Principles for the ethical use of learning analytics

The University is committed to ethical use of student data, and deploying learning analytics for the purpose of informing student-and learning-centred education practices and optimising the learning environment.

Ethical Data-Use

Staff will use learning analytics in accord with the following principles/practice domains. These data-use principles are underpinned by the policies and regulations mentioned below and the Information Management Framework.

  1. PURPOSE
  2. LEARNING ECOLOGY
  3. EVIDENCE-INFORMED
  4. DATA ANALYSIS AND INTERPRETATION
  5. LEARNER ECOLOGY
  6. ASSESSMENT
  7. STUDENT ACCESS
  8. DUTY OF CARE
  9. STUDENT FEEDBACK
  10. RESEARCH
  11. EDUCATIONAL PRACTICE
  12. DATA QUALITY
  13. STAFF CAPABILITY

1. PURPOSE

Student information is used to support students’ learning experience and outcomes. University staff should have clearly defined purposes for the use of student data.

2. LEARNING ECOLOGY

Student data should be understood in context. Learning analytics should not be considered to be generalisable information, but rather, the source of potentially actionable insights relevant in a particular context.

3. EVIDENCE-INFORMED

As with most data, learning analytics is not a single source of truth, and should be interpreted in conjunction with other relevant data and in accord with evidence from scholarly research and evidence-informed learning design.

4. DATA ANALYSIS AND INTERPRETATION

Analysis of data and analytical models should be methodologically sound. Data and algorithms can perpetuate bias or negative assumptions and every effort should be made to minimise bias when making inferences.

5. LEARNER ECOLOGY

Students have multiple identities, commitments, and learning practices, many of which are not readily quantifiable. While learning analytics may provide valuable insights, students cannot and should not be wholly defined by their data. Learning analytics must not be used to limit the University’s or the students’ expectations of what they can achieve. In the case of predictive analytics based on historical data, it is accepted that there will always be individuals whose behaviours do not follow the typical pattern. The University does not make discriminatory use of gender, age and ethnicity to distinguish, exclude or restrict or make preference in the learning environment.

6. ASSESSMENT

Metrics derived from data sources used for learning analytics should not be used as a form of assessment nor to influence the marking of any student assessments.

7. STUDENT ACCESS

Students are notified of how their information is collected, used, and disclosed through the Personal Information Privacy Plan and related appendices. This includes procedures on how to access their personal data, and the learning analytics performed on it, at any time in a meaningful, accessible format. Students have the right to correct any inaccurate personal data held about themselves.

8. DUTY OF CARE

There may be cases when data and insights cannot be shared because it is impractical or where it might have harmful impact on the student’s academic progress or wellbeing.

9. STUDENT FEEDBACK

Any provision of learning analytic data as feedback or any other purpose in any form back to students should be undertaken within a clear evidenced-informed educational strategy and supported by processes that facilitate student’s understanding, empowerment and positive application of the data towards their learning and wellbeing.

10. RESEARCH

Any proposed academic research using University-held student information, including analytics, will be conducted in accord with the ethics requirements for research involving humans as outlined in the Australian Code for the Responsible Conduct of Research.

11. EDUCATIONAL PRACTICE

Principles of common sense ethical conduct for human research should be applied when using data for educational practice.

12. DATA QUALITY

All stakeholders have a responsibility to work in partnership to maintain the quality and validity of data. The collection and management of learning analytics will be reviewed regularly to ensure ongoing relevance to learning experiences and outcomes and consistency with current research.

13. STAFF CAPABILITY

The University is committed to providing professional learning opportunities and resources to enable staff to effectively and ethically utilise learning analytics. Staff have a responsibility to engage in professional learning to optimise their use of learning analytics.

Student Privacy

University staff are responsible for the ethical use of student data. The use of student data must comply with external legislation, including the Queensland Information Privacy Act 2009, and the University’s Personal Information Privacy Plan. Student data that can reasonably identify an individual is personal information and must be managed in accordance with the Griffith University Privacy Plan.

Data should be managed as relevant to the responsibility of accountable professional and academic roles as specified in the Information Security Policy Schedule A: Roles, Standards and Operational Procedures. In the context of course data, Course Convenors are accountable for access and use of their course data.

Data Security and Retention

Student data will be analysed, stored, and transmitted using appropriate safeguards to protect its security and integrity. Student data will be retained and disposed of in accordance with the relevant retention and disposal policies issued by Queensland State Archives under the Public Records Act.

At Griffith, the two main Schedules covering the retention and disposal of student-related information are the General Retention and Disposal Schedule for Administrative Records and the University Sector Retention and Disposal Schedule. Secure storage and transmission of personal information is governed by the Information Security Policy and the Data Classification Guidelines.

Learn more about Griffith Information Management

Download the principles

Download a copy of the Principles for the Use of Learning Analytics at Griffith in PDF format

Preferred citation

Centre for Learning Futures. (2017). Principles for the use of learning analytics at Griffith. Retrieved from https://www.griffith.edu.au/learning-futures/course-analytics-at-griffith/guiding-principles

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