Learning Analytics Policy

Introduction

1. The collection and use of data about students and their learning is providing new opportunities for institutions to support learners and to enhance educational processes. Learning analytics systems present visualisations of student learning activity and provide predictive indicators for attainment. These will be used at the University of Gloucestershire to assist current students in achieving their study goals, and to help us improve our overall provision of education.
2. The institution will use learning analytics to help meet a student-focused vision where, focus will be placed on challenging and supporting students to ensure they produce their best work. This means keeping under continuous review how we strike the right balance in being supportive towards students and maximising their opportunities to succeed while still upholding academic rigour and challenge. The Personal Tutor Scheme will be the key enabler for learning analytics at Gloucestershire: Each student has a personal tutor*, a critical component in monitoring their progress and providing support and encouragement through their learning journey.
   *Each student has a personal tutor for the duration of their studies, although for some provision, including apprenticeships, the tutor carrying out the personal tutor functions may be known by another role title. 
3. Students have a responsibility to engage with the learning opportunities provided by their course. These expectations and the University’s approach to personal tutor interventions are set out in the Engagement and Attendance Policy.
4. The University will ensure that learning analytics is deployed for the benefit of students and follow the following statement of principles:

• We will use Learning Analytics to help all students reach their full academic potential.
• We will be transparent about data collection, sharing, consent and responsibilities.
• We will abide by ethical principles and align with our university strategy, policies and values.
• Learning Analytics will not be used to inform significant action at an individual level without human intervention.

5. All activities in this area will comply with the institution’s Data Protection Policy and Student Privacy Notice plus the General Data Protection Regulation.

Responsibility

6. Overall responsibility for learning analytics at Gloucestershire is held by the Pro-Vice Chancellor Governance and Student Affairs. Responsibility for relevant areas of activity is allocated as follows:

• The collection of data to be used for learning analytics – Data and Web Team Manager
• The anonymisation or de-identification of data where appropriate – Data and Web Team Manager
• The analytics processes to be performed on the data, and their purposes – Director of Library, Technology and Information Service (LTI)
• The interventions to be carried out based on the analytics – Director of Quality Enhancement
• The retention and stewardship of data used for and generated by learning analytics – Pro-Vice Chancellor Governance and Student Affairs
• Analytics presented to students are intended to help them understand how their learning is progressing, and suggestions may be made as to how they can improve their practices. Students are responsible for assessing how they can best apply any such suggestions to their learning.

Transparency and Consent

7. Students are informed about how their data will be processed when they agree to the relevant Student Contract, Student Charter and associated Student Privacy Notice at enrolment. Data will be collected for learning analytics in compliance with these documents. This information will state the purpose of analytics and the data that will be used for it. It will also mention the involvement of third parties acting as sub-contractors for processing analytics and the rationale for this.
8. The data for learning analytics comes from a variety of sources, including the student record system and the virtual learning environment. The Student Guide to Learning Analytics will clearly specify:

• The data sources being used for learning analytics
• The specific purposes for which learning analytics is being used
• The metrics used, and how the analytics are produced
• Who has access to the analytics, and why
• Guidance on how students can interpret any analytics provided to them
• The interventions that may be taken based on the analytics

9. Students will be asked for their consent for any automated prompts or suggestions to be sent to them, based on the analytics. These may include emails, SMS messages or app notifications.
10. Consent is required from the student for a learning data led intervention (unless it is a legal requirement). This will most likely take the form of responding to an email or other form of direct communication from personal tutor to student.
11. Geolocation data for the purposes of attendance monitoring is not a requirement and the student must be able to opt-out without this impacting on the ability to register attendance.

Confidentiality

12. Personally identifiable data and analytics on an individual student will be provided only to:

• The student
• University staff members who require the data to support students in their professional capacity
• University staff in Planning and Statistics who are working in partnership with the data processors to develop and improve the modelling and to evidence the impact of interventions.
• Third parties who are processing learning analytics data on behalf of the institution. In such circumstances the University will put in place contractual arrangements to ensure that the data is held securely and in compliance with the Data Protection Act and the General Data Protection Regulation.
• Other individuals or organisations to whom the student gives specific consent.

13. University IT staff will have access to systems and data to maintain proper functioning of systems rather than to access any individual’s data.

Sensitive data

14. The Data Protection Act 1998 defines categories of “sensitive data” such as ethnicity or disability. Any use of such data for learning analytics will be fully justified and documented in the Student Guide to Learning Analytics and any project initiation document or similar. These will also reflect any developments on the interpretation of “sensitive data” in the General Data Protection Regulation.

Validity

15. The quality, robustness and validity of the data and analytics processes will be monitored by the University which will use its best endeavours to use learning analytics in line with best practice in the sector, for example ensuring that:

• Inaccuracies and gaps in the data are understood and minimised
• A wide range of data sources are used with the aim of maximising prediction accuracy
• Interpretation of analytics findings are informed by people with relevant
  qualifications and experience. This should help avoid over reliance on single findings, for example.
• Written rational justification is used for the choice of algorithms and metrics used for predictive analytics
• Learning analytics is seen in its wider context, and is combined with other data and approaches as appropriate

Student access to personal data

16. Mechanisms are being developed to enable students 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.
17. Students will also be able to view any metrics derived from their data, and any labels attached to them, though sometimes they may need to request to do so, see 16.
    
18. On occasion it may be considered that access to the analytics may have a negative impact on the student’s academic progress or wellbeing. This may especially be the case when a student’s engagement is less than others in a cohort and they are identified as being “at risk”. Protocols will be developed to ensure that access this type of data is managed sensitively and that human-mediated guidance is available to the student. However, if the student requests it, all their personal data and analytics will be made available to them. Requests would be made via their personal tutor.

Interventions

19. A range of interventions may take place with students, initiated by personal tutors. The types of intervention and what they are intended to achieve are documented in the Student Guide to Learning Analytics. These may include:

• Prompts or suggestions sent automatically to the student via email, SMS message or mobile app notification (subject to the student’s consent)
• Staff contacting an individual based on the analytics if it is considered that the student may benefit from additional support

20. Interventions, whether automated or human-mediated, will normally be recorded. The records will be subject to periodic reviews as to their appropriateness and effectiveness.
21. Metrics derived from data sources input into the learning analytics system will not be used for assessment purposes. However, some of the original data sources owned by the University may be used separately for assessment purposes and for monitoring of attendance, outside of the learning analytics system. The student will have been informed of this separately.

Minimising adverse impacts

22. The University recognises that learning analytics cannot present a complete picture of a student’s learning, and that predictive indicators may not always be fully accurate.
23. Students will retain autonomy in decision making relating to their learning; the analytics are provided to help inform their own decisions about how and what to learn.

Derived from:

Jisc Model Institutional Learning Analytics Policy by Niall Sclater, Nov 2016
Draft v0.1 available from the JISC website

Student Guide to Learning Analytics and Attendance Monitoring