How do students' self‐regulation skills affect learning satisfaction and continuous intention within desktop‐based virtual reality? A structural equation modelling approach
Notice bibliographique
Résumé
Abstract Virtual reality, as an excellent supportive instructional technology, has gained increasing attention from educators and professionals, where desktop‐based virtual reality (DVR) is broadly adopted due to its affordability and accessibility. However, when evaluating students' learning experiences such as flow experiences in DVR environments, most studies adopt a single construct (the total score of flow experience) rather than multiple constructs (enjoyment, engagement, concentration, presence and time distortion). This study implemented desktop‐based virtual reality for a STEM bridge designing program with a total of 254 undergraduates to investigate the relationship between self‐regulation skills, five dimensions of flow experience, learning satisfaction and continuous intention when engaging in a DVR learning environment. The results revealed that self‐regulated learning exerted a dominant impact on students' learning attitudes in DVR learning, in which students' flow experience had a significant mediating effect. Notably, although DVR exhibited poor time distortion, higher satisfaction and continuous intention were still predicted by the mentality of flow experience (ie, enjoyment, engagement, concentration and presence). The findings of this study contribute to the consideration of learning experiences and attitudes, which has insights for the future design of desktop‐based virtual reality environments and related instructional activities. Practitioner notes What is already known about this topic Students are different in self‐regulation skills, which influences their satisfaction and continuous intention in learning. Students' self‐regulation skills are one of the important variables in predicting their flow experience. A high level of flow experience contributes to a coherent and efficient learning experience within desktop‐based virtual reality (DVR) environments. What this paper adds Students' self‐regulation skills positively predicted their flow experience and satisfaction in DVR environments. The components of flow experience (enjoyment, concentration and presence) partially mediated the relationship between self‐regulation skills and satisfaction. Students' self‐regulation skills indirectly affect continuous intention by the enjoyment and engagement of flow experiences. Implications for practice and/or policy When delivering DVR‐based learning activities educators should be supportive of students with low levels of self‐regulation skills. Emphasis on promoting flow experiences such as enjoyment, engagement, concentration and presence in designing a DVR‐based classroom could enhance student satisfaction and continuous intention. Embedding scaffolding or feedback in DVR settings would support self‐regulated learning and subsequently improve student satisfaction and persistence through enhanced flow experience.
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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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».