Engagement Strategies in a Peer-quizzing Game: Investigating Student Interactions and Powergaming
Bibliographic record
Abstract
Educational games are a popular means of engaging students in learning activities. However, students in game-based learning environments often engage in powergaming to reap undeserved rewards and spoil the experi-ence of their peers. This may happen when the students pretend to engage in game activities or collude with other students, exploiting the game settings and rules to maximize their points. In this explorative study, we investigate powergaming in three settings of an asynchronous online multiplayer peer-quizzing game in a blended learning setting – a first-year programming class in a Canadian university. We designed three experi-mental settings with three versions of an education game which allowed different levels of powergaming. The between-subject study involved three experimental groups: Group 1 used a game version where they received weekly performance feedback and tips on how to improve their performance, Group 2 used a game version with access to an existing resource bank, allowing them to maximize the number if their activities in the game, and Group 3 served as a control group with no additional interventions. The research aimed to investigate 1) the association between the power gamers' activity, their grades and their preference to work alone or in a group, 2) the types of activities (type of quiz questions) most used by power gamers, and 3) which game setting was the most conducive to power gaming. The results show that better grades are not associated with higher activity levels in the game. Students who engaged in more diverse types of game activities had better learning outcomes The control Group 3 had the highest average grades. As we expected, Group 2 engaged in the high-est number of activities, esp. in creating questions, a form of powergaming. The qualitative results showed that contrary to our assumption the powergamers in Group 2 was not harmful, because it created a rich set of re-sources in the game and fostered student engagement in the game.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".