Factors Affecting Student Engagement: A Case Study Examining Two Cohorts of Students Attending a Post-1992 University in the United Kingdom
Bibliographic record
Abstract
Issues relating to student retention and student engagement remain high on the agendas of higher education institutions worldwide. This case study considers the factors that impact on student engagement within a sample of first year undergraduate sports students attending a post 1992 university in the West Midlands region of the United Kingdom. These participants had started their three-year degree courses at the beginning of either the 2011/12 or the 2012/13 academic years. It should be noted that this meant that data collection straddled the introduction of higher student fees for students attending English universities. Data for the study were collected using a quantitative questionnaire and follow up focus groups. Although a number of different factors were found to encourage student engagement the quality of student relationships with their teachers was found to be the most important. Although it was beyond the scope of this study to draw definite conclusions about the impact of higher student fees in this area our data did suggest the possibility that these higher fees might result in staff-student relationships becoming even more important in encouraging student engagement. It is recommended that future research examines the impact of student fees in this area in more detail.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".