Jisc learning analytics account puts student conversations at the centre

Updated Sep 13, 2026

Jisc published a learning analytics account of practice at Leeds Trinity University on 7 September 2026, describing how engagement data helps staff start supportive conversations. For Student Experience teams, it prompts a practical question: how will students explain their circumstances and evaluate the support that follows?

What Jisc's learning analytics account describes

The account follows contact before registration, welcome activities and attendance during teaching. It says staff combine digital engagement data with qualitative information held in the system. Jisc presents students' circumstances as essential context for deciding how to respond.

"None of these signs gives you the full story."

The September publication describes existing practice at Leeds Trinity. It does not announce a new rollout. The student-conversation theme also connects with our earlier coverage of Newcastle's use of analytics in wellbeing appointments.

What this means for institutions

Jisc's code of practice for learning analytics, updated on 9 September 2026, recommends consulting student representatives throughout design, rollout and monitoring. It also calls for explanations of data use and access, validation of analytics, and review of interventions. These are useful reference points for planning a local review with students.

Our recommendation is to walk through a support contact from the student's perspective. Check whether the message explains why contact was made, whether the student can correct the information, and whether the offered help addresses their concern. Agree who owns the response when several services are involved. A completed referral should lead to a separate question about whether the student received useful help.

Build that question into the evaluation. For example, invite comments on unclear messages, repeated explanations and unresolved referrals, alongside accounts of helpful support. Record the invitation method and who responded. Students who reply to an evaluation may have different experiences from those who decline, so report whose views the findings describe.

How student feedback analysis connects

For a service review, we suggest grouping open-text comments by the part of the support process they concern: initial contact, explanation, referral or follow-up. Preserve mixed experiences, such as a helpful adviser alongside confusing instructions. Keep the original question and collection context visible; our open-text analysis methodology explains the limits of interpreting comment frequencies and changes over time.

Agree the purpose of each dataset before combining evidence. Jisc's code warns about re-identification across data sources and says AI analysis of student content should fit its collection purpose. For a local evaluation, keep anonymous survey themes separate from named support casework. The student comment governance checklist provides questions about access, redaction, checking and reporting. Comment themes can identify aspects of a service to investigate; they cannot establish that an intervention prevented withdrawal.

FAQ

Q: What should a university review now?

A: Start with a defined support pathway. Review its contact messages with students, assign responsibility for unresolved referrals and plan how to collect feedback on the help received. Check interpretations with staff who understand the service before recommending changes.

Q: Does this introduce a national timetable or survey requirement?

A: No. The Leeds Trinity example concerns local practice. Jisc's separate code addresses UK educational institutions; neither source announces a change to national student surveys.

Q: How should student voice shape learning analytics?

A: Involve students in deciding what the process is for and how its effects will be reviewed. Jisc's code also supports a route to question or correct AI-generated interpretations. Feedback about an intervention is evidence about students' reported experience, with separate evaluation needed for claims about continuation.

References

[Jisc]: "Five signs that can help you spot student disengagement early" Published: 2026-09-07

[Jisc]: "Code of practice for learning analytics" Published: 2015-06-04; updated: 2026-09-09