EEB504 Data-Informed Practice (8)


This subject addresses the knowledge and skills required to engage with the policy and practice of data-informed practice. The subject will distinguish the approach as applied to education from its use in other fields highlighting the strengths, limitations and potential uses of the data available in educational contexts. Knowledge management, analytics, emergent feedback, the data-definition matrix, and data-based decision-making cycle will be defined and interrogated as a context for knowledge and skill-building in the ethical use of data to inform planning, instruction and learning. Key basic psychometric concepts and existing data repositories and approaches will be investigated using a problem-based approach focused on curriculum-based measurement. The critical role of information technology in the process of data-informed practice will also be examined. The subject involves the analysis and resolution of problem-based case studies that provide an opportunity to apply the skills and knowledge learned in the modules of study. A background in measurement and statistics is not required to successfully complete this subject.

+ Subject Availability Modes and Location

Session 1
DistanceBathurst Campus
Session 3
DistanceBathurst Campus
Continuing students should consult the SAL for current offering details: EEB504
Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Teacher Education

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to demonstrate an understanding of the difference between knowledge management, analytics and data-informed practice in education;
  • be able to recognise and understand the assumptions on which data-informed practice resides;
  • be able to explain and apply the data-definition matrix including the distinction between proximal and distal data and low and high power interventions as the key elements of a schema for data-informed practice;
  • be able to know, understand and apply basic principles of measurement required to engage with data-based decision-making processes;
  • be able to understand the strengths and limitations of large-scale tests and how to use the data they provide;
  • be able to know, understand and apply the key elements in the cycle of data-informed planning;
  • be able to analyse, resolve, and evaluate problem-based cases requiring data-informed practice for instructional decision-making in applied educational settings;
  • be able to demonstrate an understanding and application of the design elements of effective professional development; and
  • be able to know, understand and assess conditions of data quality, data capacity and data culture.


The subject will cover the following topics:
  • assumptions and research underpinning data-informed practice;
  • national and state policy drivers for data-informed practice;
  • knowledge management and analytics;
  • the data-definition matrix and its use;
  • measurement and evaluation concepts required for data-informed practice;
  • analysis of existing data sources including the alignment of those sources in the school planning process;
  • the cycle of data-informed practice and planning;
  • the analysis of data-informed practice case studies; and
  • the data-driven professional.


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.