Head to head: The role of academic competition in undergraduate anatomical education
Bibliographic record
Abstract
Competition is a key element in many educational games and is often adopted by educators in an effort to motivate and excite their students. Yet, the use of academic competition in educational institutions remains the subject of much debate. Opponents argue that academic competition causes an increase in student anxiety and divides their attention. However, if the contexts of academic competition are defined, could the inclusion of a game-like competition in a university course be a viable and beneficial method of engaging students? Students (n = 67) were recruited from an undergraduate human anatomy course at Western University. Using a crossover design, students were exposed to a competitive tournament either at the time of their first term test or second term test. The anatomical knowledge of participating students was assessed prior to the start of the study using a baseline anatomy test. Following treatment with an online competitive anatomy tournament, student's term test grades and final course grades were analyzed. Both the second term test scores (F(2,64) = 3.743, P = 0.029) and overall course grades (F(2,64) = 3.356, P = 0.041) were found to be significantly different (P < 0.05) for individuals in the competitive group when compared to their non-competing peers. As suggested by the literature where organized competition in the classroom correlates to improved academic performance, this study uncovered significant results pertaining to increased academic performance resulting from participating in tournament-based competition. In light of these positive results, further exploration of the effects of academic competition on student performance across age brackets and disciplines is warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".