Taking Constructivism One Step Further: Post Hoc Analysis of a Student-Created Wiki
Bibliographic record
Abstract
BACKGROUND: Wiki platform use has potential to improve student learning by improving engagement with course material. A student-created wiki was established to serve as a repository of study tools for students in a medical school curriculum. There is a scarcity of information describing student-led creation of wikis in medical education. OBJECTIVE: The aim is to characterize website traffic of a student-created wiki and evaluate student perceptions of usage via a short anonymous online survey. METHODS: Website analytics were used to track visitation statistics to the wiki and a survey was distributed to assess ease of use, interest in contributing to the wiki, and suggestions for improvement. RESULTS: Site traffic data indicated high usage, with a mean of 315 (SD 241) pageviews per day from July 2011 to March 2013 and 74,317 total user sessions. The mean session duration was 1.94 (SD 1.39) minutes. Comparing Fall 2011 to Fall 2012 sessions revealed a large increase in returning visitors (from 12,397 to 20,544, 65.7%) and sessions via mobile devices (831 to 1560, 87.7%). The survey received 164 responses; 88.0% (162/184) were aware of the wiki at the time of the survey. On average, respondents felt that the wiki was more useful in the preclinical years (mean 2.73, SD 1.25) than in the clinical years (mean 1.88, SD 1.12; P<.001). Perceived usefulness correlated with the percent of studying for which the respondent used electronic resources (Spearman ρ=.414, P<.001). CONCLUSIONS: Overall, the wiki was a highly utilized, although informal, part of the curriculum with much room for improvement and future exploration.
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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 | 0.072 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".