Teaching Students About Plagiarism Using a Serious Game (Plagi-Warfare): Design and Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Educational games have been proven to support the teaching of various concepts across disciplines. Plagiarism is a major problem among undergraduate and postgraduate students at universities. OBJECTIVE: In this paper, we propose a game called Plagi-Warfare that attempts to teach students about plagiarism. METHODS: To do this at a level that is beyond quizzes, we proposed a game storyline and mechanics that allow the player (or student) to play as a mafia member or a detective. This either demonstrated their knowledge by plagiarizing within the game as a mafia member or catching plagiarists within the game as a detective. The game plays out in a 3D environment representing the major libraries of the University of Johannesburg, South Africa. In total, 30 students were selected to evaluate the game. RESULTS: Evaluation of the game mechanics and storyline showed that the student gamers enjoyed the game and learned about plagiarism. CONCLUSIONS: In this paper, we presented a new educational game that teaches students about plagiarism by using a new crime story and an immersive 3D gaming environment representing the libraries of the University of Johannesburg.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".