Remote Collaboration in Higher Game Development Education. Online Practices and Learning Processes of Students between Professional Routines and Psychosocial Challenges
Bibliographic record
Abstract
The development of digital games over physical distance is a common practice in the gaming industry, yet widely neglected in the curricula of digital game development programs at university level. The coronavirus pandemic, however, pushed project-oriented game programs all over the world towards an implementation of ad hoc approaches to remote development in their project-based courses. The present article demonstrates practice-based research examining such a course and its 30 third-year undergraduate students of game arts, game design, and game programming, who remotely collaborated in interdisciplinary groups of two to five persons over the course of half a semester during Germany’s logdown in spring 2020. Applying a mixed-method approach including quantitative and qualitative analyses of survey data (n=22) and qualitative content analyses of students’ postmortem documentations (n=30), this exploratory study reconstructed the online practices, experiences, and learning processes of these students between their professional routines and psychosocial challenges. The results of this study can be used in curriculum development to inform the advancement of courses focused on the development of prototypes over physical distance, which may not only be relevant for the field of games education, but also for related creative and project-oriented fields of higher education, such as design, digital media, and software engineering.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".