Medical Student Evaluation With a Serious Game Compared to Multiple Choice Questions Assessment
Bibliographic record
Abstract
Background: The gold standard for evaluating medical students’ knowledge is by multiple choice question (MCQs) tests: an objective and effective means of restituting book-based knowledge. However, concerns have been raised regarding their effectiveness to evaluate global medical skills. Furthermore, MCQs of unequal difficulty can generate frustration and may also lead to a sizable proportion of close results with low score variability. Serious games (SG) have recently been introduced to better evaluate students’ medical skills. Objectives: The study aimed to compare MCQs with SG for medical student evaluation. Methods: We designed a cross-over randomized study including volunteer medical students from two medical schools in Paris (France) from January to September 2016. The students were randomized into two groups and evaluated either by the SG first and then the MCQs, or vice-versa, for a cardiology clinical case. The primary endpoint was score variability evaluated by variance comparison. Secondary endpoints were differences in and correlation between the MCQ and SG results, and student satisfaction. Results: A total of 68 medical students were included. The score variability was significantly higher in the SG group (σ2 =265.4) than the MCQs group (σ2=140.2; P=.009). The mean score was significantly lower for the SG than the MCQs at 66.1 (SD 16.3) and 75.7 (SD 11.8) points out of 100, respectively (P<.001). No correlation was found between the two test results (R2=0.04, P=.58). The self-reported satisfaction was significantly higher for SG (P<.001). Conclusions: Our study suggests that SGs are more effective in terms of score variability than MCQs. In addition, they are associated with a higher student satisfaction rate. SGs could represent a new evaluation modality for medical students.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".