Jisc: building a business case for learning analytics, keeping student feedback in the loop

By Student Voice AI

Updated Apr 03, 2026

Learning analytics programmes rarely fail because a dashboard is missing. They stall when students and staff do not trust how the data will be used, or cannot see how feedback improves support. That is why Jisc’s 27 February 2026 guidance on the business case for learning analytics matters. It focuses on stakeholder engagement and long-term support, but the deeper lesson is broader: institutions need student feedback in the loop if they want learning analytics interventions to stay credible, explainable, and useful over time. [Jisc blog post]

What has changed in Jisc’s business case for learning analytics guidance

Jisc’s blog is the second part of a short series on building a business case for learning analytics. This instalment is less about the technical platform and more about adoption: how you take a pilot into business-as-usual without losing buy-in, quality, or safeguards. That focus is useful because it shifts attention from procurement to the harder question: how do you make the programme credible enough to last?

For institutions, the key shift is the emphasis on learning analytics as a change programme. Jisc highlights the need to plan for staff capacity, role-based training, and communication that builds confidence rather than suspicion. It also makes explicit that implementation should include an improvement loop, not a one-off launch. That is the difference between a promising pilot and a dashboard that quietly loses support after launch.

"Gather staff and student feedback, refine thresholds and workflows, and scale in phases"

Jisc also flags governance considerations that will feel familiar to student experience and quality teams: transparency with students about what data is collected and why, involvement of student representatives, and clear safeguards to manage bias, false positives, and the risk of over-alerting. Those points matter because they reduce the risk that a well-meant analytics programme feels opaque or intrusive to the students it is supposed to support, and they align closely with Glasgow's student feedback governance framework.

What this means for institutions

If you are building, or refreshing, a learning analytics business case, treat student feedback as part of the core evidence, not an add-on. Quantitative indicators can tell you where engagement or continuation risks may sit, but student voice evidence tells you why. That helps teams choose interventions that students are more likely to trust, use, and benefit from.

Practically, Jisc’s guidance translates into a few immediate actions:

Make the pilot measurable, and include feedback loops on the intervention itself, not only on the dashboard.
Agree what “good” looks like operationally: who triages alerts, what the response time is, and what escalation routes exist.
Communicate clearly with students about purpose, benefits, and safeguards, and involve student representatives early enough to shape the rollout, because students and staff should help design evaluation surveys.

For teams already doing large-scale comment analysis, the overlap is strong. The same governance disciplines apply, including repeatability, privacy controls, and being able to explain method choices to panels and committees. That makes the business case easier to defend because the intervention model, evidence base, and review process all line up, especially when teams benchmark and triangulate survey evidence rather than relying on a single signal. If you need a lightweight starting point, see our student comment analysis governance checklist. For a reminder of why versioning and data refresh matter in governance packs, see OfS TEF dashboard corrections.

How student feedback analysis connects

Learning analytics initiatives work best when they are paired with open-text feedback. Free-text comments from NSS, PTES, PRES, pulse surveys used for earlier term-time insight, and module evaluations help teams interpret what a metric cannot: whether students experience an intervention as supportive, confusing, or intrusive. That context helps institutions distinguish a genuine support gap from a thresholding problem, a communication failure, or a process issue.

If you are scaling learning analytics, build a repeatable open-text workflow alongside it so you can track what changes in student voice as you adjust thresholds and support models. Student Voice Analytics gives teams a reproducible way to analyse those comment streams across surveys, so you can connect risk signals to student experience evidence and act with more confidence. For a practical starting point, see our NSS open-text analysis methodology and student comment analysis governance checklist.

FAQ

Q: What should we do now to build a credible learning analytics business case?

A: Start with a scoped pilot and define the operational model before you scale. Include staff and student feedback in the pilot evaluation, and document how thresholds, workflows, and safeguards will be reviewed over time. That gives you a clearer case for scaling because you can show not just that the system works, but that the intervention is workable and trusted.

Q: Is Jisc’s guidance mandatory, and who does it apply to?

A: No. This is non-regulatory guidance published by Jisc on 27 February 2026. It is written for UK universities and colleges considering learning analytics, but the adoption and trust principles apply more widely.

Q: What is the biggest student voice risk in learning analytics programmes?

A: Treating learning analytics as purely technical. If students are not informed and involved, and if feedback on the intervention is not captured, institutions can undermine trust and miss unintended impacts.

References

[Jisc]: "Building a business case for learning analytics: securing stakeholder engagement and ongoing support"
Published: 2026-02-27

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