Policy on the Use of Data in the Support of Student Learning (Learning Analytics)

1. Introduction

Data that can be used to enhance the learning experience are increasingly available and are being used at the University of Glasgow. This policy aims to underpin and regularise a secure environment in which staff have confidence in using different types of data in support of student learning, and in which students have appropriate access to data and can trust that their best interests are protected.

2. Definitions

Learning analytics is defined in the Jisc Code of Practice as follows:

“Learning analytics uses data about students and their activities to help institutions understand and improve educational processes, and provide better support to learners.” (Jisc Code of Practice for Learning Analytics, updated August 2018)

In adopting this definition, we understand ‘institutions’ to include systems and people in a wide range of roles, from individual course conveners to teams with strategic oversight of the University’s learning and teaching environment. It is essential that the human judgement of suitably informed individuals and teams plays a role in the interpretation of data for the purposes of learning analytics and in the decisions and recommendations made as a result.

Learning analytics, as we interpret it here, does not include: the use of data with the end-goal of making decisions about student progression or the calculation of course grades or award of degrees or other qualifications to students; data analytics for institutional purposes other than the enhancement of learning and teaching or student engagement; data analytics solely for marketing purposes.

For illustrative examples of learning analytics activities, see the accompanying document, ‘Examples of learning analytics at the University of Glasgow’.

3. Principles

Learning analytics must be carried out in a way that respects rights and privacy, is ethical, and complies with applicable legislation and University of Glasgow policies. Further, we will carry out learning analytics in line with the following principles:

  1. The University, and those individuals or teams carrying out learning analytics, will be transparent to all stakeholders about how data is identified, collected, retained and used from learning and teaching systems.
  2. Students – individually and/or collectively – should have appropriate access to relevant insights gained from learning analytics.
  3. The uses of learning analytics should be justified and documented, and should be defined and explained as clearly as possible to stakeholders, including students.
  4. It is essential that data to be used for learning analytics should be accurate and authentic. Deficiencies or biases in data must be identified and taken into account in the interpretation of data.
  5. Data should be interpreted in as full a context as possible, with recognition that learning analytics data does not give a complete picture of a student’s learning.
  6. Learning analytics should be focused on enhancement and should be used to enable as wide a benefit to students as is appropriate. Interventions or actions stemming from learning analytics must be inclusive.
  7. Learning analytics data or insights from learning analytics will not be used by the University for any purposes beyond those set out in this policy (e.g. for staff performance review).

4. Purposes of Learning Analytics

The Jisc Code of Practice, cited above, expands on the use of learning analytics as follows:

“Learning analytics should be used for the benefit of students. This might be to assist them individually or through using aggregated and anonymised data to help other students. Learning analytics might also be used to improve the educational experience more generally.” (Jisc Code of Practice for Learning Analytics, updated August 2018)

In line with this, learning analytics activities at the University of Glasgow typically take the form of (i) projects and processes enabling interventions to assist students (individually or as a cohort) through the analysis of their own data, for example through the provision of personalised feedback; and (ii) projects and processes that use aggregated and anonymised data to benefit students more widely, such as students taking a course in a subsequent year. Examples of this latter form of activity include projects and processes to analyse datasets to identify trends or to provide staff with information on the effectiveness of pedagogical design, with a view to improving the learning experience for students more widely.

A further type of activity takes the form of Scholarship of Teaching and Learning (SoTL) projects that relate to the study and practice of learning and teaching within an HE setting. When such projects are intended for external dissemination or publication as well as for the benefit of University of Glasgow students, they also require ethical approval (see Section 7, Research ethics). 

5. Legal Basis

Under the General Data Protection Regulation (GDPR), the University must ensure that it has a basis for data processing where that involves personal data. Staff undertaking learning analytics must, therefore, satisfy themselves that there is an appropriate legal basis for the specific activity they wish to undertake, prior to commencing that activity.

When learning analytics activity involves processing of special category personal data or making significant changes that affect the risk level to personal data, the leader of the activity must also undertake a Data Protection Impact Assessment (DPIA).

Staff who, as part of a learning analytics activity, wish to share personal data outwith the University must make adequate arrangements to safeguard the handling and processing of these data. These arrangements may require a formal data sharing agreement to be entered into between the University and the other organisation(s) to ensure that all aspects of the processing of the personal data are managed appropriately. The Data Protection and Freedom of Information (DP&FOI) Office can assist in ascertaining whether there is a requirement for a data sharing agreement or other measures, if needed.

6. Data Processing and Retention

For any analytics activity, it is important to be clear who has access to the data, and to ensure appropriate measures are in place to protect it. Retention of data is dependent on the purpose for which it is gathered and will likely differ between projects. In any event, any personal data collected for learning analytics activities should only be kept for as long as necessary for the purposes of that processing, in line with the GDPR.

General guidance on records retention and the online Information Asset Register are available on the DP & FOI Office webpages.

7. Research Ethics

Learning analytics projects require University ethical approval when: (1) they take the form of non-clinical research or scholarship involving human subjects or include non-University of Glasgow data not in the public domain; and (2) they are intended to have externally-facing outputs such as conference presentations or publications. In such cases, project leaders should apply to the appropriate College Ethics Committee before commencement of the project.

Ethical approval, including consent from participants where needed, is required if a learning analytics project which was originally intended solely to inform practice within the University of Glasgow is subsequently to be disseminated externally.

8. Data Types

The following are examples of types of data that may be used for learning analytics activity where they are available and of appropriate reliability. Given advances in educational technology, it is expected that some of these types of data will become more readily available and more robust in future, and that new types of data will also become available. Some of these data types are, or will in future be, collected by the University as standard; other types may need to be collected through special efforts if staff wish to use them for learning analytics purposes. Some types are more relevant to learning analytics activity intended to support students on an individual basis; other types are more relevant to work to improving the educational experience more generally.

  • Assessment data including grades (both granular and aggregated)
  • Measures of engagement in class
    • Attendance data
    • Assignment completion data
    • Data on student contributions to discussion groups in class
    • Other measures of active engagement
  • Measures of engagement outside of class
    • Use of the virtual learning environment and/or other learning platforms
    • Engagement with online learning materials
    • Contributions to discussion fora and blogs
    • Visits to study spaces on campus
    • Time spent on campus and use of the University digital network
    • Engagement with Student Learning Development
  • Measures of self-assessment (e.g. through questionnaires or other reflective tools)
  • Data collected specifically for learning analytics (e.g. focus group data)

9. Transparency and Student Access to Data

The University of Glasgow Student Privacy Notice refers to the institution’s use of student data in learning analytics activities. Under the GDPR, students have a number of rights, including the right of access and the right to rectification of personal data held about themselves, subject to certain exemptions set out in the applicable legislation.

In addition to any legal requirements, staff undertaking learning analytics projects and processes who wish to share aggregated and unidentifiable learning analytics data with the students whom it is intended to benefit must ensure that the data is presented in a way which is contextualised and meaningful.

10. Oversight

Oversight of this policy lies with Education Policy & Strategy Committee (EdPSC) and the University Senate.