A Survey of Estonian Video Game Industry Needs
Bibliographic record
Abstract
Designing a video game design and development curriculum in higher education is a challenging task. Information about the needs of the respective industry certainly helps. In this paper, we have surveyed Estonian video game development companies to determine their current needs when it comes to knowledge areas, software tools, languages, abilities, and contextual fluencies. The survey is based on a similar survey conducted a decade ago and this paper compares the current results with those found earlier. Compared to the prior survey, we have found significant differences in the rated importance of knowledge in optimization, version control technologies, the C, C++, and C# programming languages, and the time management ability for video game development companies looking to hire university graduates. We have also extended the previous survey to include a contemporary selection of game design and development tools. Based on that, we have determined a strong need for graduates with skills specifically in Unity and Unreal Engine game engines, Photoshop raster image editing software, and Git version control software. While most of our results are largely consistent with the previous research, our added survey items like visual languages and game engines bring the results to the modern context. This allows curriculum designers and managers to see the differences regarding the landscape of industry needs for their graduates and thus make more informed decisions in their work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".