Utilitarian evaluation of educational games in post-secondary students
Bibliographic record
Abstract
,Educational games have become a niche genre, with few games produced for the postsecondary market. This study intended to investigate game mechanics by providing an educational game for first-year university math courses. Participants displayed such an aversion to the educational game genre that the study shifted to a qualitative investigation of students’ attitudes toward educational games. Interviews revealed that math aversion was a more powerful deterrent than expected, but additional themes included unexpected preconceptions toward games, social identity factors, and themes involving trust; students simply did not trust that a game with an educational purpose would be worth their time. A larger theme was utilitarian vs hedonic evaluation: once the game was introduced as ‘educational’, the students assessed it for its utility value, not just its hedonic value. Utilitarian assessment of educational games appears to be an underexplored factor in educational game adoption, particularly in adult users such as postsecondary students. These interviews, along with an examination of exergame usage as an analogue for adult educational game users, suggest that balancing utilitarian and hedonic mechanics is key. Further research is needed to identify an ideal balance for “fun tools” to enhance educational game adoption rates in post-secondary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".