Behavioral analysis in immersive learning environments: A systematic literature review and research agenda

TL;DR

This paper tackles the fragmentation between immersive technology capabilities and behavioral analysis in education by proposing the Behavioral analysis in immersive learning framework (BAILF), an integrated model combining learning requirements, specification, evaluation, and iteration. It then conducts a systematic review of 40 peer-reviewed studies from Scopus, Web of Science, IEEE Xplore, and ERIC, applying a 4DF-informed coding scheme to map how learning stages, learner factors, pedagogy, context, and representation influence behavioral patterns in AR/VR/MR learning environments. Key findings show a need for explicit pedagogical requirements, diverse learner and domain contexts, and robust, multi-method analysis approaches, while highlighting technical, implementation, and data-processing challenges that constrain current practice. The study offers a concrete research agenda to improve design, specification, evaluation, and iterative development of immersive learning interventions, aiming to better connect behavioral insights with educational outcomes and scalable implementations.

Abstract

The rapid growth of immersive technologies in educational areas has increased research interest in analyzing the specific behavioral patterns of learners in immersive learning environments. Considering the fact that research on the technical affordances of immersive technologies and the pedagogical affordances of behavioral analysis remains fragmented, this study first contributes by developing a conceptual framework that amalgamates learning requirements, specification, evaluation, and iteration into an integrated model to identify learning benefits and potential hurdles of behavioral analysis in immersive learning environments. Then, a systematic review was conducted underpinning the proposed conceptual framework to retrieve valuable empirical evidence from the 40 eligible articles during the last decade. The review findings suggest that (1) there is an essential need to sufficiently prepare the salient pedagogical requirements to define the specific learning stage, envisage intended cognitive objectives, and specify an appropriate set of learning activities, when developing comprehensive plans on behavioral analysis in immersive learning environments. (2) Researchers could customize the unique immersive experimental implementation by considering factors from four dimensions: learner, pedagogy, context, and representation. (3) The behavioral patterns constructed in immersive learning environments vary by considering the influence of behavioral analysis techniques, research themes, and immersive technical features. (4) The use of behavioral analysis in immersive learning environments faces several challenges from technical, implementation, and data processing perspectives. This study also articulates critical research agenda that could drive future investigation on behavioral analysis in immersive learning environments.

Figures (5)

• Figure 1: Behavioral analysis in immersive learning framework (BAILF)
• Figure 2: Literature identification process derived from the PRISMA framework. Using the previously-defined key concept terms, this review yielded 676 results from databases. With the aid of the StArt software, 53 duplicate papers were found and deleted. During the screening phase, a review of the titles, abstracts, and keywords revealed 560 irrelevant articles, and 63 studies that met the inclusion criteria were included for the following selection stage. During the eligibility phase, the entire text of the remaining articles was scanned in detail to check the theoretical contribution in the area of learning implementation, behavior analysis, virtual communities, and the extension of instruction theories. Thus, 24 studies with limited evidence on analyzing learner behavior sequences or without immersive intervention were excluded. Additionally, another five articles were classified as ineligible due to the lack of reliability in their contributions or inconsistent analysis. Then, the backward and forward snowballing method was carried out on Google Scholar to find more relevant literature, including another six papers in this review. In the end, 40 papers were ultimately classified as eligible and included in the final review.
• Figure 3: Learning stages and cognitive learning objectives/outcomes. In the bottom right bar chart, a majority of researchers have implemented immersive learning for the higher echelons of learning stages, i.e., dialogue (n=20), followed by construction (n=14) as the mediate echelons of learning stages, and conceptualization (n=6) as the lower echelons of learning stages. In the upper-left bar chart, the most common cognitive learning outcome based on Bloom’s revised taxonomy was the ability to create knowledge (n=13). Remembering (n=5), understanding (n=7), applying (n=6), and analyzing (n=5) were the following most common cognitive learning outcomes that participants achieved in the immersive learning environments. Finally, in 4 studies, participants acquired the ability to evaluate the immersive learning activities.
• Figure 4: Learner specifics. a, learner types. The study participants were mostly primary school students, with 12 papers accounting for 30% of the total, and higher education students, with 9 papers accounting for 22.5% of all papers. Other studies tended to recruit learners of adults (17.5%, n=7), high school students (12.5%, n=5), kindergarten children (10%, n=4), middle school students, teachers (7.5%, n=3), and students with special needs (7.5%, n=3). One paper, accounting for 2.5% of the total, did not specify the learner type in the article content. b, application domain. More than half of the articles chose STEM (62.5%) as the application domain of their learning systems. The second popular application domain was general knowledge & skills (27.5%), where learners can learn basic social or art abilities to deal with daily affairs. The rest of the articles chose to learn about knowledge in the humanities (10%). Specifically, in the category of STEM, physics (17.5%), integrated science (17.5%), and biology (12.5%) were popular topics in the immersive learning system. “Integrated science” refers to the specific subject that involves more than one scientific discipline in the learning activities. In the humanities category, history (2.5%), culture (2.5%), and language learning (5%) were the common topics. In the general knowledge & skills category, the literature highlighted three topics of educative applications: art & design (7.5%), cognitive & social skills (12.5%), and reading (7.5%).
• Figure 5: Hardware devices. As for AR condition, except for one paper that used desktop computing devices (n=1) to construct the learning system, all other papers used mobile devices (n=20) as predominant apparatus. As for VR conditions, the non-immersive VR was equipped with mobile devices (n=1) and desktop computing devices (n=6). Full-immersive VR that adopted HMD devices (n=7) as infrastructure was widespread in the immersive learning system construction. Two studies used zSpace as hardware devices, which is expected to provide learners with a semi-immersive experience in this review. As for MR conditions, four kinds of hardware devices were used to set up MR systems: mobile devices (n=1), desktop computing devices (n=1), OST-HMD (n=2), and projection-based apparatus (n=1).