Classification of Video Games Bachelor’s Curricula
Bibliographic record
Abstract
As in any professional field, aspiring video game artists, designers, and developers must acquire the necessary skills and knowledge for a successful career. Higher education institutions offer varying video game Bachelor’s degree programs to meet the diverse needs of the industry. Our objective in this study was to explore these curricula to gain insight into and understanding of the contemporary video game higher education landscape. We explored 113 Bachelor’s degree curricula in Europe that had publicly available information in English about their courses. We classified the courses within each curriculum using ten devised classifiers based on the IGDA Curriculum Framework 2008 but modified them to suit our interests. The content of the classified curricula was then used to create curriculum profiles – data vectors that characterize a curriculum based on its contents. These profiles allowed for hierarchical clustering and principal component analysis (PCA) to identify and investigate the three common types of video game curricula: video game art, interdisciplinary video game design, and video game technology/programming. Our results indicate that art and programming curricula are highly specialized, with clear distinctions in yielding Bachelor of Arts and Bachelor of Science degrees. Curricula focused on interdisciplinary video game design do not have such clear distinctions in the degree titles and content specialization. They are more varied in their profiles and tend to bridge the gap between art and programming curricula, reflecting the interdisciplinary nature of game design as a profession. Compared to results from previous studies, we found that contemporary curricula place a greater emphasis on graduation projects, internships, and soft skills. Our findings provide an overview of the current state of higher education in video games, which may prove helpful for those working with or interested in these curricula.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".