The Digital Voice of Students: Analysing Emerging Themes in a Corpus of University Reviews
Bibliographic record
Abstract
For higher educational institutions, reviews on Google Maps offer a free and easily accessible platform for students to express their needs and voice their concerns voluntarily. Such a platform can be valuable for capturing a wide range of academic and non-academic aspects that may not be manifested via traditional feedback instruments such as surveys. Accordingly, this study explores the themes that emerge in a corpus of online reviews collected from Google Maps of 29 Saudi public universities while also highlighting how such reviews form a public discourse shaped by the medium itself. To this end, this research employed an NLP advanced technique, namely BERT, to categorise the reviews into themes based on their semantic similarity. Then, a thematic analysis was conducted to reveal six main recurrent themes in the corpus: location and accessibility, facilities and infrastructure, academic quality and teaching, student support services, religious sentiment and gratitude, and community and social environment. The findings indicate that while Google Maps reviews capture a number of cultural and social aspects of university life, they inherently foreground physical (e.g., amenities) and logistical (e.g., location) dimensions. The paper also demonstrates how online reviews provide students with a platform to raise issues related to both micro-level (e.g., specific courses, amenities) and macro-level concerns (e.g., inclusivity), hence their potential value in contributing to the evaluative discourse on higher education institutions. These results suggest the need for a systematic engagement with online feedback platforms to promote continuous institutional improvement and cultivate an inclusive, student-centred educational environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".