Educational Data Analytics Technologies For Data-Driven Decision Making In Schools

Supporting Data-Driven Decision Making With Educational Data Analytics Technologies

Data-driven decision making in schools is a global trend aiming to support School Autonomy and allow schools to meet both external requirements of Accountability to Regulatory Standards, as well as internal continuous Self-Evaluation and Improvement needs. A vital aspect of this process is Data Literacy for Teachers, which empowers teachers to use data in the decision-making process. This is not always an easy task without the support of digital technologies. This article discusses Educational Data Analytics technologies for supporting data-driven decision making in schools.

School Autonomy

School autonomy is highlighted as a policy trend for achieving better educational outcomes for students and more efficient school operations.  It has been at the spotlight of Education System Reforms globally, for example in Europe, Australia, and internationally.

In this context, schools are allowed more freedom in terms of decision making, for example curriculum design and delivery, human resources management and infrastructure maintenance and procurement.

This increased level of autonomy, however, is accompanied by an increased need to provide robust evidence of (a) meeting the requirements of external Accountability and Compliance to (National) Regulatory Standards and (b) engaging in continuous School Self-Evaluation and Improvement.

Such evidence can be generated by collecting and analyzing Educational Data.

Educational Data And Data-Driven Decision Making

Data-driven decision making in schools has been described as  “the systematic collection, analysis, examination, and interpretation of data to inform practice and policy in educational settings”. The aim of data-driven decision making is to report, evaluate and improve the processes and outcomes of schools.

Educational Data are generated by various sources, both internal and external to the school, for example:

  • Student data, such as demographics and prior academic performance.
  • Teacher data, such as competences and professional experience.
  • 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.
  • Human Resources, Infrastructure, and Financial Plan, including educational and non-educational personnel, hardware/software, expenditure.
  • Students’ Wellbeing, Social and Emotional Development, such as support, respect to diversity and special needs.

Given the emerging need for data-driven decision making, a core competence for classroom teachers, directly related to their capacity to use educational data for self-evaluation and improvement, is Data Literacy.

Data Literacy For Teachers

Data literacy for teachers is broadly described asthe ability to understand and use data effectively to inform decisions”. It comprises the competence set (knowledge, skills, and attitudes) required to identify, collect, analyze, interpret, and act upon Educational Data from different sources so as to support improvement of the teaching, learning and assessment process. Data Literacy for Teachers is beginning to be included in both teachers’ pre-service and licensure standards (e.g., the CAEP Accreditation Standards), as well as continuing professional development standards (e.g., the InTASC Model Core Teaching Standards).

However, despite its importance, the current level of teachers’ use of educational data is still limited, mainly due to a number of barriers, including (a) limited access to educational data, (b) untimely collection and analysis of educational data, (c) low quality of educational data which can be manually collected, and (d) lack of time and support.

To address these barriers, a specific strand of digital technologies has emerged, referred to as (Educational) Data Analytics.

Educational Data Analytics Technologies

In general, Data Analytics refers to methods and tools for analyzing large sets of data from diverse sources aiming to support and improve decision making.  Even though Data Analytics includes now mature technologies applied in real-life financial, business and health systems, it has only recently been considered in the context of Higher Education and School Education.

Educational Data Analytics technologies are now considered as useful to overcome practical barriers for sustainable and effective data-driven decision making in teaching and learning. They can be classified in 3 main types, as follows:

  1. Teaching Analytics refers to methods and tools that enable those involved in educational design (Instructional Designers and/or educators) to analyse their designs in order to reflect on and improve them prior to the delivery to the learners.
  2. Learning Analytics has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”.
  3. Teaching and Learning Analytics, which combines Teaching Analytics and Learning Analytics to support the process of teacher inquiry, facilitating teachers to reflect on their teaching design using evidence from the delivery to the students.


Following the current international trend for more School Autonomy, the use of Educational Data to inform school decision making for both accountability and self-improvement is a critical issue. However, considering that Data Literacy capacity of teachers is usually hindered by a series of practical barriers, Data Analytics technologies are considered, with Teaching Analytics, Learning Analytics, and Teaching and Learning Analytics being the most promising.

If you are interested to learn more, you can join me and a large community of innovative teachers from around the globe to Curtin's new edX MOOC on Analytics For The Classroom Teacher.