Comparing medical students' learning via paper-based versus electronic curriculum
Bibliographic record
Abstract
Medical schools are increasingly utilizing electronic curriculum management systems (CMS) for scheduling, delivery, administration and reporting. Little attention has been paid to comparing electronic systems to the paper-based curricula. Previous research showed that the quality of education delivery does not depend on the medium used. Current research in electronic curriculum delivery focuses mostly on the specific features that the system has to offer. When moving from a paper-based curriculum to the digital variant, we must assure that our primary goals of student learning are not lost. During implementation of electronic Curriculum Management System-OPAL, developed at the University of Manitoba; we compared students' perception of learning and actual learning, as we transitioned from the paper-based (Class of 2012) to electronic-based (Class of 2013) curriculum. Evaluation of student's perception via a 60 questionnaire course evaluation survey of three courses in Block III showed better satisfaction with the electronic system. On a 5 point Likert scale, where 1 = least satisfaction and 5 = most satisfaction; for the class of 2012 (Paper) overall percentage scores were: 0%, 2%, 17%, 34%, 47% vs. 0%, 0%, 5%, 38% 57% for the class of 2013 (Electronic), p<0.01. Performance at the final course examination demonstrated similar performance between the two classes. Average student examination scores for the classes of 2012 and 2013 respectively were: Overall 75.95±6.13 vs. 75.40±7.14 (p=0.54); Cardiovascular 76.78±8.86 vs. 77.20±8.45 (p=0.716); Respiratory 77.38±9.25 vs. 74.55±9.14 (p=0.024); ENT 62.95±15.94 vs. 72.63±13.13 (p<0.001). Our project demonstrated that despite better student perception of the electronic compared to the paper-based curriculum, students' actual learning is equivalent.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".