The perceptions of game developers compared to research on employment readiness regarding shortcomings in expertise and implications for curriculum development
Bibliographic record
Abstract
Twenty three interviews and four surveys were conducted as case studies investigating the perceptions of expertise, expertise acquisition, and gaps in employment readiness for novice game developers. Participants were primarily game development production staff and educators involved in game related programs. Research results were compared to employability skills research. The findings indicated that there is a great deal of alignment between them, but employability skills may be insufficient on their own to be a reliable standalone source for curriculum development in the game development field because of the industry’s unique characteristics. Implications from the research results, and insights from the in-depth interviews, that may be relevant to curriculum developers include evidence for a mismatch of the values, needs, and expectations of stakeholders; and a delineation of key characteristics of expertise and long-term success that may be valuable for inclusion in curriculum outcomes and measures. Two of the key characteristics identified were goal-focused passion, and holistic perspectives. Holistic perspectives included an awareness of heuristic use of tacit knowledge. The model of an expert learner was supported as a potential curriculum outcome focus that encapsulated the main characteristics of expertise that novices or advanced beginners could acquire. Another implication is that there may be a relation between expert characteristics and characteristics of functional behaviours that are related to positive psychology and cognitive behavioural therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".