Cost and quality shape student attendance
Updated Aug 16, 2026
An empty lecture theatre does not tell a university why students stayed away. It can reflect a journey they cannot afford, a class they do not expect to help, or several pressures acting at once. Tom Lowe, Conor Naughton, Tania Struetzel, Rebecca Adams, Jessica Walker, Philip Kynaston and Colum Mackey's Active Learning in Higher Education paper, "Why Are Students Not Attending In-Person Classes Post-COVID-19? An Explorative Study in Student Engagement", matters because it asks elected student representatives rather than treating absence as a simple lack of commitment. For UK universities using student voice to understand engagement, the paper makes a useful distinction: attendance is an outcome, but students' reasons are the evidence teams need to act.
Context and research question
In-person attendance has fallen across UK and global higher education since COVID-19. Several explanations compete for attention, including a preference for online learning, the cost-of-living crisis, and worsening mental health. Existing evidence on why students choose online or on-campus participation also shows that flexibility and social presence can pull in different directions. The practical question is therefore not simply how to increase attendance. It is what students are weighing when they decide whether a particular journey and class are worthwhile.
The authors explored that question with elected programme-level Student Academic Representatives from three UK post-92 institutions. This gives the study direct access to students who hear concerns across their programmes, while also setting an important boundary around the evidence: representatives can surface recurring explanations, but they are not a statistically representative attendance survey. For Student Experience and Market Insights teams, the study is best used as a framework for asking better local questions about attendance.
Key findings
The cost-of-living crisis materially shapes whether students attend in person. When coming to campus has to compete with a limited student budget, attendance becomes a financial decision as well as an academic one. That matters for universities because a student can value their course and still decide that another trip is unaffordable. Attendance data alone may therefore misclassify financial constraint as weak motivation.
Teaching quality and content remain central to the decision. The paper does not present falling attendance as something universities can explain entirely through external pressures. Students also judge whether the session offers enough value to justify the time and cost of being there. The abstract puts the point plainly:
"teaching quality and content remain at the heart of student decision-making"
This finding gives institutions a genuine lever. Universities cannot remove every financial pressure, but they can make the purpose of in-person teaching clearer, design sessions that use presence well, and act when comments repeatedly describe low-value contact time.
Attendance has several possible causes, so one headline explanation is unlikely to be enough. The study begins with competing accounts involving online preference, living costs, and mental health, then centres cost and teaching in its reported findings. For institutional teams, the lesson is methodological as much as practical: do not ask only whether students attended. Ask what made attendance possible, difficult, or worthwhile on that course.
Student representation can reveal the reasoning hidden behind an attendance rate. By foregrounding programme-level representatives' accounts, the research treats students as interpreters of the problem rather than targets of an attendance intervention. This matters because post-pandemic flexibility can widen access while weakening belonging, and the same policy may help one group while creating a thinner experience for another. Listening across programmes helps institutions identify where a sector-wide pattern has a local cause.
Practical implications
First, universities should separate barriers from judgements about value when they collect attendance feedback. A short pulse survey could ask about travel cost, paid work, caring responsibilities, timetable gaps, health, recordings, teaching format, and the expected value of the session. One open question, such as "What most influenced your decision to attend or not attend this week?", can reveal combinations that a fixed-response item misses. This gives teams an actionable diagnosis instead of a generic engagement problem.
Second, institutions should pair attendance records with open-text evidence. Compare comments by programme, year, commuting pattern, and demographic group, while protecting privacy and avoiding punitive interpretation. A documented NSS open-text analysis methodology offers a useful model for grouping themes consistently and retaining traceability to source comments. Student Voice Analytics can support that process at scale, helping teams distinguish recurring cost, timetable, teaching, and wellbeing signals before deciding what to change.
Third, programme teams should make in-person value explicit and then test whether students experience it. If a session is designed around discussion, practice, feedback, specialist facilities, or peer learning, explain that purpose before students make the journey. Follow up with representatives and open comments to see whether delivery matched the promise. That creates a tighter evidence loop between course design and attendance decisions.
Finally, institutions should avoid using attendance mandates as the first response. A mandate may lift the recorded rate without resolving cost pressure, weak session design, or a timetable that makes one hour on campus consume most of a day. Start with the barriers students describe, test targeted changes, and monitor both attendance and experience. This approach is more likely to improve participation without penalising students for conditions the institution could have addressed.
FAQ
Q: How can a university apply these findings to its attendance work?
A: Add a short, repeatable attendance prompt to module or pulse surveys, with one open-text question about the student's most important reason. Analyse responses alongside attendance patterns, then give programme teams a small set of testable issues such as travel cost, timetable gaps, unclear session purpose, or teaching format. Review the same themes after any change so the university can see whether the intervention addressed the stated barrier.
Q: What should institutions keep in mind before generalising from this study?
A: The published abstract identifies elected programme-level representatives from three post-92 universities as the evidence base, but it does not report the participant count or enough procedural detail to estimate prevalence. Representatives may be especially good at surfacing shared concerns, but their accounts should not be treated as a sector-wide distribution of reasons. Universities should use the findings to design local enquiry, then validate the pattern with their own students and attendance data.
Q: What does this change about student voice practice more broadly?
A: It shows why behavioural indicators need interpretation. Attendance, logins, and room use can show what happened, but student comments explain whether cost, care, teaching quality, confidence, or course design shaped the behaviour. When universities combine those forms of evidence, student voice becomes a way to diagnose institutional conditions rather than label students as engaged or disengaged.
References
[Paper Source]: Tom Lowe, Conor Naughton, Tania Struetzel, Rebecca Adams, Jessica Walker, Philip Kynaston and Colum Mackey "Why Are Students Not Attending In-Person Classes Post-COVID-19? An Explorative Study in Student Engagement" DOI: 10.1177/14697874251366213
Request a walkthrough
Book a free Student Voice Analytics demo
See all-comment coverage, sector benchmarks, and reporting designed for OfS quality and NSS requirements.
- All-comment coverage with HE-tuned taxonomy and sentiment.
- Versioned outputs with TEF-ready reporting.
- Benchmarks and BI-ready exports for boards and Senate.
UK-hosted · No public LLM APIs · Same-day turnaround