Towards a Framework to Support the Design of Esports Curricula in Higher Education
Bibliographic record
Abstract
Esports has generated an industry of increasing economic and cultural importance. In recent years, universities and other higher education institutions have responded to its growth by establishing undergraduate courses to satisfy the needs of innovators operating in the area. However, there is not yet consensus on what an esports curriculum should include. Despite being a technology-driven sector with ethical and professional dimensions that intersect computing, current ACM and IEEE curricula do not mention esports. Furthermore, existing courses tend to provide teaching and training on a wide variety of topics aside from those traditionally in computer science. These include: live events management; psychological research; sports science; marketing; public relations; video (livestream) production; and community management; in addition to coaching. This working group seeks to examine the requirements for developing esports studies at universities with a focus on understanding career prospects in esports and on the challenges presented by its disciplinary complexity. The group will identify key learning outcomes and assess how they align with industry needs, paving the way for a framework to support the design of esports curricula in higher education.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".