroom through the deliberate and systematic study of their own practice.
Teacher inquiry is a key element in reflective teaching practice. Nevertheless, it can be a complex and cumbersome process for individual teachers, given that their typical teaching workload allows limited time for reflection on their teaching practice. That is why technologies that support and facilitate teacher inquiry, such as Teaching And Learning Analytics tools, are becoming increasingly relevant.

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The following table summarises the outcomes that each learning analytics strand may deliver to teachers:

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Learning Analytics are commonly classified into three main strands, namely:

Descriptive Learning Analytics

Predictive Learning Analytics

Prescriptive Learning Analytics

In principle, all strands can exploit the same student data, however they process them in a different way in order to provide different outcomes to teachers.
Let’s explore each one in more detail.
DESCRIPTIVE ANALYTICS
The descriptive strand refers to:

“Describing and analyzing historical data collected on students [..]. The goal is to identify patterns from samples to report on current trends” (Daniel, 2015)

Typically, Descriptive Learning Analytics process student data in order to summarise and report on them using dashboards.
Thus, Descriptive Learning Analytics should not aim to simply visualise student profile data as-is. Instead, they should create dashboards that depict meaningful patterns or insights emerging from the student data.

PREDICTIVE ANALYTICS
The predictive strand refers to:

“The statistical analysis of historical and current data derived from learners and the learning process to create models that allow for predictions that improve the learning environment within which it occurs” (EDUCAUSE, 2015)

Typically, Predictive Learning Analytics process student data to predict future trends in student progress. As this short article from The Journal notes, the most common use of Predictive Analytics is to help classroom teachers to understand how individual students might perform in the future and, therefore, to identify students who might become ‘at-risk’ in terms of low performance or low engagement.
PRESCRIPTIVE ANALYTICS
The prescriptive strand refers to:

“Suggesting actions based on descriptive and predictive analyses of complex data” (IBM, 2013)

Typically, Prescriptive Learning Analytics process student data to generate recommendations for further teaching and learning actions (e.g., suggest alternative educational resources or tools) aiming to influence the predicted paths of individual students.

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useful learning analytics infographic produced by the European Union Learning Analytics Community Exchange (LACE) project.

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The main types of outcomes that Learning Analytics can provide to support classroom teachers in facilitating personalised learning to their students, include:

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The main types of outcomes that Learning Analytics can provide to support classroom teachers in facilitating personalised learning to their students, include:

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Outcome #1: Discover patterns within student data.
To perform this task, Learning Analytics process student data and discover patterns related to student learning. The expected result is to have a better and more accurate insight of students trends during their past and current learning and assessment activities. This can help the classroom teacher provide, among other things, better informed feedback and scaffolding, as well as to conduct more holistic assessments that consider students’ overall performance and engagement throughout the learning process.
Outcome #2: Predict future trends in students’ progress.
To perform this task, Learning Analytics process student data and predicts future trends based on the knowledge created by the past history. The expected result is to predict how the students (individually or in groups) might perform in the future, for example in terms of performance or engagement.
Outcome #3: Recommend teaching and learning actions to either the teacher or the student.
To perform this task, Learning Analytics extend the two previous tasks using intelligent analysis of both student data and the conditions of the learning context. The expected result is to generate recommended actions for both teachers and students. Such recommendations may include recommendations for educational resources/tools or learning and assessment activities that are appropriate to meet the individual needs of students.

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As we have already discussed and will demonstrate later in this module, Learning Analytics can provide the methods and tools to:

Collect student data during the delivery of a teaching design (e.g., a lesson plan) to update individual student profiles. The types of student data that Learning Analytics typically collect belong to the “Dynamic Student Data” category discussed previously. In this regard, it is data collected during the learning process, such as:

Data type #1: Engagement in learning activities. For example, the progress each student is making in completing learning activities or the level of each student’s contribution in collaborative activities and discussions.

Data type #2: Performance in assessment activities. For example, formative or summative assessment scores or the number of attempts required before a correct answer.

Data type #3: Interaction with Digital Educational Resources and Tools, for example which educational resources each student is viewing/using or how much time they are spending viewing/using them.

Data type #4: Behavioural data. For example behavioural incidents or attendance level.

Analyse and report on student data to provide insights from the learning process and help the classroom teacher to choose appropriate additional personalised interventions (including, feedback, assessment, scaffolding or support). To facilitate this process, Learning Analytics can provide different types of outcomes, utilising both “Dynamic Student Data” (defined above) as well as “Static Student Data”, which is commonly stored in Student Information Systems.

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Learning analytics: a technology for supporting personalised teaching and learning

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Learning analytics: a technology for supporting personalised teaching and learning

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Learning Analytics have been defined in various ways, for example:
"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" (SOLAR, 2011).

"Learning Analytics is the use of data and models to predict student progress and performance, and the ability to act on that information"(Next Generation Learning Challenges, cited in Siemens, 2010)

"The collection and analysis of usage data associated with student learning" (EDUCAUSE, 2011)

"Collecting traces that learners leave behind and using those traces to improve learning" (Erik Duval, 2012)

Overall, these definitions share a common theme: that learning analytics involves collecting, analysing and reporting on student data from the learning process, with the aim of improving the learning conditions for individual learners or groups of learners. This is mainly related to personalising teaching and learning according to individual student needs.

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Hi everybody! My name is Nikos Manaras. I teach math at a High School in Thessaloniki.

I hope this MOOC will provide us all with new inspiration for innovative teaching and learning.

I'm keen on technology, which I have long been trying to implement in my teaching practice.
https://twitter.com/NikgMan

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Student profiles to support personalisation

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Student profiles to support personalisation

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Personalised learning relies on monitoring the learning process to measure, collect, analyse and report on learners’ educational data. This is commonly achieved using student profiles.
A student profile is a set of attributes and their values that describe a student. As suggested in this infographic by the Data Quality Campaign project, the types of student data commonly used by schools to build and populate student profiles can be classified in two types:
Static student data
Refers to personal and academic attributes of students which can remain unchanged for large periods of time. Such data are usually stored in Student Information Systems and are mainly related to:

Student demographics, such as race, age, special education needs or nationality.

Past academic performance data, such as history of course enrolments or academic transcripts

Dynamic student data
Data generated in a more frequent rate and mainly refer to students’ activities during the learning process. Such data are usually collected by the classroom teachers and/or through Learning Management Systems. They are mainly related to:

Data regarding student engagement in the learning activities, such as level of participation in the learning activities, level of motivation or level of usage of educational resources.

Data regarding student behaviour during the learning activities, such as disciplinary incidents or absenteeism rates.

Data regarding student performance, such as formative and summative assessment scores.

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There are many types of data that support student learning-and they're so much more than test scores. But individual data points don't give the full picture needed to support the incredibly important education goals of parents, students, educators, and policymakers.

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Why is Personalised learning highlighted as a key challenge in 21st Century education?

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Why is Personalised learning highlighted as a key challenge in 21st Century education?

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The global focus on Personalised learning in 21st Century school education has come about as a result of empirical evidence revealing the benefits it can deliver to students.
For example, in the USA, the Bill and Melinda Gates Foundation and RAND Corporation are conducting a large-scale study to investigate the potential of personalised learning in school education. Their initial project results from over 20 schools claim an almost universal improvement in student performance. As the report indicates, one significant factor that explains this improvement is the use of student data to meet individual student needs and provide targeted support.
In another example, Education Elements conducted a study with 117 schools from 23 districts in the USA, in order to identify the impact of personalisation on students' learning. The results showed a consistent improvement in students’ learning outcomes and engagement. These results were mostly attributed to the use of students’ progress data that allowed for:

students’ self-reflection; and

teachers’ targeted support to individual students based on their needs.

You can find more case studies of schools that have adopted innovations to support personalisation in their teaching practices in the additional optional readings list provided at the end of this module.
These case studies make it evident that a key element for successful personalised learning is the measurement, collection and analysis of appropriate student data.

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Personalised learning: A key challenge in 21st Century education

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Personalised learning: A key challenge in 21st Century education

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One of the key challenges for 21st Century school education is how to better support individual student learning in a pedagogically effective and practically efficient personalised manner, based on their individual short, mid and long-term needs. This is typically referred to as Personalised Learning.
This challenge has been highlighted as a key priority in a number of educational policy and foresight reports globally, including:

the 2015 NMC K-12 Horizon Report produced by the New Media Consortium - personalising learning is highlighted as a significant but difficult challenge for K12 education;

the Personalised Learning report published by the International Society for Technology in Education - Peggy Grant and Dale Basye argue for the need to use digital technologies to personalise learning in school education so as offer better learning experiences to each individual student according to their needs; and

the Next Generation Learning Challenges, a network founded by EDUCAUSE in partnership with the Council of Chief State School Officer (CCSSO), the International Association for K-12 Online Learning (iNACOL), the League for Innovation in the Community College, and the Bill & Melinda Gates Foundation has defined personalised learning in schools as one of the main challenges to research and build sustainable solutions for.

So why has personalised learning in school education attracted all this global attention? Let’s have a look at some case studies that showcase the benefits it can deliver.

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Module 3:Learning Analytics in school classroom teaching practice

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Module 3:Learning Analytics in school classroom teaching practice

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In general, Learning Analytics refers to methods and tools that monitor the learning process to measure, collect, analyse and report on learners’ educational data and the learning context in which the data is generated, with the aim of improving the learning conditions for individual learners or groups of learners. This is related to Teaching Analytics (which we covered in Module 2), which provide the means to analyse the learning context, by analysing the educational design.
In the context of school classroom teaching practice, Learning Analytics refers to methods and tools that monitor the classroom delivery of a lesson plan (including homework as well as other school-based facilities, such as a laboratory), to measure, collect, analyse and report on students’ educational data. The aim of Learning Analytics is to better inform classroom teachers on both their interventions to support individual students during the delivery of a lesson, and in reflecting on their lesson plans.

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Digital teaching analytics tools aim to address the shortcomings of paper-and-pencil instruments.
As the image below shows, digital teaching analytics tools can be:

embedded in Lesson Planning tools, or

embedded in Learning Management Systems (LMS).

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However, paper-and-pencil teaching analysis instruments have some disadvantages. These include:

limited capacity for supporting self-reflection (Teaching Analytics task #1), since they cannot generate any kind of support in the form of dashboards or feedback;

limited support for analysing classroom teaching design through sharing with peers or mentors for receiving feedback (Teaching Analytics task #2); and

no support for analysing classroom teaching design through co-designing and co-reflecting with peers (Teaching Analytics task #3).

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Another example of an evaluation checklist to help teachers reflect on their lesson plan is provided by the STARTALK project, which is administered by the National Foreign Language Center at the University of Maryland in USA. Downis an excerpt from an evaluation checklist that aims to provide the teacher with a benchmark against which to “evaluate” their design.

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by using paper-and-pencil instruments or

by using digital tools.

PAPER-AND-PENCIL TEACHING ANALYSIS INSTRUMENTS
Paper-and-pencil teaching analysis instrumentsmostly refer to lesson plan evaluation checklists and/or rubrics. Lesson plan evaluation checklists/rubrics commonly provide a list of criteria that the teacher uses to “evaluate” and reflect on their teaching design.

For example, the table on the left depicts an excerpt from an evaluation rubric provided by the University of Northern Colorado (click on it to access the whole rubric). This checklist defines a set of questions that the teacher can use for self-reflection and improvement of their classroom teaching design.

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Analyse classroom teaching design through sharing with peers or mentors for receiving feedback
Teaching Analytics can also be used to support the process of sharing a lesson plan with peers or mentors, allowing them to provide feedback through comments and annotations.

In this case, Teaching Analytics provides the functionality to:

share a structured lesson plan with peers or mentors;

allow the peer/mentor to annotate or comment directly on the lesson plan;

reflect on the commented/annotated lesson plan.         TEACHING ANALYTICS TASK #3 :
Analyse classroom teaching design through co-designing and co-reflecting with peers
Finally, Teaching Analytics can allow peers to jointly analyse and annotate a common teaching design in order to allow for co-reflection.
In this case, Teaching Analytics provides functionalities for a cycle of:

creating a collaborative space for co-analysing lesson plans between peers;

annotating or commenting on joint lesson plans by all collaborating peers;

co-reflecting on the commented/annotated joint lesson plan.

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Analyse classroom teaching design for self-reflection and improvement
In this case, Teaching Analytics can be used to analyse a lesson plan and help teachers to self-reflect. This can be performed in three ways:

1a. Visualise the elements of the lesson plan and facilitate teacher reflection on their design. Examples of elements that can be visualised include:

the number and/or types of learning/assessment activities of the lesson plan;

the number and/or types of educational resources of the lesson plan;

the teaching time allocated in each learning/assessment activity.

1b. Visualise the alignment of the lesson plan to educational objectives / standards, to allow the teacher to track if and to what extent the objectives of their lesson plans are aligned to specific educational objectives or relevant standards.
1c. Perform validation of a lesson plan in order to highlight potential inconsistencies in the teaching design. For example, inconsistencies could relate to misallocations of time between the overall lesson plan and the individual learning/assessment activities.

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Teaching Analytics can be used to support three main tasks related to teaching planning:

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Chalk.com - Lesson 2 - Templates and Lesson Planning

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This video walks through some basics of interacting with the Planboard lesson planner: -How to edit a lesson plan -How to create a lesson plan template -How to assign standards to a lesson -How to add images and videos to a lesson

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OnTESOL: Why is Lesson Planning Important?

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Acquire professional lesson planning skills. Take an accredited TESOL/TEFL certificate course http://www.ontesol.com

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Lesson plans are usually based on templates. These templates define a set of elements that teachers should populate in order to capture and document their teaching designs in a structured manner.
As presented in Maloy et al (page 62), lesson plan templates should include at least three core elements:

the educational objectives/standards to be attained by students;

the flow and timeframe of the learning and assessment activities to be delivered during the lesson; and

the educational resources and/or tools that will support the delivery of the learning and assessment activities.

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Capturing and documenting your classroom teaching designs: Lesson plans

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Capturing and documenting your classroom teaching designs: Lesson plans

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"A concise working document which outlines the teaching and learning that will be conducted within a lesson" (Butt, 2008)

"The instructor’s road map of what students need to learn and how it will be done effectively during the class time" (Center for Research on Learning and Teaching)

"A teaching outline of the important points of a lesson for a single class period, arranged in the order in which they are to be presented. It may include objectives, materials, assignments" (McNeil, & Wiles, 1990)

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In general, teaching analytics refer to methods and tools that enable those involved in educational design (instructional designers and/or educators) to analyse their designs in order to better reflect on them (as a whole or elements of them), aiming to improve the learning conditions for their individual learners or groups of learners.

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In this module, you have learnt:

what educational data is and how it can support data-driven decision-making at various levels of school operations, strengthening school autonomy;

what (educational) data literacy for teachers is and why it is considered a core competence for supporting not only School Accountability and Compliance to (National) Regulatory Standards, but also continuous school self-evaluation and improvement; and

what (educational) data analytics technologies are and why they are essential for efficient and effective educational data-driven decision-making, highlighting Teaching Analytics, Learning Analytics and their synergy (Teaching and Learning Analytics) as the technologies which will be further discussed in the rest of this course.

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Data analytics refers to methods and tools for analysing large sets of different types of data from diverse sources, which aim to support and improve decision-making.  Data analytics are mature technologies that are currently applied in real-life financial, business and health systems.
However, it is only recently (Johnson et al. 2011, 28-30), that data analytics have been considered in education - first in higher education, and more recently in school education (Bienkowiski et al. 2012).
Educational data analytics technologies that can support teaching and learning can be classified into three main types :

1. Teaching Analytics refer to methods and tools that enable those involved in educational design(instructional designers and/or educators) to analyse their designs, in order to better reflect on them (as a whole, or elements of them), with the aim of improving learning conditions for their targeted individual learners or groups of learners.  Typically, the analysis of educational designs is combined with insights from their implementation (for example through learning analytics).

2. Learning Analytics refer to methods and tools that monitor the learning process to measure, collect, analyse and report on learners' educational data, and the learning context in which it is generated, aiming to improve the learning conditions for individual learners or groups of learners. It is related to teaching analytics, which provides the means to analyse the learning context, by analysing the educational design.

3. Teaching and Learning Analytics combines both teaching analytics and learning analytics, to help teachers systematically reflect on their teaching design using evidence from the delivery to the students in the classroom.

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Although it is anticipated that building data literacy capacity will become a competitive advantage in both teaching professional practice and school quality outcomes, there are currently limited opportunities for developing data literacy competencies.
In their 2006 report, Making Sense of Data-Driven Decision Making in Education, Julie Marsh and her colleagues (Marsh et al. 2006, 9) recognised a number of barriers for the effective and efficient take-up of educational data use in schools' self-evaluation and improvements. These include:
Access to educational data
Lack of easy access to diverse data from different sources internal and external to the school system

Timely collection and analysis of educational data
Delayed or late access to data and/or their analysis might affect the efficiency of the planned-in-response-to-data intervention

Quality of educational data
Verification of the validity of collected data - do they accurately measure what they are supposed to?

Lack of time and support
A very time- and resource-consuming process (infrastructure and human resources)

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Prof Ifenthaler talks about Data Literacy and why it is becoming increasingly important for teachers as a core competence for supporting not only School Accountability and Compliance to (National) Regulatory Standards, but also continuous School Self-Evaluation and Improvement.

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Teach Toddlers Colors Numbers Preschool Race Cars & Magnet Truck

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EDU1x - Analytics for the Classroom Teacher

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Data Quality Campaign, the non-profit U.S. organisation launched in 2005 to promote the use of educational data in school education, has also defined a similar set of data literacy competencies for teachers in their “Roadmap for Educator Licensure Policy Addressing Data Literacy” report.  They are summarised below:
Roadmap for Educator Licensure Policy Addressing Data Literacy (DQC, 2014)

1. Locate and Collect Relevant Educational Data

2. Synthesise and Analyse Educational Data from Diverse Sources

3. Know about Educational Data beyond Grades

4. Understand How to Use Educational Data beyond Grades

5. Engage in a Data-Driven Continuing Inquiry Process

6. Use Data Analysis to Customise Teaching Plans to Diverse Groups

7. Use Own Data to Reflect on Practice

8. Facilitate Students to Understand their Data

9. Communicate Insights from Data Analysis to Diverse Internal and External Stakeholders

10. Monitor this process in a continuous manner

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As we can see from the above, data literacy competence for teachers involves the holistic ability, beyond simple student assessment interpretation ("assessment literacy"), to identify, collect, combine, analyse, interpret and act upon educational data from different sources, for the support of both continuous school self-evaluation and improvement, as well as reporting for external accountability and compliance to regulatory standards.

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Classroom teachers' educational data literacy competencies

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Classroom teachers' educational data literacy competencies

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Data literacy for teachers is now recognised as a key set of competencies that need to be included in both pre-service teacher education and in-service teachers' professional development.  Although it is still early days, it is anticipated that building data literacy capacity will become a competitive advantage in both teaching professional practice and school quality outcomes.
Let’s look at some recent efforts to define data literacy competence frameworks for teachers.
In their report, “Teachers' Ability to Use Data to Inform Instruction: Challenges and Supports”, Barbara Means and her colleagues have identified five competence categories that comprise the competence set that “data literate” teachers need to acquire. The following figure summarises the five competence categories:

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Educational data literacy for teachers is defined as:
“the ability to accurately observe, analyse and respond to a variety of different kinds of data for the purpose of continuously improving teaching and learning in the classroom and school” (Love, 2012)

“the ability to understand and use data effectively to inform decisions … composed of a specific skill set and knowledge base that enables educators to transform data into information and ultimately into actionable knowledge” (Mandinach & Gummer, 2013)

“[the capacity to] continuously, effectively, and ethically access, interpret, act on, and communicate multiple types of data from state, local, classroom, and other sources in order to improve outcomes for students in a manner appropriate to their professional roles and responsibilities” (Data Quality Campaign, 2014).

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Dr. Gobby talks about the international trend of School Autonomy reforms, and how Educational Data can be used to inform data-driven decision making at various levels of school operations, strengthening School Autonomy

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EDU1x - Analytics for the Classroom Teacher

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When discussing the school self-study cycle, Barnes makes an analogy – that if you woke up and found your living room flooded, you wouldn’t ignore it and move your couch to the second floor. Instead, you’d “look for the source of the leak, define the extent of the damage, and contemplate remedies” (p. 4). Barnes suggests that genuine school improvement requires that the people who are leading the school also engage in the same process of purposeful planning and acting.

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Educational data for school self-evaluation and improvement

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Educational data for school self-evaluation and improvement

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School self-evaluation and improvement is defined as:
"a procedure involving systematic information gathering initiated by the school with the intention to assess the functioning of the school for supporting decision making, organisational learning, and for fostering school improvement" (Schildkamp & Kuiper, 2010)

Essentially, school self-evaluation and improvement is a continuous process that involves:

assessing performance;

planning for improvements;

implementing the defined plans for improvement; and

reflecting on the implementation.

It is based on educational data from different sources, collected, combined, analysed, and interpreted meaningfully and purposefully.

Frank Barnes (2004) in his seminal work on Inquiry and Action - Making school improvement part of daily practice, proposed a school-improvement guide based on a school self-evaluation cycle of inquiry and action toward continuing improvement.
The following figure summarises Barnes' proposal:

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Educational data for school accountability and compliance

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Educational data for school accountability and compliance

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According to the OECD (Organisation for Economic Cooperation and Development) study on Education at a Glance, school external accountability can be classified into three categories:

Accountability Category
1.Performance Accountability

Description
This type of accountability focuses on evaluating school outcomes in terms of student performance in standardised tests, (inter-) national exams.

Types of Educational Data Used
Outcome Data

Accountability Category
2.Regulatory Accountability

Description

This type of accountability focuses on ensuring that the school adheres to regulations and legislation.
Types of Educational Data Used

Input Data
Process Data
Context Data
Accountability Category
3.Market Accountability

Description

This type of accountability applies when school funding follows students' enrolments, and focuses on school outcomes.
Types of Educational Data Used

Outcome Data
Context Data

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The following video was created by the Data Quality Campaign, a non-profit U.S. organisation launched in 2005 to promote the use of educational data in school education.

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Data is one of the most powerful tools to inform, engage, and create opportunities for students along their education journey-and it's much more than test scores. Data helps us make connections that lead to insights and improvements. Everyone has an important role to play in helping all students succeed in their own individualized ways.

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What is educational data for school data-driven decision-making?

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What is educational data for school data-driven decision-making?

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Educational data can be broadly defined as:
"information that is collected and organised to represent some aspect of schools. This can include any relevant information about students, parents, schools, and teachers derived from qualitative and quantitative methods of analysis." (Lai & Schildkamp, 2013 - p. 10)
As this definition suggests, educational data is not restricted to students' grades in national exams and standardised tests (though that is a common misconception). Instead, educational data comprises a wide range of data generated by various sources, both internal (school-wide and classroom-specific data) and external (state and/or district data) to the school.
Lai and Schildkamp (2013 - pg 11-12) have extended Ikemoto and Marsh’s (2007) categories of educational data, to input data, process data, context data and outcome data.  The following figure presents indicative examples of educational data for each category:
Input data
Student characteristics, such as demographics, prior academic performance, transfer records, native language. Teacher characteristics, such as teacher competences, academic qualifications or professional experience.

Data Process
Data generated during the teaching, learning and assessment processes, both within and beyond the physical classroom premises, such as lesson plans, methods of assessments, classroom management.

Context data
The Curriculum such as subject syllabus (including learning outcomes) and additional educational programs. School Human Resources, Infrastructure and Financial Plans, including educational and non-educational personnel, buildings, hardware/software, expenditure. School Culture such as school climate, student / parent / teacher/ community relations.

Outcome data
Students' Achievements in classroom-based formative assessments, homework, standardised tests, (inter-) national exams. Students' Wellbeing, and Social and Emotional Development such as safety, support, respect for diversity and special needs. Graduate Data on employment after graduation or further academic studies.

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Why data-driven decision-making is important: Support school autonomy

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Why data-driven decision-making is important: Support school autonomy

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School autonomy is globally debated as an essential requirement for achieving better educational outcomes (related to students' learning and wellbeing), as well as more efficient and productive school operations.
the World Bank define school autonomy as:
"a form of school management in which schools are given decision-making authority over their operations" (Arcia et al., 2011)
Typical indicators of school autonomy are related to the type and the level of school leadership and governance decisions that can be made at local school level, and may include:
Educational decisions
including selection of teaching and assessment methods, educational resources and textbooks, etc.

Human resources decisions
including selection and hiring of educational and non-educational staff, professional development, etc.

Resources and infrastructure decisions
including equipment and technology procurement, planning and development of physical learning spaces, etc.

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What is data-driven decision-making in schools?

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What is data-driven decision-making in schools?

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Data-Driven Decision Making (DDDM) in schools has been defined as:
"the systematic collection, analysis, examination, and interpretation of data to inform practice and policy in educational settings"  (Mandinach, 2012).

"[a process that] refers to teachers, principals, and administrators systematically collecting and analysing various types of data […] to guide a range of decisions to help improve the success of students and schools”  (Marsh & Farrell, 2014)

"systematically analysing existing data sources within the school, applying outcomes of analyses to innovate teaching, curricula, and school performance, and implementing and evaluating these innovations" (Schildkamp & Kuiper, 2010)
Essentially, data-driven decision-making in schools refers to a continuous cycle of identifying, collecting, combining, analysing, interpreting and acting upon educational data from different sources, in order to report, evaluate and improve the resources, the processes and the outcomes of schools. Building school-wide capacity for education data-driven decision-making is a key requirement for supporting school autonomy.