Quantitative and Qualitative Analysis of Student Feedback on ePortfolio Learning
Bibliographic record
Abstract
At the University of British Columbia, we introduced an ePortfolio assignment in the operative dentistry clinical simulation module and conducted a pilot study to explore the usefulness of ePortfolios as a learning tool for dental students. Qualitative assessments included student self-reflections on the ePortfolio experience. In the quantitative evaluation, ePortfolio learning was hypothesized as a multidimensional experience with four dimensions: 1) an ePortfolio experience is valuable for learning operative dentistry; 2) an ePortfolio is time-consuming, but overall a useful experience; 3) ePortfolio learning requires technical skills that are manageable; and 4) ePortfolio experience may be beneficial for lifelong learning. Overall, both quantitative and qualitative assessments demonstrated that students valued ePortfolio learning as a positive experience. In multivariate analyses (confirmatory factor analysis), the four-dimensional model of ePortfolio learning was confirmed. Future studies are needed to validate or revise the four-factor model of ePortfolio learning in different student cohorts.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".