LEARNING ANALYTICS DATA USE POLICY
Date first approved:
8 December 2017
Date of effect:
8 December 2017
Date last amended:
Date of Next Review:
First Approved by:
Custodian title & e-mail address:
Director, Learning, Teaching and Curriculum
Learning Analytics Specialist, Learning, Teaching and Curriculum
Responsible Division & Unit:
Learning, Teaching and Curriculum
Deputy Vice-Chancellor (Academic) Portfolio
Supporting documents, procedures & forms:
Learning Analytics – UOW Student Perspective Survey 2013
Learning Analytics Strategy 2013-18
Relevant Legislation &
- 1 Purpose of Policy 5
- 2 Definitions 5
- 3 Application & Scope 6
- 4 Policy Principles 6
- 5 Transparency in Learning Analytics Activities 7
- 6 Student Access to Learning Analytics 7
- 7 Student Privacy 8
- 8 Validity of Data and Analytics Processes 9
- 9 Enabling Positive Student Interventions 9
- 10 Minimising Adverse Impacts 9
- 11 Continuous Improvement of Teaching and Learning Activities 9
- 12 University Research 10
- 13 Consequences of Breaching this Policy 10
- 14 Roles & Responsibilities 10
- 15 Version Control and Change History 12
- 16 Appendix A: Approval Process – University Research Using Learning Analytics Data 13
- 1. The purpose of this policy is to:
- 1.1 Assist Staff to fulfil their responsibility to take reasonable care in obtaining, storing, processing and distributing data for learning analytics.
- 1.2 Provide guidelines for the management of student privacy and ethical use of data issues that arise within the context of learning analytics at UOW. This includes use of learning analytics data for research purposes.
- 1.3 Establish a best-practice culture of data management for all UOW learning analytics stakeholders.
Definition (with examples if required)
Staff employed under the Academic Staff Enterprise Agreement.
A student with a personalised data-profile which includes evidence informed risk factors that can inhibit successful completion of studies.
Data and Analytics Self Service Hub. The UOW enterprise business intelligence platform.
Extracting or mining knowledge from large amounts of data.
A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.
Deputy Vice-Chancellor Academic
Human Research Ethics Committee
Information Management Unit. A single unit within Information Management and Technology Services (IMTS).
The measurement, collection, analysis and reporting of data about students and their learning contexts.
Learning analytics data
The aggregated dataset sourced from smaller datasets many of which originate in the numerous transaction processing systems in use at UOW.
Learning Analytics Governance Committee
A group established by the Deputy Vice-Chancellor (Academic) with responsibility for reviewing and advising on the strategic direction and on operational matters at UOW regarding the deployment of learning analytics to support student learning.
Professional Services staff
Staff employed under the General Staff Enterprise Agreement.
All persons appointed by the University as academic or professional services staff .
- 1 The use of learning analytics at UOW forms part of the broader activities undertaken to support students through their studies and reach their full academic potential.
- 2 This policy applies to any person responsible for the preparation, analysis, decisions and actions at UOW arising directly from learning analytics.
- 3 The data sources used for learning analytics at UOW cover the key aspects of the UOW learning platform. This includes, but is not limited:
- 3.1 Moodle
- 3.2 SOLS
- 3.3 Library (aggregated information only)
- 3.4 Peer Assisted Study Sessions (PASS)
- 4 Data related to student utilisation of counselling services is protected by health records privacy legislation and excluded from use in learning analytics.
- 5 While the focus of learning analytics at UOW is on near real-time delivery of learning related information to our staff and students to support the student learning experience, the scope of this policy includes the continuous improvement of teaching and learning. This includes academic or professional services staff engaged in research activities associated with the University.
- 6 The scope of this policy includes all related activities within the information management lifecycle of learning analytics data as follows:
- 6.1 Integration
- 6.2 Analysis
- 6.3 Dissemination
- 6.4 Maintenance
- 7 The primary focus of learning analytics is for onshore UOW students.
- 1 The overarching principles of this policy are based on a literature review of the ethical and legal issues associated with learning analytics.1
- 1.1 Responsibility. The Deputy Vice-Chancellor (Academic) [DVC(A)] has overall responsibility for the legal, ethical and effective use of learning analytics.
- 1.2 Transparency. Clear explanations will be given to staff and students at UOW around data sources, analytics purposes and usage practices associated with learning analytics at UOW.
- 1.3 Informed Consent. The general consent obtained by students on enrolment covers the application of learning analytics at UOW. The duty of care obligations towards students means UOW will not permit students to opt out of learning analytics initiatives.
- 1.4 Privacy. Access to student data and analytics should be restricted to those identified by UOW as having a legitimate need to view the data.
- 1.5 Validity. Data and analytic process will be monitored to develop and maintain confidence in learning analytics at UOW.
- 1.6 Access. Students at UOW should be able to access all their own personal data informing their learning analytics in an accessible format.
- 1.7 Enabling positive interventions. Interventions arising from learning analytics are designed to maximise the possibility of making a positive impact for students.
- 1.8 Minimising adverse impacts. Learning analytics will not give a complete picture of an individual student’s learning. Steps will be taken by UOW to ensure outputs of learning analytics do not bias staff, students or institutional perceptions and behaviours towards either staff or students.
- 1.9 Stewardship of data. Data for learning analytics will comply with existing UOW policies. Any use of learning analytics data at UOW must be approved by the DVC(A).
- 1 The University is committed to operating the learning analytics initiatives in a manner, as open and transparent as possible.
- 2 Duty of care obligations for students requires the University to use learning analytics to monitor student progress towards learning goals. A student is unable to opt-out of inclusion in learning analytics initiatives at the University because of this duty of care obligation.
- 3 All students must be made aware of learning analytics activities at the University through multiple touch-points, including but not limited to:
- 3.1 A statement regarding the operation of learning analytics in every Subject Outline;
- 3.2 UOW public webpage explaining learning analytics and the objective to support learning at UOW;
- 3.3 A clear statement on every Moodle site where an academic chooses to use learning analytics, with a link to the page mentioned in 3.2.
- 4 The data sources, the purposes of the analytics, the metrics used, who has access to the analytics, the boundaries around usage, and how to interpret the data must be explained clearly to staff and students using the touch-points outlined in section 5.3 above.
- 5 The University must clearly describe the processes involved in producing the analytics to students and staff. This will be done using different channels such as the touch-points outlined in section 5.3 above.
- 1 Student access to learning analytics is under the following conditions:
- 1.1 Direct access to analytics designed specifically for students
- 1.2 Indirect access granted by academic staff for information about individual students. This could include course-level information as well as subject-level information. The decision to share such information is at the discretion of the individual academic and needs to be mindful of the potentially harmful impact on the student’s academic progress or wellbeing.
- 1.3 Students will be shown the raw data about them used for both student-facing and teacher-facing learning analytics if they ask to see it.
- 2 Students have a right to be able to correct inaccurate personal data held about themselves.
- 1 The use of learning analytics data at UOW is underpinned by a Privacy Impact Assessment (PIA) undertaken in accordance with the best practice available in higher education sectors worldwide.
- 2 Access to student data and analytics is restricted to those staff identified by the institution as having a legitimate need to view the data. This includes, but is not limited to the following:
- 2.1 Relevant academic staff:
a. Subject Coordinators
b. Heads of Students
c. Course Directors
d. Executive Deans
e. Associate Deans (Education)
- 2.2 Relevant professional services staff:
a. Information Management (IMU) staff
b. Pro Vice-Chancellor (Students) staff
c. Learning, Teaching & Curriculum (LTC) staff
- 3 Access to the data contained within the learning analytics data warehouse must be managed to protect student privacy:
- 3.1 Staff may only gain access to the learning analytics data warehouse with the approval of the DVC(A) for the following purposes:
d. Business as usual recipient of learning analytics deliverables;
e. Approved research-based learning analytics activities conforming to the elements outlined in section 12 below.
- 3.2 Staff requesting access to the Learning Analytics data warehouse must also have the approval for access to the DASH student information and student equity sensitive data. This serves as a co-requisite with requirements outlined above in section 7.3.1.
- 3.3 All applications for access to the learning analytics data warehouse must include:
a. Clear descriptions of the data elements being requested
b. Clear descriptions of the intended purpose of access
c. Start and finish dates for access to the data
d. Clear proposals for action to be taken if any learning analytics indicators flag individual ‘at risk’ students. These actions must be consistent with the Guidelines for Actioning Learning Analytics Insights as approved by the Learning Analytics Governance Committee.
- 3.4 Access to the learning analytics data warehouse will only be provided for purposes allowed within ethical and legal constraints, with evidence of any required approvals.
- 5 Use of learning analytics data for academic promotion purposes must not contain personally identifiable student information.
- 1 In order to develop and maintain confidence in learning analytics and ensure it is used to the benefit of students, the University will monitor the quality, robustness and validity of the learning analytics data and processes.
- 2 The University will take measures to ensure that:
- 2.1 Inaccuracies in the data sources for learning analytics are understood and minimised;
- 2.2 The implications of incomplete learning analytics datasets are understood;
- 2.3 The optimum range of data sources for learning analytics purposes are selected;
- 2.4 Misleading correlations on learning analytics are avoided.
- 3 All algorithms and metrics used for predictive analytics or interventions will be understood, validated, reviewed and improved by appropriately qualified staff.
- 1 The circumstances when analytics suggest that a student could benefit from additional support, along with the type and nature of interventions, are specified in the Guidelines for Actioning Learning Analytics Insights.
- 2 The University will record predictions and interventions made based on insights generated through learning analytics processes. This information will be auditable so the appropriateness and effectiveness of both predications and interventions is available for review.
- 1 The University recognises that analytics cannot give a complete picture of an individual’s learning. The University is thus required to take into account personal circumstances when deciding the actions to take on insights generated through learning analytics.
- 2 Opportunities for students to unethically influence the learning analytics will be minimised. This includes preventative actions such as rigorous designs in the source transactional systems. It also includes contingent actions such as robust auditing and careful interpretation of the data.
- 1 Data analysis focussed on the continuous improvement of teaching and learning activities is undertaken with the support of the DVC(A). This support is contingent upon the results of such data analysis not being published, unless express permission has been granted by UOW as per the elements outlined in section 12 below.
- 1 Research conducted by the University using learning analytics data is subject to existing research policies at the University.
- 2 The approval process is covered in Appendix A: Approval Process – University Research Using Analytics Data and includes:
- 2.1 Written approval from the DVC(A)
- 2.2 Written approval from the Human Research Ethics Committee (HREC)
- 1 The University views any breach of this policy as extremely serious. Depending on the severity of the breach, a staff member or student may face disciplinary action in accordance with the Academic Misconduct (Coursework) procedures, Academic Integrity policy, University code of conduct and Professional Staff Misconduct guidelines.
- 1. Staff
- 1.1 Staff have a responsibility to:
a. Act on the insights identified through learning analytics as appropriate to their role;
b. Record details of the interventions triggered by learning analytics;
c. Comply with UOW learning analytics practices as specified in this Policy and associated procedures, guidelines and supporting documents;
d. Comply with the UOW Data Management and Records Management Policy when handling data in learning analytics reports;
f. Comply with UOW research policies if undertaking research using learning analytics data.
- 2 Students
- 2.1 Students have a responsibility to:
a. Act on any learning analytics presented to them. These obligations will be clearly set out and communicated by UOW to students;
b. Not engage in any opportunities to unethically influence the learning analytics;
c. Notify UOW staff if they would like access to their data used for learning analytics purposes;
d. Notify UOW staff of any corrections required to inaccurate personal data held about them.
- 3 Pro Vice-Chancellor (Students) Staff
- 3.1 Pro Vice-Chancellor (Students) staff have a responsibility to:
a. Develop and review student intervention policies and associated procedures and standards;
b. Serve as an intermediary to support services as part of enacted student interventions;
c. Record details of the interventions triggered by learning analytics;
d. Provide advice on learning analytics practices and issues to Business Units across UOW;
e. Contribute to learning analytics training and education programs.
- 4 Learning, Teaching & Curriculum (LTC) Staff
- 4.1 LTC staff have a responsibility to:
a. Implement and support a culture of learning analytics informed educational practice;
b. Design, develop, implement and support analytics processes to be performed on the data;
c. Provide advice on learning analytics practices and issues to Business Units across UOW;
d. Provide learning analytics training and education programs.
- 5 Information Management (IMU) Staff
- 5.1 IMU staff have a responsibility to coordinate and maintain UOW’s Enterprise Data Warehouse, including:
a. Collection and integration of data to be used for learning analytics into the data warehouse;
b. Design and develop the technical infrastructure underpinning learning analytics reports and analysis.
- 6 Learning Analytics Governance Committee
- 6.1 The Learning Analytics Governance Committee has a responsibility to:
a. Consider the impact of student interventions, training requirements and workload on staff roles;
b. Provide clear direction about the priority given to learning analytics in relation to other UOW requirements.
- 7 Ethical Use of Data Advisory Group
- 7.1 The Ethical Use of Data Advisory Group is considered the main forum for consultation with staff and students on this policy.
8 December 2017
1 Refer to ‘Code of Practice for Learning Analytics’ in the supporting documents section at the start of this document.