Learning analytics in immersive virtual learning environments: a systematic literature review
Guardado en:
| Publicado en: | Smart Learning Environments vol. 12, no. 1 (Dec 2025), p. 43 |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Springer Nature B.V.
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3231993639 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2196-7091 | ||
| 024 | 7 | |a 10.1186/s40561-025-00381-6 |2 doi | |
| 035 | |a 3231993639 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Tao, Lei |u The Education University of Hong Kong, Department of Mathematics and Information Technology, Hong Kong SAR, China (GRID:grid.419993.f) (ISNI:0000 0004 1799 6254) | |
| 245 | 1 | |a Learning analytics in immersive virtual learning environments: a systematic literature review | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Research on learning analytics (LA) in various educational contexts is extensive, but research specifically on LA in immersive virtual learning environments (immersive VLEs) remains underexplored in terms of theoretical integration, methodological diversity, and multimodal data utilisation. This study reviews applications of learning analytics in immersive VLEs following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The paper presents the findings from 34 peer-reviewed journal articles and conference proceedings, describing their research purposes, learning environments, subjects, theoretical frameworks, data types, data analysis techniques, and challenges. Findings show that (1) the application of LA in immersive VLEs has expanded, shifting from an initial focus on learning outcomes and behavioural analysis to include performance prediction, self-regulation, social interaction, and affective states. However, these areas remain unevenly explored; (2) research has predominantly examined desktop-based immersive VLEs, while fewer studies have explored immersive virtual reality settings such as head-mounted displaysand cave automatic virtual environments; (3) higher education students have been the most frequently studied participants, with fewer studies involving K-12 students and adult learners; (4) most studies have employed data-driven approaches to identify behavioural patterns, but explicit theoretical frameworks have been used less frequently to guide analysis and interpretation; (5) behaviour data remains the most commonly used data type; (6) statistical methods such as regression and ANOVA dominate the analytical approaches, with machine learning and deep learning techniques remaining underutilised; and (7) challenges including technical complexity, data interpretability, privacy concerns, and adoption barriers impact the effectiveness and scalability of LA applications in immersive VLEs. These findings provide a comprehensive synthesis of current research trends, methodological limitations, and key challenges in LA applications within immersive VLEs, offering insights to guide future research and practice. | |
| 653 | |a Behavior | ||
| 653 | |a Computer assisted instruction--CAI | ||
| 653 | |a Performance prediction | ||
| 653 | |a School environment | ||
| 653 | |a Immersive virtual reality | ||
| 653 | |a Educational technology | ||
| 653 | |a Data analysis | ||
| 653 | |a Learning management systems | ||
| 653 | |a Variance analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Virtual reality | ||
| 653 | |a Students | ||
| 653 | |a Literature reviews | ||
| 653 | |a Online data bases | ||
| 653 | |a Statistical methods | ||
| 653 | |a Educational research | ||
| 653 | |a Learning analytics | ||
| 653 | |a Deep learning | ||
| 653 | |a Systematic review | ||
| 653 | |a Immersive learning | ||
| 653 | |a Virtual environments | ||
| 653 | |a Conferences (Gatherings) | ||
| 653 | |a Experiential Learning | ||
| 653 | |a Influence of Technology | ||
| 653 | |a Educational Methods | ||
| 653 | |a Teaching Methods | ||
| 653 | |a Learning Processes | ||
| 653 | |a Student Behavior | ||
| 653 | |a Academic Achievement | ||
| 653 | |a Learning Experience | ||
| 653 | |a Periodicals | ||
| 653 | |a Meta Analysis | ||
| 653 | |a Databases | ||
| 653 | |a Electronic Learning | ||
| 653 | |a Student Participation | ||
| 653 | |a Educational Experience | ||
| 653 | |a Educational Environment | ||
| 653 | |a Database Management Systems | ||
| 653 | |a Learner Engagement | ||
| 653 | |a Educational Strategies | ||
| 700 | 1 | |a Cukurova, Mutlu |u University College London, UCL Institute of Education, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201) | |
| 700 | 1 | |a Song, Yanjie |u The Education University of Hong Kong, Department of Mathematics and Information Technology, Hong Kong SAR, China (GRID:grid.419993.f) (ISNI:0000 0004 1799 6254) | |
| 773 | 0 | |t Smart Learning Environments |g vol. 12, no. 1 (Dec 2025), p. 43 | |
| 786 | 0 | |d ProQuest |t Education Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231993639/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3231993639/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231993639/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |