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Notice bibliographique
Résumé
While internet capable technology (ICT) use integrated within the curriculum has been linked to higher test scores, better GPAs and greater learning goal achievement (Kay & Lauricella, 2014), technology use does not always enhance learning. Within learning environments many students use ICT for off-task activities, and this is referred to as media-multitasking (Ophir, Nass, & Wagner, 2009). Unless two tasks are simple and well practiced, people show diminished attention and performance capabilities whilst multitasking due to cognitive limitations. Within educational contexts this explains why higher levels of media-multitasking have been associated with poorer academic performance and lower GPAs (e.g., Bowman, Levine, Waite, & Gendron, 2010). Given the significant implications of students’ media-multitasking for their learning outcomes, it is important to understand what media-multitasking activities are undertaken within learning contexts. The current study presents data examining the association between students’ media-multitasking within academic contexts (lectures, tutorials, exam study, assignment writing and recorded lecture viewing), and their attention and memory skills. Across all academic contexts, higher levels of media-multitasking were associated with more mental errors, more attentional focus and memory problems, and more mind wandering. Students reported more media-multitasking during assignment writing and exam study than when at class or viewing recorded lectures. The cognitive consequences of media-multitasking within learning environments will be discussed (e.g., increased task difficulty, memory load and switching between tasks) and the Cognitive Load Theory (Van Merrienboer & Sweller, 2005) will be used to illustrate why media-multitasking interferes with learning. Given the duty of care of educators for student learning, strategies for educating and regulating student media-multitasking behaviours within academic learning environments (e.g., technology use rules, engaging classes, active learning and educational activities, Hayashi, & Nenstiel, 2019, Purwaningtyas, 2019) will also be discussed.
 
 References
 Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54(4), 927-931.
 Hayashi, Y., & Nenstiel, J. N. (2019). Media multitasking in the classroom: Problematic mobile phone use and impulse control as predictors of texting in the classroom. Current Psychology, 1-7.
 Kay, R. H., & Lauricella, S. (2014). Investigating the benefits and challenges of using laptop computers in higher education classrooms. Canadian Journal of Learning and Technology, 40(2), n2.
 Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587. https://doi.org/10.1073/pnas.0903620106
 Purwaningtyas, I. (2019). Pursuing Effective Media Multitasking: An Effort of Managing Distractions in Digital Learning Classrooms.
 Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.
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.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
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 découle