1. We use learning analytics to help students understand their engagement and to help staff offer timely support. We do not use learning analytics for assessment or discipline. We do not take significant action about an individual without human involvement. This policy explains our purposes, what data we use, who can see it, and your choices. See also our Student Privacy Notice and Data Protection Policy. <\/p>\n\n\n\n
2. This policy covers the Jisc Learning Analytics service, attendance recording, and the student app. It applies to all taught students and the staff who support them. <\/p>\n\n\n\n
3. 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 University of Gloucestershire: Each student has a Personal Tutor*, a critical component in monitoring their progress and providing support and encouragement through their learning journey. 4. Students have a responsibility to engage with the learning opportunities provided by their course. These expectations and the University\u2019s approach to Personal Tutor interventions are set out in the Engagement and Attendance Policy. <\/p>\n\n\n\n 5. The University will ensure that learning analytics is deployed for the benefit of students and follow the following statement of principles:<\/p>\n\n\n\n a) We will use\u00a0learning\u00a0analytics to help all students reach their full academic potential.\u00a0 6. All activities in this area will comply with the institution\u2019s Data Protection Policy and Student Privacy Notice plus UK Data Protection legislation.<\/p>\n\n\n\n 7. Overall responsibility for learning analytics at University of Gloucestershire is held by the Chief Operating Officer. <\/p>\n\n\n\n 8. 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. Any questions about learning analytics should be directed to Student Centres. <\/p>\n\n\n\n 9. Students are informed about how their data will be processed when they agree to the relevant Student Contract and associated Student Privacy Notice at enrolment. Data will be collected for learning analytics in compliance with these documents. <\/p>\n\n\n\n 10. The data for learning analytics comes from a variety of sources, including the Student Records system and the Virtual Learning Environment. <\/p>\n\n\n\n 11. Personally identifiable data and analytics on an individual student will be provided only to: <\/p>\n\n\n\n a) The student. 12. University IT staff will have access to systems and data to maintain proper functioning of systems rather than to access any individual\u2019s data. <\/p>\n\n\n\n 13. UK Data Protection legislation defines categories of \u201csensitive data\u201d 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 Attendance and any project initiation document or similar. These will also reflect any developments on the interpretation of \u201csensitive data\u201d in UK Data Protection legislation. <\/p>\n\n\n\n 14. 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: <\/p>\n\n\n\n a) Inaccuracies and gaps in the data are understood and minimised.\u00a0 15. Students have the right to correct any inaccurate personal data held about themselves. <\/p>\n\n\n\n 16. 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. <\/p>\n\n\n\n 17. A range of interventions may take place with students. These may include: <\/p>\n\n\n\n a) Prompts or suggestions sent automatically to the student via email, SMS\u00a0message\u00a0or\u00a0Jisc\u00a0mobile app notification. 18. Interventions, whether automated or by University staff, will normally be recorded within the Student Records system. <\/p>\n\n\n\n 19. Metrics from learning analytics are not used for summative assessment. <\/p>\n\n\n\n
*All taught students have a Personal Tutor, or equivalent,<\/em>\u202ffor 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.\u202f<\/em><\/p>\n\n\n\n
b) We will be transparent about data collection, sharing,\u00a0consent\u00a0and responsibilities.
c) We will abide by ethical principles and align with our\u00a0University\u00a0strategies,\u00a0policies\u00a0and values.\u00a0\u00a0
d) Learning\u00a0analytics will not be used to inform significant action at an individual level without human intervention.<\/p>\n\n\n\nResponsibility<\/h2>\n\n\n\n
Transparency and Consent<\/h2>\n\n\n\n
Confidentiality<\/h2>\n\n\n\n
b) University staff members who require the data to support students in their professional capacity.
c) University staff in\u00a0Data and Insights\u00a0who are working in partnership with the data processors to develop and improve the modelling and to\u00a0evidence\u00a0the impact of interventions.
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d) Third parties who are processing learning analytics data on behalf of the institution. In such circumstances,\u00a0the University will put in place contractual arrangements to ensure that the data is held securely and in compliance with\u00a0UK Data Protection legislation.\u00a0
e) Other individuals or organisations to whom the student gives specific consent.<\/p>\n\n\n\nSensitive data<\/h2>\n\n\n\n
Validity<\/h2>\n\n\n\n
b) Interpretation of analytics findings are informed by people with relevant\u00a0qualifications and experience. This should help avoid overreliance on single findings, for example.
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c) Learning analytics is seen in its wider\u00a0context, and\u00a0is combined with other data and approaches as\u00a0appropriate.\u00a0<\/p>\n\n\n\nStudent access to personal data<\/h2>\n\n\n\n
Interventions<\/h2>\n\n\n\n
b) An invitation to talk. Staff use analytics to inform conversations, not to make assumptions.\u00a0<\/p>\n\n\n\n