"[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.

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.

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.

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

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

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

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

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:

“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).

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)

"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)

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

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.

In this case, Teaching Analytics provides the functionality to:

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:

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.

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.

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.

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:

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

"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.

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.

“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:

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:

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.

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:

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:

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: