Head to Head: The Role of Competition in Undergraduate Education
Bibliographic record
Abstract
Opponents argue that academic competition increases student anxiety and divides their attention. Yet, little evidence concerning the application of academic game style competition exists. Could game‐like competition in the classroom be a viable and beneficial method of engaging students? This study aims to sample the effects of anonymous peer competition and performance using an e‐classroom response system (ERS). Students (n= 136) were recruited from an undergraduate anatomy course. Using a crossover design, students were exposed to two competitive treatments, a tournament with many competitive elements and a quiz with few competitive elements. Students were assessed using qualitative surveys, a baseline anatomy knowledge quiz and their course grades. Preliminary data indicates a positive student response toward the use of competitive ERS in education. Approximately 77% of students found the competitive tournament to be engaging. Additionally, 62% reported enjoying the incorporation of competitive elements into their undergraduate anatomy course. We hypothesize that academic competition among peers in an online tournament setting encourages increased familiarization with lecture material, resulting in improved exam performance. Additionally, we believe that knowledge of personal tournament rank will influence a student's scholarly motivation and study habits prior to examination. Grant Funding Source : Ontario Graduate Scholarship
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.006 |
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".