Teaching Students How to Make Games for Research-Creation/Meaningful Impact
Bibliographic record
Abstract
There are multiple courses in higher education today that expose students to elements of game studies, development, design, and associated research methods, but far fewer explore using games directly as a method for research creation. There are emerging themes in the field around curricular efforts that consider the role of games as a method to (1) advance research (broadly defined) through the act of making games; (2) use games as tools for doing research; and (3) creatively present research topics and findings through games. This paper presents a post-mortem analysis of two courses that were designed, developed, and offered to graduate students at separate universities with these topics in mind, describing their success, failure, and lessons learned. One of these universities is largely focused on doctoral students in game studies, while the other is focused on MFA students in game design, and both offer game-centric MA programs, and also opened these courses to other graduate students in related fields. By examining the design, development, and evaluation of these courses as a comparative case study, the authors provide a practical narrative of best practice in the emerging area of games as research creation tools and associated curriculum.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".