A Serious Game for Soft Skills Assessment in Human Resources: A Cross-Sectional Within-Participant Convergent Validity Study (Preprint)
Notice bibliographique
Résumé
Abstract Background Soft skills are increasingly assessed in human resources, but commonly used methods (eg, interviews and self-report questionnaires) have well-known limitations. Serious games have been proposed as a complementary assessment format because they can standardize administration, embed assessment in interactive scenarios, and capture behavioral traces. However, evidence for their psychometric validity remains limited and heterogeneous. Establishing convergent validity against well-established reference instruments is a key step in supporting their use as assessment tools. Objective This study aimed to evaluate the convergent validity of Yuzu, a serious game that assesses (1) active listening via a gamified questionnaire inspired by the Active-Empathic Listening Scale (AELS), (2) decision-making under uncertainty via a gamified adaptation of the Iowa Gambling Task (IGT), and (3) teamwork style via dialogue choices inspired by the SYMLOG (System for the Multiple Level Observation of Groups) model. Methods We conducted a cross-sectional, within-participant convergent validity study in France with 39 adults (n=23 women; mean age 27.79, SD 7.96 y). Participants completed a single laboratory session on a desktop PC with headphones (Yuzu build v2, developed by Yuzu). Participants completed the 3 Yuzu modules and the corresponding reference instruments, administered separately (the AELS, an online IGT via PsyToolkit, and a simplified SYMLOG questionnaire). Primary outcomes were the associations between Yuzu and reference scores for each construct (active listening total score, IGT exploitation-phase net score, and SYMLOG dimension scores). Convergent validity was examined using Spearman correlations (2-sided α=.05). Agreement was additionally examined using Bland-Altman analyses for active listening and equivalence testing using two one-sided tests (TOST) for IGT net scores. Results At α=.05, active listening showed strong convergence between Yuzu and the AELS total score (Spearman ρ=0.890, 95% CI 0.804-0.948; P <.001), with minimal systematic bias (mean difference of 0.024, 95% CI −0.031 to 0.078). Decision-making scores were statistically equivalent across modalities based on TOST. The mean net score difference was 1.35 (90% CI −1.22 to 3.93), within the equivalence bounds [−5,+5] (TOST lower: P =.001; TOST upper: P =.01). Teamwork dialogue scores did not converge with the SYMLOG dimensions (dominance: ρ=−0.070, 95% CI −0.420 to 0.280; P =.69; positivity: ρ=0.082, 95% CI −0.266 to 0.418; P =.64; task orientation: ρ=0.134, 95% CI −0.260 to 0.494; P =.44), consistent with a ceiling effect toward cooperative choices. Conclusions This study provides convergent validity evidence for 2 complementary assessment modalities embedded in a single serious game, showing that a gamified AELS-inspired module and a gamified IGT adaptation can closely match established reference measures while supporting standardized administration. In contrast, the dialogue-choice teamwork module showed limited sensitivity and no convergence, suggesting that interpersonal profiling in serious games may require more discriminating scenario design and stronger controls for social desirability. Unlike many previous studies that evaluated a single game component, this study provides a module-by-module convergent validity blueprint within a single platform by using matched reference instruments, thereby informing both research and human resources deployment.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».