Immersive virtual reality for supporting complex scientific knowledge: Augmenting our understanding with physiological monitoring
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Résumé
Abstract Educators are recognizing the potential power of immersive virtual reality (IVR) to allow learners to experience previously intangible firsthand phenomena, such as atoms and molecules. In this study, an IVR simulation of a complex gene regulation system was co‐designed with an undergraduate microbiology course instructor. The course, with 234 students, was taught using active learning strategies, including peer instruction and exposure to a two‐dimensional computer simulation. Thirty‐four students from the course participated in an interactive IVR experience using head‐mounted displays. We assess students' conceptual understanding using tests, multimodal data collected during the IVR sessions (including video analysis in combination with physiological sensor data and eye‐tracking data) as well as semi‐structured interviews. We found that students who were seated while in IVR demonstrated significantly higher conceptual understanding of gene regulation at the end of the course and higher overall course outcomes, as compared to students who experienced the course as originally designed (control). However, students who experienced IVR in a standing position performed similarly to the control group. In addition, learning gain appears to be influenced by a combination of prior knowledge and how IVR is experienced (ie, sitting vs. standing). Learning implications for the connections between sensorimotor systems and cognition in IVR are discussed. Practitioner Notes What is already known about this topic Research on the educational applications of IVR for K‐12 and higher education emerged in the nineties, which can be summarized by several key reviews and meta‐reviews surveying the field but the answer to questions about the “added‐value” of IVR is often mixed (Dede, Jacobson, & Richards, 2017; Merchant, Goetz, Cifuentes, Keeney‐Kennicutt, & Davis, 2014)—we turn to the question of when IVR is effective for student learning. A common issue reported by researchers is that cognitive overload can hinder learning in IVR (Makransky & Lilleholt, 2018; Moreno & Mayer, 2004). Research considering the contribution of body positioning and sensorimotor perception on cognitive load is just emerging (Funk et al ., 2012; Nerhood & Thompson, 2016). What this paper adds Our finding that learning outcome is influenced by a combination of how IVR is experienced (ie, sitting vs. standing position) and students’ level of prior domain knowledge, builds on earlier findings that suggest IVR experiences to be taxing on cognitive resources and further suggests that body position and prior knowledge are related mitigating factors for learning outcomes in an IVR experience—thus a more nuanced relationship exists between cognitive resources, prior knowledge and learning outcomes in IVR. We offer a new approach for using multimodal physiological measures to gain insight into the conditions under which IVR impacts the learning experience. Implications for practice and/or policy Implications of our preliminary study suggest for a seated IVR learning experience for supporting students with lower levels of prior knowledge of complex concepts, while students with higher levels of prior knowledge could choose between either sitting or standing, full‐body experience.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 découle