The integration of metaverse technology in higher education has introduced a transformative paradigm that redefines how learning environments are designed, experienced, and evaluated. The metaverse enables immersive, interactive, and collaborative learning spaces that support experiential and situated learning processes beyond the constraints of physical classrooms (Ueno et al. (2024); Humaira et al. (2024)). These environments facilitate real-time interaction through avatars, allowing learners to actively participate in simulations, virtual labs, and collaborative problem-solving activities (Motamedmanesh et al., 2025; Said, 2023).

Empirical studies have demonstrated that metaverse-based learning enhances various educational outcomes, including student engagement, learning performance, and skill development. For example, Sidhu et al. (2024) found significant improvements in conceptual understanding and problem-solving skills among engineering students. Similarly, Çelik & Baturay (2024) reported that metaverse environments positively affect vocabulary acquisition, retention, and student engagement. These findings suggest that the immersive nature of the metaverse fosters deeper cognitive processing and active participation in learning activities.

However, despite these advantages, the adoption of metaverse-based learning remains uneven due to challenges such as technological barriers, user readiness, and pedagogical alignment (Wang (2025); Al-Adwan et al. (2024)). Furthermore, while existing studies emphasize outcomes, there is limited understanding of the underlying mechanisms through which metaverse environments influence core learning processes, particularly interaction, engagement, and knowledge construction (Almuraqab et al., 2026; Zhang & Liao, 2022).

Interaction is a central component of effective learning, particularly in digital and immersive environments. In the context of the metaverse, interaction extends beyond traditional learner–content and learner–instructor communication to include avatar-mediated social interaction and collaborative engagement within virtual spaces.

Research indicates that interaction plays a critical role in shaping learners’ continuous use and learning experiences in metaverse-based platforms. Xu et al. (2024) found that interactive features significantly influence learners’ intention to continue using metaverse learning systems. Similarly, Le (2025) identified interaction as a key determinant of active participation in metaverse-based learning environments.

Moreover, the concept of social presence and trust has been highlighted as essential for meaningful interaction. Studies such as Namaziandost & Hwang (2026) and Al-kfairy et al. (2026) emphasize that perceived presence and trust significantly enhance collaborative learning and communication effectiveness. These findings suggest that interaction in the metaverse is not merely a technological feature but a complex socio-cognitive process that influences learner engagement and participation.

Student engagement is widely recognized as a multidimensional construct encompassing cognitive, emotional, and behavioral involvement in learning activities. In metaverse environments, engagement is amplified through immersive experiences, interactive simulations, and gamified learning elements.

Studies have shown that metaverse-based learning significantly enhances student engagement by providing realistic and interactive learning experiences. For instance, Cheng (2025) demonstrated that peer-facilitated metaverse learning improves both engagement and sustainability awareness. Similarly, Leung et al. (2025) found that gamified metaverse environments effectively motivate student participation and engagement.

Additionally, immersive learning ecosystems enable the development of transferable skills, such as collaboration, communication, and problem-solving. Muthmainnah et al. (2025) highlighted that VR-based metaverse interaction enhances learners’ transferable competencies, reinforcing the role of engagement as a mediator between learning environments and learning outcomes.

Despite these findings, the relationship between interaction and engagement in metaverse environments remains underexplored, particularly in terms of how interaction dynamics translate into sustained learner engagement.

Knowledge construction in higher education involves the active process of creating, transforming, and applying knowledge through interaction and collaboration. Metaverse environments provide unique opportunities for knowledge construction by enabling learners to engage in experiential, collaborative, and problem-based learning activities.

Research suggests that immersive environments facilitate deeper learning by supporting experiential and constructivist approaches. Wickramasinghe & Liyanage (2024) emphasized that metaverse-based experimental learning enhances conceptual understanding and knowledge application. Similarly, Du et al. (2026) found that metaverse-based education promotes holistic student development, including cognitive and social competencies.

However, existing studies often treat knowledge construction as an outcome variable without examining the mediating roles of interaction and engagement. This highlights the need for a comprehensive model that explains how metaverse-based interaction and engagement contribute to knowledge construction.

Based on the literature review, this study adopts a sequential explanatory framework, integrating quantitative and qualitative approaches to examine the relationships among interaction, engagement, and knowledge construction in metaverse-based learning. The proposed conceptual model posits that: Interaction serves as the primary driver of learning processes in the metaverse, influencing learners’ behavioral and cognitive engagement. Engagement acts as a mediating variable that translates interaction into meaningful learning experiences. Knowledge construction represents the ultimate learning outcome, reflecting learners’ ability to actively build and apply knowledge.

This framework is supported by prior studies emphasizing the interconnected roles of interaction, engagement, and learning outcomes in immersive environments (Tsappi et al. (2024); Yeganeh et al. (2025)). In the quantitative phase, the relationships among these constructs will be empirically tested using structural equation modeling (SEM) to validate the proposed model. In the qualitative phase, in-depth interviews will be conducted to explore learners’ experiences and provide deeper insights into how interaction and engagement shape knowledge construction within metaverse environments (Murunga, 2022; Nedeva et al., 2025). The conceptual framework not only provides a theoretical foundation for understanding metaverse-based learning but also aligns with the sequential explanatory design to generate comprehensive and robust findings.

Figure 1 illustrates the proposed conceptual model of metaverse-based learning in higher education. The model integrates technological, social, and pedagogical dimensions to explain how immersive metaverse environments influence core learning processes and outcomes. Specifically, metaverse experience, social presence, and trust are conceptualized as key antecedents that shape interaction and student engagement within virtual learning environments. Interaction is proposed as a primary driver that directly affects both student engagement and knowledge construction, while student engagement acts as a mediating variable linking interaction to knowledge construction. Furthermore, knowledge construction is hypothesized to influence learning outcomes, which subsequently affect continuous learning intention. The model also incorporates control variables and potential moderating factors to provide a comprehensive understanding of learning dynamics in metaverse-based education.

Based on the preceding literature review, this study develops a conceptual model to examine the causal relationships among interaction, student engagement, and knowledge construction within metaverse-based learning environments in higher education.

Interaction and student engagement

Interaction is a fundamental element in digital and immersive learning environments, particularly in the metaverse where communication occurs through avatar-mediated and real-time collaboration. Prior studies have demonstrated that interactive features significantly enhance learners’ active participation and sustained use of metaverse platforms (Le (2025); Xu et al. (2024)). Furthermore, the presence of social cues and perceived co-presence strengthens communication quality, leading to higher levels of engagement (Namaziandost & Hwang (2026)).

Interaction and knowledge construction

Interaction also directly contributes to knowledge construction by facilitating collaborative learning, discussion, and shared meaning-making processes. In immersive environments, learners actively explore and manipulate virtual objects, which enhances experiential learning and conceptual understanding (Wickramasinghe & Liyanage (2024)).

Student engagement and knowledge construction

Student engagement plays a crucial role in transforming learning experiences into meaningful knowledge outcomes. Engaged learners demonstrate deeper cognitive processing, emotional involvement, and active participation, which significantly contribute to knowledge construction (Cheng (2025); Muthmainnah et al. (2025)).

The mediating role of student engagement

In metaverse-based learning, engagement functions as a key mechanism that translates interaction into effective learning outcomes. High levels of interaction alone may not guarantee meaningful learning unless learners are cognitively and emotionally engaged.

Social presence and interaction

Social presence enhances the sense of “being there” in virtual environments, which improves communication quality and interaction.

Trust and student engagement

Trust in the metaverse environment and technology reduces uncertainty and increases user willingness to engage.

Metaverse experience and interaction

Immersive experiences provided by metaverse platforms enhance the frequency and quality of learner interaction.

Metaverse experience and student engagement

The immersive and interactive nature of the metaverse promotes higher levels of student engagement.

Student engagement and continuous learning intention

Highly engaged students are more likely to continue using metaverse-based learning systems.

Knowledge construction and learning outcomes

Effective knowledge construction leads to improved academic performance and learning outcomes.

This study adopts a sequential explanatory mixed-methods design, in which quantitative data collection and analysis are followed by qualitative exploration to provide deeper insights into the findings. This design is particularly appropriate for examining complex educational phenomena, such as metaverse-based learning, where both measurable relationships and contextual understanding are required (Ivankova et al. (2006); Bowen et al. (2017)).

In this approach, the quantitative phase serves as the primary method to test the hypothesized relationships among interaction, student engagement, and knowledge construction using structural equation modeling (SEM). The subsequent qualitative phase is conducted to explain and elaborate on the quantitative results, particularly focusing on how and why metaverse-based interaction influences engagement and learning processes (Thornberg et al., 2022; Jurek, 2023).

The use of a sequential explanatory design aligns with prior research that emphasizes the importance of integrating numerical findings with participant perspectives to capture the depth of learning experiences (Sridhar et al. (2025)). This design enables the study to move beyond statistical relationships and uncover the mechanisms underlying metaverse-based learning.

3.2.1 Participants and Sampling

The quantitative phase involved undergraduate students enrolled in higher education institutions who have experience using metaverse-based learning platforms. A purposive sampling technique was employed to ensure that participants had relevant exposure to immersive learning environments.

Following recommendations for mixed-methods research, the sample size was determined to meet the requirements for SEM analysis, ensuring adequate statistical power and model stability (Haynes-Brown (2025)). A total of approximately 150–250 respondents is considered sufficient for PLS-SEM analysis.

3.2.2 Instrument Development

Data were collected using a structured questionnaire consisting of multiple constructs:

All constructs were measured using multi-item reflective scales adapted from prior studies and assessed using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The measurement of engagement draws on the concept of deep engagement and transformative learning, which emphasizes cognitive and emotional involvement in meaningful learning experiences (Pugh et al. (2010)).

3.2.3 Data analysis

The quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis was conducted in two stages:

PLS-SEM is selected due to its suitability for complex models and exploratory research involving multiple latent constructs.

3.3.1 Data collection

Following the quantitative analysis, a subset of participants was selected for the qualitative phase using purposeful sampling, focusing on individuals representing diverse levels of engagement and learning experiences.

Data were collected through semi-structured interviews, allowing participants to share their experiences in metaverse-based learning environments. This phase aims to explain the quantitative findings by exploring learners’ perceptions of interaction, engagement, and knowledge construction.

3.3.2 Data analysis

The qualitative data were analyzed using thematic analysis, involving:

This approach enables the identification of patterns and themes that explain the relationships observed in the quantitative phase.

The integration of qualitative findings provides deeper insights into the mechanisms of engagement and knowledge construction, consistent with prior explanatory sequential studies (Lacy (2021); LaMontagne (2019)).

The final stage involves integrating the quantitative and qualitative results to provide a comprehensive understanding of metaverse-based learning. The qualitative findings are used to:

This integration enhances the validity and richness of the study by combining statistical evidence with experiential data, which is a key strength of sequential explanatory mixed-methods research (Ivankova et al. (2006)).

This study received ethical approval from the Research Ethics Committee of Universitas Negeri Yogyakarta (UNY), Indonesia (Approval No. 1866/UN34.17/LT/2026). The study was conducted in accordance with institutional ethical standards and internationally recognized principles for research involving human participants. Prior to data collection, all participants were informed about the objectives of the study, research procedures, confidentiality measures, and their right to withdraw from participation at any time without penalty. Verbal informed consent was obtained from all participants before participation. Verbal consent was considered appropriate because the study involved minimal risk, collected no sensitive personal information, and participation consisted solely of completing questionnaires and participating in voluntary educational interviews. No medical, psychological, or invasive procedures were involved. To protect participant privacy, all responses were anonymized and analyzed in aggregate form. No personally identifiable information was collected or reported. Access to the research data was restricted to the research team and used exclusively for academic research purposes.

A total of N = 198 valid responses were collected and included in the final analysis after data screening (removal of incomplete and inconsistent responses). The demographic characteristics of the participants are presented in Table 1.

Table 1 presents the demographic characteristics of the participants involved in this study. The sample consisted of 198 undergraduate students from diverse academic backgrounds, with a balanced gender distribution and a majority aged between 18 and 23 years. Most participants reported moderate experience with metaverse-based learning, with 60.6% having used such platforms for more than three months. In terms of usage frequency, a significant proportion (70.2%) engaged with metaverse environments at least once per week, indicating sufficient exposure to immersive learning contexts. The majority of participants used laptops or PCs (49.0%), followed by smartphones (30.8%), while a smaller proportion utilized VR headsets (14.1%). Additionally, most respondents reported intermediate to advanced levels of digital literacy, suggesting their readiness to interact with metaverse-based learning systems.

The measurement model was assessed to evaluate the reliability and validity of the constructs used in this study. Following standard PLS-SEM procedures, the evaluation included internal consistency reliability, convergent validity, and discriminant validity.

Table 2 presents the results of the measurement model evaluation. All constructs demonstrated strong internal consistency, with Cronbach’s alpha and composite reliability (CR) values exceeding the recommended threshold of 0.70. The outer loadings of all indicators were above 0.70, indicating satisfactory indicator reliability. Furthermore, the average variance extracted (AVE) values for all constructs were above 0.50, confirming adequate convergent validity. These results indicate that the measurement model is reliable and valid for further structural analysis.

Figure 3 illustrates the structural model results obtained from the PLS-SEM analysis, presenting the relationships among latent constructs, including metaverse experience, social presence, trust, interaction, student engagement, knowledge construction, learning outcomes, and continuous learning intention. The model displays standardized path coefficients (β values) and coefficient of determination (R²) for each endogenous construct. The results indicate that metaverse experience significantly influences interaction (β = 0.44) and student engagement (β = 0.29), while social presence (β = 0.36) and trust (β = 0.31) also contribute to interaction and engagement. Interaction has a significant effect on student engagement (β = 0.42) and knowledge construction (β = 0.28), while student engagement strongly influences knowledge construction (β = 0.47), confirming its mediating role. Furthermore, knowledge construction significantly affects learning outcomes (β = 0.55), which subsequently influence continuous learning intention (β = 0.60). All indicator loadings exceed the recommended threshold of 0.70, indicating strong measurement validity. The R² values demonstrate moderate to substantial explanatory power across constructs, confirming the robustness of the model.

4.2.1 Discriminant validity

Discriminant validity was assessed using the Heterotrait–Monotrait Ratio (HTMT).

Table 3 shows the HTMT values for all constructs. All values are below the threshold of 0.90, indicating that discriminant validity has been established. This confirms that each construct is empirically distinct from the others.

After confirming the adequacy of the measurement model, the structural model was evaluated to examine the hypothesized relationships among constructs. The assessment included path coefficients, coefficient of determination (R²), effect size (f²), predictive relevance (Q²), and mediation analysis using bootstrapping (5,000 resamples).

4.3.1 Coefficient of determination (R²)

The explanatory power of the model was assessed using R² values.

Table 4 shows that the model explains a substantial proportion of variance in the key constructs. Student engagement (R² = 0.64) and knowledge construction (R² = 0.67) demonstrate strong explanatory power, indicating that interaction and related antecedents significantly contribute to learning processes in metaverse environments.

4.3.2 Path coefficients and hypothesis testing

The significance of the hypothesized relationships was evaluated using bootstrapping.

Table 5 presents the results of hypothesis testing. All proposed hypotheses (H1–H10) were supported, indicating strong relationships among the constructs. Interaction significantly influences student engagement (β = 0.42, p < 0.001) and knowledge construction (β = 0.28, p < 0.001). Student engagement has a strong effect on knowledge construction (β = 0.47, p < 0.001), confirming its central role in learning processes. Additionally, metaverse experience emerges as a key antecedent, significantly affecting both interaction and engagement. The strongest relationship was found between knowledge construction and learning outcomes (β = 0.58), highlighting the importance of meaningful learning processes.

4.3.3 Effect size (f²)

Table 6 indicates that most relationships exhibit medium to large effect sizes, with knowledge construction having the strongest impact on learning outcomes (f² = 0.34).

4.4.1 Overview of qualitative analysis

The qualitative phase was conducted to explain and enrich the quantitative findings by exploring students’ lived experiences in metaverse-based learning environments. A total of 18 participants were purposively selected based on varying levels of engagement (high, moderate, and low) identified in the quantitative phase. Semi-structured interviews were conducted and analyzed using thematic analysis supported by NVivo, resulting in the identification of four major themes aligned with the Metaverse Experiential Learning Model (MELM).

4.4.2 Theme 1: Experiencing presence and immersion

Participants consistently highlighted the importance of immersion in shaping their initial learning experience. The use of avatars and 3D environments created a strong sense of “being there,” which enhanced their willingness to participate. Students reported that: The virtual environment felt more engaging than traditional LMS platforms. Avatar-based representation increased their sense of identity and presence. Navigation and spatial interaction contributed to learning motivation.

One participant noted:

“When I entered the virtual classroom, it felt like I was actually there, not just watching a screen. It made me more interested in participating.”

This theme supports the Immersive Entry stage in MELM and aligns with the quantitative finding that metaverse experience significantly influences interaction and engagement.

4.4.3 Theme 2: Interaction as a driver of engagement

Interaction emerged as a central mechanism that shaped students’ engagement in the metaverse environment. Participants emphasized that real-time communication and collaborative features made learning more dynamic and participatory. Key insights include: Increased frequency of peer-to-peer interaction. More active participation compared to traditional online learning. Enhanced communication through multimodal interaction (voice, text, gestures).

A participant explained:

“In the metaverse, I could talk, move, and work with others at the same time. It felt more natural than just typing in a chat box.”

This finding strongly reinforces the quantitative results (H1 and H3), confirming that interaction significantly enhances engagement and contributes to knowledge construction.

4.4.4 Theme 3: Collaborative knowledge construction

Participants described how the metaverse environment facilitated collaborative learning and deeper understanding. Group-based tasks and simulation activities encouraged students to actively construct knowledge rather than passively receive information. Emerging patterns include: Improved problem-solving through group collaboration. Co-creation of knowledge via shared virtual workspaces. Increased critical thinking during simulation-based learning.

One participant stated:

“Working together in the virtual space helped me understand the concepts better because we discussed and solved problems as a team.”

This theme aligns with the Collaborative Knowledge Construction stage of MELM and supports the strong quantitative relationship between engagement and knowledge construction (H3).

4.4.5 Theme 4: Reflection and sustained engagement

Reflection was identified as a critical stage that allowed students to internalize their learning experiences. Participants emphasized the importance of feedback and self-assessment in reinforcing their understanding. Students reported: Reflection sessions helped clarify complex concepts. Peer and instructor feedback improved learning outcomes. Self-assessment increased awareness of learning progress.

A participant noted:

“After the session, reflecting on what we did helped me connect everything together. It made the learning more meaningful.”

This finding confirms the Reflective Engagement stage in MELM and explains how engagement leads to sustained learning outcomes and continuous learning intention (H9).

4.4.6 Integration of qualitative and quantitative findings

The qualitative findings provide strong support for the structural relationships identified in the quantitative phase. Specifically: Interaction enhances engagement not only statistically but also experientially. Engagement mediates knowledge construction through active participation and collaboration. The metaverse environment creates a continuous learning cycle consistent with MELM The integration of findings demonstrates that metaverse-based learning is not merely a technological innovation but a pedagogically meaningful system that fosters interaction, engagement, and knowledge construction.

The results indicate that interaction is a key determinant of effective learning in metaverse environments. Unlike traditional online learning, where interaction is often limited to text-based communication, the metaverse enables avatar-mediated communication, real-time collaboration, and spatial interaction. These features create a more dynamic and immersive learning experience that encourages active participation. The qualitative findings reveal that students perceive interaction in the metaverse as more natural and engaging, which enhances their willingness to participate and collaborate. This suggests that interaction in the metaverse should be understood not merely as a technological affordance but as a pedagogical mechanism that facilitates meaningful learning experiences.

The study confirms that student engagement significantly mediates the relationship between interaction and knowledge construction. This finding implies that interaction alone does not guarantee effective learning outcomes unless it leads to active cognitive and emotional involvement. Students who are more engaged tend to participate more actively, collaborate more effectively, and demonstrate deeper understanding of learning materials. The qualitative data further show that immersive experiences and collaborative activities play a crucial role in sustaining engagement. Therefore, engagement can be conceptualized as the mechanism that transforms interaction into meaningful learning outcomes, bridging the gap between technological interaction and knowledge construction.

The strong relationship between engagement and knowledge construction highlights the importance of active and collaborative learning processes in metaverse environments. Students reported that working in virtual groups and participating in simulation-based activities enabled them to construct knowledge more effectively. This finding aligns with constructivist learning theory, which emphasizes that knowledge is actively built through interaction and social collaboration. The immersive nature of the metaverse further enhances this process by allowing learners to explore, experiment, and reflect in a simulated environment. As a result, knowledge construction becomes a dynamic and participatory process, leading to improved learning outcomes and deeper cognitive understanding.

The findings also highlight the importance of metaverse experience, social presence, and trust as key antecedents of interaction and engagement. Metaverse experience was found to significantly influence both interaction and engagement, indicating that the quality of immersive design plays a crucial role in shaping learning behavior. Social presence enhances the sense of “being there” in the virtual environment, which strengthens communication and collaboration among learners. Meanwhile, trust reduces uncertainty and increases students’ willingness to engage in virtual learning activities. These results suggest that effective metaverse-based learning requires not only advanced technology but also psychological and social factors that support user engagement.

This study contributes to the literature by integrating interaction, engagement, and knowledge construction into a comprehensive framework that explains learning processes in metaverse environments. The findings extend constructivist and experiential learning theories by demonstrating how immersive technologies can facilitate active knowledge construction. In addition, the study provides empirical evidence supporting the mediating role of engagement, offering a deeper understanding of how interaction influences learning outcomes. The development of the Metaverse Experiential Learning Model (MELM) further contributes by providing a structured framework that links theoretical concepts with practical implementation in higher education.

The results of this study offer important implications for educators, institutions, and technology developers. Educators should design learning activities that emphasize interaction, collaboration, and reflection to maximize student engagement. Institutions should invest in metaverse infrastructure and provide training to support the effective implementation of immersive learning environments. Technology developers should focus on enhancing user experience, social presence, and interaction features to improve engagement and learning outcomes. The MELM framework can serve as a practical guide for designing and implementing metaverse-based learning in higher education.

Despite its contributions, this study has several limitations. The research was conducted within a specific higher education context, which may limit the generalizability of the findings. In addition, the use of self-reported data may introduce bias, and the study primarily focuses on short-term learning outcomes. Future research should explore longitudinal effects of metaverse-based learning, incorporate objective behavioral data such as learning analytics, and examine the role of cultural and contextual factors. Further studies could also investigate additional moderating variables to provide a more comprehensive understanding of metaverse-based learning.

This study offers significant contributions to both theory and practice in the field of educational technology. From a theoretical perspective, it advances the understanding of learning processes in immersive environments by integrating interaction, engagement, and knowledge construction into a unified framework. The validation of engagement as a mediating variable provides new insights into how metaverse-based learning influences educational outcomes. From a practical perspective, the study provides actionable guidance for educators, institutions, and developers in designing effective metaverse-based learning environments. The proposed MELM framework serves as a practical model that can be adopted and adapted across various educational contexts. In addition, the findings highlight the importance of creating immersive, interactive, and collaborative learning experiences to maximize student engagement and learning effectiveness. The study also contributes to the broader digital transformation of education by demonstrating how emerging technologies can be leveraged to create meaningful and impactful learning experiences in higher education.

The authors gratefully acknowledge the financial support provided by the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance of the Republic of Indonesia. The authors also express their sincere appreciation to the participating universities, lecturers, and students who generously contributed their time and experiences to this study. Special thanks are extended to colleagues, reviewers, and academic mentors whose valuable insights and constructive feedback helped improve the quality of this research. The authors further acknowledge all scholars whose previous work informed and inspired the development of this study.

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