How well do computer science students communicate with supervisors, lecturers and tutors?
Broadly positive but uneven across cohorts: in National Student Survey (NSS, the UK‑wide final‑year student survey) open‑text for the communication with supervisor, lecturer, tutor theme, there are 6,373 comments and a sentiment index of +5.5. Computing within this theme reads slightly higher at +7.1. Looking at computer science through the sector’s Common Aggregation Hierarchy used for benchmarking, students focus as much on assessment clarity as on contact itself: Feedback accounts for 8.5% of comments and sentiment around Marking criteria sits at −47.6, so predictable availability and actionable guidance matter more than message volume.
Effective communication between computer science students and their academic mentors shapes learning in modules heavy with complex concepts and fast‑moving tools. Students respond to both accessible people and reliable structures: defined office hours, predictable response times, and a blend of in‑person and digital channels. While face‑to‑face discussions support deep problem‑solving and assessment interpretation, many students prefer digital routes for speed and accessibility. Departments that monitor response‑time compliance, confirm adjustments in writing, and offer short check‑ins at assessment peaks reduce anxiety and support attainment.
What should supervisor engagement look like?
Engagement with supervisors should prioritise meaningful, structured interactions that build students’ academic progress. Accessible supervisors who provide timely, relevant feedback and use meetings to promote critical thinking and real‑world application make the biggest difference. Combine scheduled face‑to‑face time for complex issues with digital check‑ins for quick clarifications. Publish office hours, set a reply‑within‑X‑working‑days norm at programme level, and name back‑up contacts during leave. Short, proactive check‑ins at assessment pinch points particularly help disabled and mature students, who often face higher barriers to contact.
What does lecturer accessibility require?
Students value lecturers who are responsive and willing to discuss intricate theories and fast‑changing technologies. Effective practice blends virtual office hours, discussion forums and brief recorded updates with in‑person opportunities for deeper exploration. Standardise expectations for response times, and route different query types to the right place (e.g. VLE forum for routine questions, email for personal matters). Close the loop by summarising actions and common Q&A on the VLE so cohorts can self‑serve and staff can spot recurring issues to adjust teaching promptly.
How do tutors best support problem‑solving?
Tutors bridge theory and practice by guiding students through coding challenges while fostering independent problem‑solving. Calibrate support: use worked examples and targeted hints where needed, and step back to encourage students to test, debug and reason. Offer approachable drop‑ins and small‑group clinics, vary methods for different learning preferences, and escalate patterns of misunderstanding to module leaders so assessment briefs and marking criteria are clarified early. Tracking missed responses and follow‑ups helps ensure no student stalls between sessions.
Which communication channels work best?
Use channels deliberately. Keep email for formal, documented exchanges; run VLE forums for routine technical questions; provide virtual office hours for real‑time help; and retain face‑to‑face sessions for complex topics and assessment discussions. Maintain a single source of truth on the VLE where decisions, deadlines and changes are summarised after meetings. For time‑poor cohorts such as part‑time learners and those on placements or apprenticeships, provide predictable, asynchronous updates and some out‑of‑hours slots.
What feedback on assignments do computer science students need?
Timely, specific, and actionable feedback enables students to iterate effectively. Given the persistent concerns in computer science about assessment clarity, prioritise annotated exemplars, checklist‑style rubrics and explicit marking criteria alongside realistic turnaround times and feed‑forward guidance. Feedback should highlight strengths as well as targeted improvements, mapping comments to the marking criteria students will meet again in later modules. Where feasible, use brief feedback surgeries so students can query points before the next submission.
How do we balance autonomous learning with guidance?
Set structured independence. Define what autonomy looks like in each module, give choice within assessment briefs, and make support routes transparent. Use midpoint check‑ins and light‑touch pulse surveys to surface where students need more guidance, then adjust seminars, labs or resources accordingly. This adaptive approach preserves exploration while keeping students aligned to programme outcomes.
What should we improve next?
- Set programme‑wide service standards for academic communication: channels by query type, a simple reply‑within‑X‑working‑days norm, visible office hours and named back‑ups.
- Fit communication to time‑poor cohorts with weekly digests, recorded briefings and some out‑of‑hours options; summarise key decisions in one VLE location.
- Reduce barriers for disabled and mature students by offering alternative modes (captioned recordings, written summaries) and confirming adjustments in writing; schedule short proactive check‑ins at key assessment points.
- Stabilise delivery and assessment clarity: name an owner for scheduling and course communications, and publish exemplars, rubrics and turnaround expectations to address weak sentiment on feedback and marking criteria.
- Measure and learn fast: track response‑time compliance and common communication issues by cohort; review at programme meetings and act within the next teaching block.
How Student Voice Analytics helps you
- See topic and sentiment for this communication theme over time, with drill‑downs by school, programme, cohort and site.
- Compare like‑for‑like across computer science and other discipline groupings, and by demographics (age, domicile, mode, disability, commuter status).
- Get concise, anonymised summaries that highlight what to fix now and what to scale, with export‑ready outputs for programme boards, TEF and quality reviews.
- Evidence progress by tracking response‑time compliance, issue volumes and assessment‑related sentiment shift across teaching blocks.
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