Development and Evaluation of Intelligent Serious Games for Children With Learning Difficulties: Observational Study
Notice bibliographique
Résumé
BACKGROUND: Positive results can be obtained through game-based learning, but children with physical disabilities have fewer opportunities to participate in enjoyable physical activity. Because intelligent serious games can provide personalized learning opportunities, motivate the learner, teach 21st-century skills, and provide an environment for authentic and relevant assessment, they may be used to help children and adolescents with different kinds of learning disabilities to develop social and cognitive competences. OBJECTIVE: The aim of the study was to produce and evaluate a suite of intelligent serious games based on accessible learning objectives for improving key skills, personal development, and work sustainability among children with learning difficulties. METHODS: We conducted this research between 2016 and 2018, with pupils aged 11 to 12 years with learning disabilities who were integrated into the mainstream educational system. We used a 4-step methodology to develop learner creativity and social competences: (1) needs analysis, (2) development of learning content, (3) development of intelligent serious games, and (4) a usability evaluation focusing on the research questions and hypothesis. This was based on an initial teachers' evaluation, using a survey, of students using 2 of the games, where the main goal was to determine user motivation and initiative and to improve the games and the evaluation process. The initial evaluation was followed by a pilot evaluation, which was performed for all proposed games, in all partner countries. RESULTS: In an initial evaluation with 51 participants from Slovenia consisting of a pretest, followed by intelligent serious game intervention and concluding with a posttest, we observed statistically significant improvement in social and cognitive competences measured by tests. Based on these findings and observations, we improved the games and evaluation process. In the pilot test, conducted in all participating countries on a sample of 93 participants, the mean score on the teachers' observation form on the pretest (before students began using the intelligent serious games) was 3.9. In the posttest, after students had used intelligent serious games, the mean score was 4.1. CONCLUSIONS: We focused on developing and evaluating intelligent serious games for persons with learning disabilities, particularly for students with disabilities who are integrated into the mainstream educational system. Such games provide an opportunity for personalized learning and should be tailored to ensure that every learner can achieve the highest standard possible. However, we recommend that the games be adapted based on the students' needs and capabilities and a specially developed curriculum. The collected feedback showed that (1) children with learning disabilities need appropriately developed intelligent serious games, and (2) intelligent serious games, and the pertaining didactic methodology, should be based on an interoperable curriculum, so that teachers and trainers can use them. The student survey confirmed improvements in all aspects.
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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 | 0,000 | 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 ».