Learning analytics in immersive virtual learning environments: a systematic literature review

• Published in: Smart learning environments Vol. 12; no. 1; pp. 43 - 27
• Main Authors: Tao, Lei, Cukurova, Mutlu, Song, Yanjie
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2025; Springer Nature B.V; SpringerOpen
• Subjects: Academic Achievement; Behavior; CAI; Computer assisted instruction; Computers and Education; Conferences (Gatherings); Data analysis; Database Management Systems; Deep learning; Education; Educational Environment; Educational Experience; Educational Methods; Educational research; Educational Strategies; Educational technology; Electronic Learning; Experiential Learning; Immersive learning; Immersive virtual reality; Influence of Technology; Learner Engagement; Learning analytics; Learning Experience; Learning management systems; Learning Processes; Literature reviews; Machine learning; Meta Analysis; Multimodal learning analytics; Online data bases; Performance prediction; Periodicals; Review; School environment; Statistical analysis; Statistical methods; Student Behavior; Student Participation; Students; Systematic review; Teaching Methods; Variance analysis; Virtual environments; Virtual learning environment; Virtual reality; Systematic review; Learning analytics; Virtual learning environment; Multimodal learning analytics
• ISSN: 2196-7091; 2196-7091
• DOI: 10.1186/s40561-025-00381-6

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.