Investigating studio-based learning in a course on game design
Bibliographic record
Abstract
Jobs in the computing field demand communication and teamwork skills in addition to programming skills. Focus at the undergraduate level needs to be shifted towards developing these collaborative skills to enable a more smooth transition into employment in industry. The University of Victoria is in its second year of offering a course on game design. In the first offering, new activities were introduced to address issues identified by recent studies on university graduates entering industry. We focused on integrating cooperative learning, group orientation, and peer review activities into the game design process. The course attracted students across multiple disciplines, and an analysis indicated increased student interest in pursuing a computer science degree. Unfortunately, the same pre- and post-surveys suggested that our collaborative activities may have resulted in a decrease in student interest regarding course work and in pursuing studies in game design. In this paper we report on how we used a studio-based pedagogical approach to restructure the peer review activities in our course. In our previous offering, students received peer feedback only on their final game presentation. In our latest offering, we integrated peer review activities into every step of the game development process, allowing students to refine their ideas while progressing through a game project. A quantitative analysis informs us that our refined peer review activities were successful in increasing student presentation confidence, sense of community, and excitement towards their course projects.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".