Evaluating the Impact of Serious Games on Study Skills and Habits
Bibliographic record
Abstract
Learning is a constructive process that requires dedication and motivation. Learning games can be used to en-gage learners, by providing opportunities to apply knowledge in a safe and fun environment. We created a peer-quizzing game that allows students to quiz each other on the course material and to playfully engage in good study habits. We set out to explore if learners with better learning skills and habits would be more en-gaged in the game in the context of a first-year computer science university class. We used the "Study Skills and Habits Questionnaire" to study the relationship between study skills and habits (especially time management, concentration, goal setting, and comprehension) and engagement in a competitive version of a peer-quizzing game including a leaderboard. We collected and analysed gameplay data of the students (n=34), such as creating quiz questions and solving the questions created by their peers. The results of the data analysis showed a moderate positive correlation between study skills and habits (time management, concentration, goal setting, and comprehension) and the number of questions answered and a moderate negative correlation with the number of questions created in the game. However, the regression model was not statistically significant in explaining the variation in the dependent variable (questions created). Among the individual predictor variables, only goal setting had a statistically significant positive effect on the number of questions created. Other variables, such as time management, concentration, and comprehension, did not show significant effects on the number of questions created by students.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".