Examining students' self‐regulated learning processes and performance in an immersive virtual environment.
Background: Self‐regulated learning (SRL) is a predictive variable in students' academic performance, especially in virtual reality (VR) environments, which lack monitoring and control. However, current research on VR encounters challenges in effective interventions of cognitive and affective regula...
| Publicado en: | Journal of Computer Assisted Learning Vol. 40; no. 6; pp. 2948 - 2964 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180899679&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180899679 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2024 vid: 40 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 180899679 178730515 180899679 180899679 10.1111/jcal.13047 180899679 ppf: 2948 ppct: 16 formats: tig: atl: Examining students' self‐regulated learning processes and performance in an immersive virtual environment. aug: au: Li, Yi‐Fan Guan, Jue‐Qi Wang, Xiao‐Feng Chen, Qu Hwang, Gwo‐Jen affil: Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua Zhejiang,, China sug: subj: Self-Directed Learning Evaluation Academic Performance Evaluation Virtual Reality Utilization Educational Technology Self Regulation Students, Undergraduate Funding Source China Male Female Human Behavior Cognition Pilot Studies Electroencephalography Feedback Computer-Assisted Instruction Data Analysis Software Analysis of Covariance Vocabulary One-Way Analysis of Variance Descriptive Statistics Facial Expression Attention Male Female ab: Background: Self‐regulated learning (SRL) is a predictive variable in students' academic performance, especially in virtual reality (VR) environments, which lack monitoring and control. However, current research on VR encounters challenges in effective interventions of cognitive and affective regulation, and visualising the SRL processes using multimodal data. Objectives: This study aimed to analyse multimodal data to investigate the SRL processes (behaviour, cognition and affective states) and learning performance in the VR environment. Methods: This study developed a VR‐based immersive learning system that supports SRL activities, and conducted a pilot study in an English for Geography course. A total of 21 undergraduates participated. Face tracker, electroencephalography, and learning logs were used to gather data for learning behaviour, cognition and affective states in the VR environment. Results and Conclusions: First, the study identified three categories of learners (HG, MG and LG) within the VR environment who presented different behavioural engagement and SRL strategies. The HG exhibited the highest level of cognition and affective states, which resulted in superior performance in terms of vocabulary acquisition and retention. The MG, despite possessing a higher level of cognition, performed inadequately in other aspects, leading to no difference in vocabulary acquisition and retention from the LG. By collecting and mining multimodal data, this study helps to enrich the visual analysis of SRL processes. In addition, the results of this study help to dissect the problems of students' SRL in a VR learning environment. Furthermore, this study provides a theoretical basis and reference for the study of SRL development in immersive learning environments. Lay Description: What is already known about this topic?: Self‐regulated learning (SRL) is a predictive variable in students' academic performance.Students in virtual reality (VR) environments may lack monitoring and control.SRL is a complex system influenced by multiple factors.SRL in VR environments needs multimodal data to analyse the process. What this paper adds?: A VR‐based immersive system that supports SRL activities is devised.Analysis of SRL processes and performance obtained from multimodal data are discussed.Students present different behavioural engagement and SRL strategies in the VR environment.Students with different SRL behaviours present different attention, affective state, and learning performance. Implications for practise and/or policy: Effective SRL interventions should be designed within the VR environments.The mining of SRL processes with multimodal data used in the study is recommended for explaining the SRL mechanisms in VR environments. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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