Involvement of End Users in the Development of Serious Games for Health Care Professions Education: Systematic Descriptive Review
Bibliographic record
Abstract
BACKGROUND: On the basis of ethical and methodological arguments, numerous calls have been made to increase the involvement of end users in the development of serious games (SGs). Involving end users in the development process is considered a way to give them power and control over educational software that is designed for them. It can also help identify areas for improvement in the design of SGs and improve their efficacy in targeted learning outcomes. However, no recognized guidelines or frameworks exist to guide end users' involvement in SG development. OBJECTIVE: The aim of this study is to describe how end users are involved in the development of SGs for health care professions education. METHODS: We examined the literature presenting the development of 45 SGs that had reached the stage of efficacy evaluation in randomized trials. One author performed data extraction using an ad hoc form based on a design and development framework for SGs. Data were then coded and synthesized on the basis of similarities. The coding scheme was refined iteratively with the involvement of a second author. Results are presented using frequencies and percentages. RESULTS: End users' involvement was mentioned in the development of 21 of 45 SGs. The number of end users involved ranged from 12 to 36. End users were often involved in answering specific concerns that arose during the SG design (n=6) or in testing a prototype (n=12). In many cases, researchers solicited input from end users regarding the goals to reach (n=10) or the functional esthetics of the SGs (n=7). Most researchers used self-reported questionnaires (n=7). CONCLUSIONS: Researchers mentioned end users' involvement in the development of less than half of the identified SGs, and this involvement was also poorly described. These findings represent significant limitations to evaluating the impact of the involvement of end users on the efficacy of SGs and in making recommendations regarding their involvement.
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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) | 0.001 | 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) | 0.000 | 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".