Impacts of gamified homework in an online oral biology course
Bibliographic record
Abstract
Active learning strategies in face-to-face teaching can improve student performance, motivation, and F I G U R E 1 (A) Course pattern and the design of the Gimkit homework intervention.Gimkit homework (Gimkit HW) was introduced in the winter term of the course.The winter term of the course has two non-cumulative exams (Exam-I and Exam-II).Only one homework was assigned in the first part of the winter term, covering practice questions from 14 vodcasts.In the 2nd part of the winter term, three Gimkit homework tasks were posted in the learning management system (LMS) with questions from 10 vodcasts.(B) Class average was calculated for Exam-I and Exam-II.Statistical analyses (Two-tailed paired t-tests) with statistical significance defined as p < 0.05 were performed.The average of Exam-II was significantly higher than Exam-I (p = 0.00038).(C) Students' performances were compared between Exam-I and Exam-II.In the graph, the percentage of marks and number of students are represented in X and Y-axis respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".