Current Competencies of Game Facilitators and Their Potential Optimization in Higher Education: Multimethod Study
Bibliographic record
Abstract
BACKGROUND: Serious games can be a powerful learning tool in higher education. However, the literature indicates that the learning outcome in a serious game depends on the facilitators' competencies. Although professional facilitators in commercial game-based training have undergone specific instruction, facilitators in higher education cannot rely on such formal instruction, as game facilitation is only an occasional part of their teaching activities. OBJECTIVE: This study aimed to address the actual competencies of occasional game facilitators and their perceived competency deficits. METHODS: Having many years of experience as professional and occasional facilitators, we (n=7) defined requirements for the occasional game facilitator using individual reflection and focus discussion. Based on these results, guided interviews were conducted with additional occasional game facilitators (n=4) to check and extend the requirements. Finally, a group of occasional game facilitators (n=30) answered an online questionnaire based on the results of the requirement analysis and existing competency models. RESULTS: Our review produced the following questions: Which competencies are needed by facilitators and what are their training needs? What do current training courses for occasional game facilitators in higher education look like? How do the competencies of occasional game facilitators differ from other competencies required in higher education? The key findings of our analysis are that a mix of managerial and technical competencies is required for facilitating serious games in higher educational contexts. Further, there is a limited or no general competence model for game facilitators, and casual game facilitators rarely undergo any specific, formal training. CONCLUSIONS: The results identified the competencies that game facilitators require and a demand for specific formal training. Thus, the study contributes to the further development of a competency model for game facilitators and enhances the efficiency of serious games.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".