Effectiveness of an Electronic Histology Tutorial for First‐Year Dental Students and Improvement in “Normalized” Test Scores
Bibliographic record
Abstract
The effectiveness of an electronic histology tutorial (EHT) as a mode of learning was assessed by comparing performance on two term tests for an EHT class of sixty-nine students and five prior classes (n=347) who learned by traditional methods. The aims of this study were to 1) develop and introduce a self-instructional, computer-aided approach to guide student learning in the first-year histology course at the University of Toronto Faculty of Dentistry; 2) evaluate the effectiveness of the self-study electronic histology tutorial by comparing students' test scores for the EHT group to students' scores in previous years; and 3) evaluate students' acceptance of this novel mode of learning by means of a satisfaction questionnaire. The EHT group performed significantly better on both the general histology and oral histology term tests than the five prior control years (p<0.001), yet there were no significant differences in overall GPA between the groups, suggesting that the improvement was specific to the EHT/histology course grades (p=0.1 to 0.47). A statistically significant improvement in performance per unit overall GPA was noted in the test group, which demonstrated an increase in this test score normalized ratio (TSNR) of 3-18 percent in the general histology term test and 7-21 percent in the oral histology term test over the control groups. In addition to determining the effects of the EHT on grade performance, this study sought to evaluate students' acceptance of this alternative mode of learning in comparison to the standard teaching model by means of a satisfaction questionnaire. Overall, students' responses to the questionnaire were positive with an overall mean level of agreement for all ten responses of 4.5 out of 5 (90 percent).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".