Advance HE's pre-arrival questionnaire case study shows how disclosure gaps can hide students from support

Updated Sep 07, 2026

student supportstudent voice

Early student voice is less useful if the students who disclose a need never make it into the datasets used for support. On 10 June 2026, Advance HE published Who are we missing?, a University of East London case study showing how pre-arrival questionnaire data exposed disclosure gaps in institutional records for disability, care-experienced status, and estrangement. For teams working on student voice in higher education, that matters because a missing record is not just a data issue. It can become a missed support offer, a distorted access and participation picture, and weaker evidence about what students need before teaching even starts.

What has changed in pre-arrival questionnaire use

This is not a new national survey requirement. It is a June 2026 institutional case study showing what happens when universities match pre-arrival survey data to named student records with consent. Advance HE says UEL has used the Pre-arrival Academic Questionnaire since 2021, but the first national pilot allowed entrants to provide student IDs and consent so teams could compare responses with institutional systems and act earlier. That builds on the wider pre-arrival questionnaire pilot findings, but moves from a sector-wide signal to an operational test.

UEL focused on three access and participation plan priority groups that depend on self-disclosure: disability, estrangement and care experience. Staff removed duplicate responses and invalid or missing IDs, matched responses with institutional records, and used descriptive analysis in Power BI.

The authors reported differences between survey disclosure and institutional recording, alongside substantial missing administrative data. They explicitly distinguish comparisons of overall proportions from mismatches at individual-student level. They also retained "prefer not to say" separately from unknown or unrecorded responses.

"some students may be overlooked due to data gaps"

The published percentage breakdowns need clarification before they are used as benchmarks. For example, the source gives home-student disability rates of 29.1% and 19.3%, but labels their difference as 19.8 percentage points; those displayed rates differ by 9.8 points. Its overall, subgroup and individual-match figures also cannot be reconciled from the information provided. We have therefore removed the original numerical comparison from this briefing rather than infer corrected denominators or rates.

The supported practical finding is narrower: this early-stage institutional analysis identified possible gaps worth investigating. It does not establish the prevalence of disability or support need across UEL, demonstrate that the survey is a definitive record, or show that an intervention improved outcomes. The authors said actions were still being explored, including asking about disclosure, reviewing data collection and considering earlier signposting.

What this means for institutions

The main lesson is not simply to collect more survey data. It is to test whether survey, admissions, and student support systems agree about who students are. Where self-disclosed identities appear in pre-arrival or early-term surveys but not in operational records, teams should review the wording of disclosure questions, consent routes, matching processes, and staff handoffs. Universities often focus on response rates when they review student feedback systems. This case study is a reminder that capture quality matters just as much.

It also shows why institutions should avoid treating "prefer not to say" and "not recorded" as the same thing. The authors discuss possible differences by fee status, study level and entry route, but the numerical breakdown requires clarification before those groups can be ranked reliably. That makes this a segmentation problem as much as a data problem. As our post on student survey benchmarking and triangulation argues, evidence gets more useful when institutions compare survey signals with administrative records, continuation data, and service take-up rather than reading each source alone.

The practical next step is early action. If pre-arrival data suggests students are worried about disclosure, or reveals groups that institutional systems rarely capture well, the response should sit inside induction, signposting, and support workflows, not in a retrospective dashboard alone. The intended benefit is earlier, more appropriate support; whether that happens needs local evaluation.

How student feedback analysis connects

This matters for comment analysis because subgroup evidence is only as reliable as the identifiers behind it. If disability, care-experienced status, or estrangement are inconsistently recorded, universities will struggle to segment open-text feedback credibly across pre-arrival surveys, induction pulses, module evaluations, and national surveys. That does not make qualitative evidence less useful. It means the governance around identity data has to be tighter before institutions can trust the patterns they see.

A governed workflow such as Student Voice Analytics becomes more useful once teams want to compare what students disclose before arrival with what they later say in comments about support, belonging, assessment, or communication. The real value is not another dashboard. It is a clearer trail from early disclosure, to later feedback, to action on the groups most at risk of being missed.

FAQ

Q: What should institutions do now if they want to act on this case study?

A: Start by comparing pre-arrival, induction, and early-term survey data with the student characteristics held in your institutional systems for the groups you most need to support. If the records do not line up, review question wording, consent, matching, and handoff processes before assuming the problem is only low disclosure.

Q: What is the timeline and scope of this change?

A: Advance HE published the case study on 10 June 2026. It describes work at the University of East London, which has used the Pre-arrival Academic Questionnaire since 2021 and used the first national pilot model to match responses to student records with consent. The case study is institution-specific, but the data quality issues it highlights are relevant across UK higher education.

Q: What is the broader implication for student voice?

A: Student voice evidence is not only about asking students better questions. It is also about whether institutions can identify, segment, and act on what students say in a reliable way. If key groups disappear between survey response and institutional record, the evidence base will look tidier than the student experience really is.

References

[Advance HE]: "Who are we missing?" Published: 2026-06-10

[Advance HE]: "Pre-arrival questionnaire (PAQ) national pilot wave 1 initial results" Date note: the current page header says 2026-04-15; its body says the report was published on 2026-04-16.

Corrected 7 September 2026: removed disclosure percentages whose displayed totals and subgroup figures could not be reconciled, distinguished aggregate rates from individual record mismatches, and made the study's early-stage limitations explicit. Recorded conflicting dates on the linked pilot announcement.