Learning for Earning: Student Expectations and Perceptions of University
Bibliographic record
Abstract
In the context of increasing numbers of students enrolling in higher education in the last decade, understanding student expectations of their universities becomes more important. Universities need to know what students expect if they want to keep them satisfied and continue attracting them. On the other hand, it is also important to know whether student expectations are in line with the purpose of the universities and the causes they serve. This research explores students’ expectations and perceptions of the university in post-Soviet Georgia, as well as whether these expectations are in line with the perspectives of university administrators. For the purposes of this research, over 800 bachelor level students of different academic programs were surveyed at five big public universities across Georgia. Additionally, 10 in-depth interviews were conducted with university administrators to learn about the purpose that public universities try to serve and to understand their perspectives on what should be expected of university. After the analysis of the results, two focus groups were conducted with the students in Western and Eastern Georgia to make sense of the findings obtained through the student survey. Finally, 4 in-depth interviews were conducted with experts to understand their perspectives on the actual findings of this research. The results suggest that employment is the main expectation from a university education. Moreover, there is a mismatch between what students identify as their primary expectation and what administrators believe students should expect. Significance and implications of these results for universities are discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".