Effect of Gamification on Task Engagement During an Eye-Tracking Test Battery in 5-Year-Old Children Born Preterm: Observational Study
Notice bibliographique
Résumé
BACKGROUND: Recent studies suggest that eye movements during tasks reflect cognitive processes and that analysis of eye movements using eye-tracking devices can identify developmental impairments in young children. Maintaining engagement during eye-tracking assessments in young children is challenging and often results in data loss due to distractions. This leads to incomplete recordings and repeated measurements, which can be taxing for young children. Gamification of eye-tracking procedures for cognitive diagnosis might increase engagement and help mitigate these problems, but its effects should be studied and quantified. OBJECTIVE: This study compares a standard eye-tracking test battery designed by us with a gamified cartoon version to evaluate the effectiveness of gamification in reducing data loss. The gamified test incorporated child-friendly visuals to provide context for the stimuli presented. The study has two objectives: (1) to compare the data quality between the two versions of the test and (2) to investigate whether, by applying a dynamic stopping criterion to both tests, the higher data quality of the gamified procedure allows earlier test termination. METHODS: Data were collected in a cohort of 25 children born preterm aged 5 years. We measured data quality using a metric derived from robustness, which we defined as the lost data index (LDI), along with task completion rates and feedback from participants. Data analyses were performed as follows: (1) direct comparison of the LDI for the two tests and (2) demonstrating that, although the base gamified test is longer, applying a stopping criterion results in comparable durations. The stopping criterion was based on the number of tasks with an LDI value below a predefined threshold. RESULTS: The gamified version demonstrated a significant reduction in average LDI compared with the standard version in the first (P<.001, Mann-Whitney U test) and second (P=.01, U test) quarters of the test. In addition, a lower rate of missing values, concentrated at the beginning of the tests, allowed the cartoon test to be stopped after fewer tasks. This, together with the longer tasks of the cartoon test, resulted in comparable test lengths for all thresholds measured by area under the curve (P=.50, U test) and at the chosen threshold of 0.2 LDI (P=.21, U test). Increased engagement was further supported by positive feedback, with 79% (11/14) of the participants who provided feedback preferring the gamified version. CONCLUSIONS: These findings highlight the potential of serious games in eye-tracking-based cognitive assessments for 5-year-old children born preterm. Specifically, gamification might reduce missing values and increase participant engagement, leading to higher retention rates and more effective tests, without significantly lengthening testing procedures.
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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 | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| 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 ».