Feasibility, Enjoyment, and Language Comprehension Impact of a Tablet- and GameFlow-Based Story-Listening Game for Kindergarteners: Methodological and Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Enjoyment plays a key role in the success and feasibility of serious gaming interventions. Unenjoyable games will not be played, and in the case of serious gaming, learning will not occur. Therefore, a so-called GameFlow model has been developed, which intends to guide (serious) game developers in the process of creating and evaluating enjoyment in digital (serious) games. Regarding language learning, a variety of serious games targeting specific language components exist in the market, albeit often without available assessments of enjoyment or feasibility. OBJECTIVE: This study evaluates the enjoyment and feasibility of a tablet-based, serious story-listening game for kindergarteners, developed based on the principles of the GameFlow model. This study also preliminarily explores the possibility of using the game to foster language comprehension. METHODS: Within the framework of a broader preventive reading intervention, 91 kindergarteners aged 5 years with a cognitive risk for dyslexia were asked to play the story game for 12 weeks, 6 days per week, either combined with a tablet-based phonics intervention or control games. The story game involved listening to and rating stories and responding to content-related questions. Game enjoyment was assessed through postintervention questionnaires, a GameFlow-based evaluation, and in-game story rating data. Feasibility was determined based on in-game general question response accuracy (QRA), reflecting the difficulty level, attrition rate, and final game exposure and training duration. Moreover, to investigate whether game enjoyment and difficulty influenced feasibility, final game exposure and training duration were predicted based on the in-game initial story ratings and initial QRA. Possible growth in language comprehension was explored by analyzing in-game QRA as a function of the game phase and baseline language skills. RESULTS: Eventually, data from 82 participants were analyzed. The questionnaire and in-game data suggested an overall enjoyable game experience. However, the GameFlow-based evaluation implied room for game design improvement. The general QRA confirmed a well-adapted level of difficulty for the target sample. Moreover, despite the overall attrition rate of 39% (32/82), 90% (74/82) of the participants still completed 80% of the game, albeit with a large variation in training days. Higher initial QRA significantly increased game exposure (β=.35; P<.001), and lower initial story ratings significantly slackened the training duration (β=-0.16; P=.003). In-game QRA was positively predicted by game phase (β=1.44; P=.004), baseline listening comprehension (β=1.56; P=.002), and vocabulary (β=.16; P=.01), with larger QRA growth over game phases in children with lower baseline listening comprehension skills (β=-0.08; P=.04). CONCLUSIONS: Generally, the story game seemed enjoyable and feasible. However, the GameFlow model evaluation and predictive relationships imply room for further game design improvements. Furthermore, our results cautiously suggest the potential of the game to foster language comprehension; however, future randomized controlled trials should further elucidate the impact on language comprehension.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».