Diagnostic Markers of User Experience, Play, and Learning for Digital Serious Games: A Conceptual Framework Study
Bibliographic record
Abstract
BACKGROUND: Serious games for medical education have seen a resurgence in recent years, partly due to the growth of the video game industry and the ability of such games to support learning achievements. However, there is little consensus on what the serious and game components in a serious game are composed of. As a result, electronic learning (e-learning) and medical simulation modules are sometimes mislabeled as serious games. We hypothesize that one of the main reasons is the difficulty for a medical educator to systematically and accurately evaluate key aspects of serious games. OBJECTIVE: This study aimed to identify markers that can evaluate serious games and distinguish between serious games, entertainment games, and e-learning. METHODS: Jabareen's eight-phase framework-building procedure was used to identify the core markers of a serious game. The procedure was modified slightly to elicit "diagnostic criteria" as opposed to its original purpose of a conceptual framework. Following the identification of purported markers, the newly developed markers were tested on a series of freely available health care serious games-Dr. Game Surgeon Trouble, Staying Alive, and Touch Surgery-and the results were compared to the published test validity for each game. RESULTS: Diagnostic criteria for serious games were created, comprising the clusters of User Experience (UX), Play, and Learning. Each cluster was formed from six base markers, a minimum of four of which were required for a cluster to be considered present. These criteria were tested on the three games, and Dr. Game Surgeon Trouble and Staying Alive fit the criteria to be considered a serious game. Touch Surgery did not meet the criteria, but fit the definition of an e-learning module. CONCLUSIONS: The diagnostic criteria appear to accurately distinguish between serious games and mediums commonly misidentified as serious games, such as e-learning modules. However, the diagnostic criteria do not determine if a serious game will be efficacious; they only determine if it is a serious game. Future research should include a much larger sample of games designed specifically for health care purposes.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".