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EEB504 Data-Informed Practice (8)

Abstract

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, learning 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 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
OnlineBathurst Campus
Session 3
OnlineBathurst 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 build and articulate a personal position on the role of evidence and data and be able to provide feedback to the perspectives of their peers. The perspective will explore the assumptions on which data-informed practice resides including data quality, data capacity and data culture. This includes distinguishing between leaning, analytics, analytics and knowledge management;
  • be able to demonstrate an understanding of the difference between analytics, learning analytics and data-informed practice in education and their role in a data-informed school or setting;
  • 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 and as a context for completing a self-assessment of your or a known school or setting;
  • be able to apply basic principles of measurement to data-based decision-making processes;
  • be able to identify commitments and theories of action that will enable the setting to become a data-informed learning environment. The commitments are included in Assessment Task 2;
  • be able to conduct a situational analysis of a setting to determine its baseline circumstances with respect to data-informed practice. The situational analysis is used to develop theories of action for Assessment Task 2;
  • be able to build an understanding of the Response to Intervention framework (RTI) as a methodology for taking up the situational analysis and critical actions in screening and progress monitoring at and for Validity Public School;
  • understand and be able to apply the key elements in the cycle of data-informed planning including the screening, progress monitoring and action planning approaches applied to the case study setting;
  • be able to develop an implementation action plan for the case study setting; and,
  • be able to build a professional capacity building session on Data-informed Practice (DIP) that demonstrates their knowledge of the key concepts and processes of DIP.

Syllabus

The subject will cover the following topics:
  • Evidence and Data
  • Key Ideas
  • Data Definition Matrix and Self-Assessment
  • Measurement Principles
  • Evidence-Based Commitments
  • Situational Analysis
  • Framework for Action-Screening and Progress Monitoring
  • Data-Informed Action Planning
  • Implementation
  • Professional Capacity

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The information contained in the 2018 CSU Handbook was accurate at the date of publication: 18 October 2017. The University reserves the right to vary the information at any time without notice.