Apps and Animations: Choosing Web-based Demonstrations to Support Student Learning
Notice bibliographique
Résumé
Over the past decade, the prevalence of mobile devices (e.g., smartphones, tablets, laptops) on university campuses has skyrocketed. A 2015 survey shows these technologies are quickly becoming ubiquitous in the classroom, with 87% of university students using laptops and 64% of students using smartphones on a weekly basis to complete their schoolwork (Pearson, Harris Polls, 2015). These same students also agree that tablets will transform university learning in the future (83%), that mobile technology makes learning more fun (79%), and helps students perform better in class (68%); in addition, 40% of university students would like to use mobile technologies in classes more often than they do now, while only 13% would like to use mobile devices less often (Pearson, Harris Polls, 2015).\nMobile devices seem to tantalize both students and educators alike with the promise of enhanced student learning including tailored content, instructional methods based on the needs of individuals, interactive engagement with the material, exploration beyond the classroom, and connections to the material unrestricted by time or location. The goals of teaching-related apps and animations are obvious: to generate student interest in a topic, promote student engagement, concretize abstract principles, and to enhance student learning. However, the small-but-growing body of research on the use of apps and animations has suggested that all are not created equal, particularly with respect to the ultimate goal of enhancing student learning (e.g., Tversky, Morrison, & Betrancourt, 2002). Several reviews have reported mixed findings with respect to the effects of mobile technology on student learning, with some suggesting that it enhances learning (Hwang & Wu, 2014) and others finding few significant benefits in learning outcomes (Cheung & Hew, 2009). Some studies have even shown that objective measures of student learning of critical concepts are actually impaired by the use of animations or computer-based demonstrations of these concepts (Copeland, Scott, & Houska, 2010; Mayer, Hegarty, Mayer, & Campbell, 2005).\nThe purpose of this workshop is to introduce participants to some of the research exploring the use of apps, animations, and demonstrations (e.g., participation in a classical experiment on visual perception) in university-level courses, with a focus on identifying the characteristics that separate the good from the bad in terms of student learning measures. The ultimate goal is to provide guidelines that will help educators better identify those apps, animations, or other instructional technologies that will be most beneficial in terms of encouraging deep student understanding of course material. Much of the material in this workshop is drawn from research in education and psychology, but the principles that we discuss would apply to almost any domain.
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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 | 0,000 | 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 | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».