Factors Influencing Seminar Learning and Academic Achievement
Bibliographic record
Abstract
Many veterinary curricula use seminars, interactive educational group formats in which some 25 students discuss questions and issues relating to course themes. To get indications on how to optimize the seminar learning process for students, we aimed to investigate relationships between factors that seem to be important for the seminar learning process, and to determine how these seminar factors account for differences in students' achievement scores. A 57-item seminar evaluation (USEME) questionnaire was administered to students right after they attended a seminar. In total, 80 seminars distributed over years 1, 2, and 3 of an undergraduate veterinary medicine curriculum were sampled and 988 questionnaires were handed in. Principal factor analysis (PFA) was conducted on 410 questionnaires to examine which items could be grouped together as indicators of the same factor, and to determine correlations between the derived factors. Multilevel regression analysis was performed to explore the effects of these seminar factors and students' prior achievement scores on students' achievement scores. Within the questionnaire, four factors were identified that influence the seminar learning process: teacher performance, seminar content, student preparation, and opportunities for interaction within seminars. Strong correlations were found between teacher performance, seminar content, and group interaction. Prior achievement scores and, to a much lesser extent, the seminar factor group interaction appeared to account for differences in students' achievement scores. The factors resulting from the present study and their relation to the method of assessment should be examined further, for example, in an experimental setup.
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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 | 0.004 | 0.031 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".