Enhancing learning in an online oral epidemiology and statistics course.
Bibliographic record
Abstract
Background: Students in the Faculty of Dentistry at the University of British Columbia have articulated challenges in understanding learning objectives in their oral epidemiology and statistics course. This study aimed to measure the impact of a course renewal intended to enhance student learning. Examples of educational interventions included providing more time for activities, increasing student interactivity, and integrating more hands-on applicable exercises using statistical software. Methods: An online mixed-methods survey using a 5-point Likert scale and open-ended questions was distributed to 43 dental hygiene students before the course renewal and again to a second cohort of 43 students after course revisions. The survey asked students to rank their levels of challenge and self-confidence in learning 23 of the course objectives throughout each academic year. Four semi-structured interviews were also conducted with faculty and staff members involved in teaching or coordinating this course to understand their experiences after the course revisions. Results: < 0.001).The changes on the challenge and confidence scores in the degree-completion cohort were not statistically significant (23% vs. 24% and 31% vs. 36%, respectively). Student satisfaction levels increased in all 6 categories measured. Conclusion: Providing students with more time to absorb their learning, increasing interactivity, offering timely feedback, and integrating applicable exercises using statistical software resulted in an enhanced learning environment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".