ENHANCING STUDENT KNOWLEDGE ACQUISITION IN ONLINE LEARNING: A DUAL PROCESSING AND SOCIAL CAPITAL PERSPECTIVE
Notice bibliographique
Résumé
Social interaction in online learning can positively impact student learning outcomes, such as knowledge acquisition. As higher education increasingly transitions from traditional face-to-face learning to online platforms, understanding how to enhance student learning outcomes in online learning becomes essential. While social interaction differs in online and offline learning, the extant online learning literature mainly focuses on students’ social interaction frequency or quantity in general, without delving into their use of technologies for social interaction (i.e., social features). To provide a richer process-oriented and social capital perspective, the overarching objective of this dissertation is to understand how the use of social features in online learning can enhance student learning outcomes through emotional and cognitive engagement. More specifically, three research questions are investigated: 1) How does students’ use of social features in online learning affect their emotional and cognitive engagement experience with online learning? 2) How do multiple dimensions of social capital (i.e., structural capital, relational capital, and cognitive capital) moderate the relationship between students’ use of social features in online learning and their emotional/cognitive engagement experience in online learning? 3) How do students’ emotional/cognitive engagement experiences influence their knowledge acquisition in online learning? Drawing on Dual Process and Social Capital theories, this research develops a research model to elucidate how students' use of online social features influences their knowledge acquisition through the dual processes of emotional and cognitive engagement in online learning, and the moderating role of social capital on the impact iv of students’ use of social features in online learning. Data for this study was collected through a survey of participants who had at least one semester of online learning experience in the past three years within a university program. Structural equation modeling was employed for data analysis. The findings indicate that students' use of social features in online learning positively influences both emotional and cognitive engagement, which, in turn, affects knowledge acquisition. Additionally, cognitive capital positively moderates the impact of social feature usage on emotional and cognitive engagement in online learning. Relational capital negatively moderates the impact on cognitive engagement, but not on emotional engagement in online learning. Structural capital positively moderates the impact on cognitive engagement but not on emotional engagement in online learning. This dissertation contributes to the online learning literature by shedding light on how the utilization of social features can interact with students' social capital to influence their engagement, subsequently impacting their knowledge acquisition in online learning. The study advances the existing literature by exploring the intricate interplay between students’ social capital and their use of social features in online learning, elucidating the circumstances under which social resources enhance or impede the impact of such usage. From a practical standpoint, the insights gleaned from this study regarding students' online learning offer valuable guidance for distance educators and policymakers to enhance educational practices within online learning.
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 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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».