Exploring Student Attitudes to Directed Self-Learning Online through Evaluation of an Internet-Based Biomolecular Sciences Resource
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: In 2000, funding was awarded by the University of Glasgow's Learning and Teaching Development Fund (L&TDF) for the authors to develop an interactive, online learning resource for veterinary biomolecular sciences teaching. This course is a core component of the veterinary undergraduate curriculum at the university. Evaluations were carried out to gauge students' experiences of using the resource as a basis for exploring students' attitudes toward online, independent learning. METHODOLOGY: Peers were asked to review the design and content of four modules, also evaluated by students using questionnaires and focus group discussions. Additionally, students were observed using the modules. Both first-year students and second-year direct-entry students (i.e., students entering the veterinary program with advanced training) participated in the evaluation, which allowed for some comparison between the groups. One cohort used the modules independently, and their responses were compared with the cohorts that used the modules in scheduled classes. RESULTS AND CONCLUSIONS: The evaluations indicate that this is a useful resource that could act as a template for other courses within the veterinary undergraduate curriculum, particularly for learning of basic sciences. On average, first-year and timetabled students rated the program more highly overall, rated the program more highly in relation to previous instruction, and rated tutor presence as more important than second-year direct-entry and independent students did. The lower rating given to tutor presence by second-year direct-entry and independent students indicates that they are more confident using the modules without tutor supervision.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".