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Article

Bridging Theory and Practice with Immersive Virtual Reality: A Study on Transfer Facilitation in VET

by
David Kablitz
Department of Business Administration and Economics, University of Konstanz, 78464 Konstanz, Germany
Educ. Sci. 2025, 15(8), 959; https://doi.org/10.3390/educsci15080959
Submission received: 22 May 2025 / Revised: 18 July 2025 / Accepted: 22 July 2025 / Published: 25 July 2025
(This article belongs to the Special Issue Dynamic Change: Shaping the Schools of Tomorrow in the Digital Age)
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Abstract

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings.

1. Introduction

The use of immersive virtual reality (IVR) in education has increased markedly in recent years, as evidenced by its growing integration into educational projects and initiatives. This development underscores the perceived potential of IVR to enrich teaching and learning processes, particularly by enabling learners to engage with realistic, interactive virtual environments (Raja & Lakshmi Priya, 2020). Through its capacity to simulate authentic learning contexts and promote active engagement, IVR supports a strong connection to practice, thereby fostering practice-oriented learning—an essential element of vocational education and training (Buehler & Kohne, 2019).
Systematic reviews further suggest that IVR often outperforms traditional, predominantly analog, instructional settings, especially with regard to the acquisition of domain-specific knowledge (Conrad et al., 2024; Hamilton et al., 2021; Howard et al., 2021). This advantage is particularly notable in the acquisition of relevant subject knowledge. In the context of vocational education, especially within the German dual training system, a central objective, alongside the acquisition of specialized knowledge, is the knowledge transfer between school-based instruction and practical application in the workplace (Wirth, 2013).
The promotion of knowledge transfer in IVR-based training programs has been predominantly discussed in corporate training settings (Howard et al., 2021). In contrast, findings regarding knowledge transferability between theory and practice in vocational school settings are still lacking. Therefore, this study examined an IVR-based instruction for retail trainees and investigated how IVR technology can enhance transferability in vocational school contexts, with a focus on knowledge acquisition and transfer-related self-efficacy as relevant predictors for actual learning transfer (Huang et al., 2015; Wißhak, 2022). Additionally, key factors that are theoretically linked to learning effectiveness in IVR-integrated settings, according to the Cognitive and Affective Model of Immersive Learning (CAMIL; Makransky & Petersen, 2021), were considered.

2. Theoretical and Empirical Background

2.1. Potential of IVR as an Educational Technology

In education, IVR offers innovative approaches to presenting learning content, with the aim of making it more tangible and experiential (Hellriegel & Čubela, 2018). IVR learning environments are, therefore, characterized by their ability to simulate realistic situations and are well suited for authentic knowledge acquisition due to their immersive properties (Buehler & Kohne, 2019). This practical relevance plays a central role in vocational training as IVR can be used to create learning settings that mirror actual workplace environments that can foster the link between theory and practice and, thus, knowledge transfer (Zender, 2020). Thereby, learners can be prepared for future work situations by engaging in practical processes and procedures in IVR. This enables the acquisition of specific professional competencies and domain-specific knowledge (Zinn, 2019). Furthermore, it is possible to simulate scenarios that would be prohibitively expensive, inaccessible, or hazardous in reality (Zender, 2020). Against this background, the integration of IVR technology in vocational education and training (VET) can be very useful for simulation-based, practice-oriented and experiential teaching (Kablitz et al., 2023; Kim et al., 2020; Lee, 2020; Mulders et al., 2022; Thomann et al., 2024).

2.2. IVR-Based Learning

Although IVR has gained importance in educational research, there is little consistency regarding its integration into learning theory (Allcoat & von Mühlenen, 2018; Makransky & Petersen, 2021; Radianti et al., 2020). Nevertheless, learning in an IVR environment is primarily associated with constructivist learning theory (Loke, 2015; Makransky & Petersen, 2021; Marougkas et al., 2023). In particular, the high level of interactivity in a virtual learning environment reflects this constructivist approach. Learners can independently explore the virtual world, navigate objects, and interact with other people/avatars in so-called collaborative IVR environments (Schwan & Buder, 2006). This feeling of the users to control their own actions in virtual environments is described as agency (Moore & Fletcher, 2012). At the same time, in addition to the opportunity for agency, immersion and the associated experience of being present in IVR environments are key factors that distinguish learning with IVR from other educational technologies. Makranksky and Petersen emphasized these aspects in the Cognitive Affective Model of Immersive Learning (CAMIL), which was developed based on existing research and theories of motivation and learning (Makransky & Petersen, 2021). The CAMIL, shown in Figure 1, assumes that presence and agency are general prerequisites for learning with IVR. These two factors, known as IVR affordances, depend on three technical characteristics of the medium: immersion, control factors, and representational fidelity. Immersion refers to the extent to which an individual feels fully engaged and absorbed in a virtual environment. Control factors include variables such as the degree of control, immediacy of control by the user, and control mode. Representational fidelity refers to a realistic display of the environment and a smooth change in view (Makransky & Petersen, 2021). The higher the level of these factors, the stronger the perception of presence and agency in IVR. The two affordances theoretically influence six affective and cognitive factors (interest, intrinsic motivation, self-efficacy, embodiment, cognitive load, and self-regulation). In turn, these factors influence knowledge acquisition and the transfer of that knowledge. The central characteristics of IVR (agency and presence) are, therefore, hypothesized to exert an indirect effect on learning outcomes (Makransky & Petersen, 2021).

2.3. Knowledge Transfer

In the literature, knowledge transfer is defined as “the extent of retention and application of knowledge, skills and attitudes from the training environment to the workplace environment” (Bossard et al., 2008, p. 1). As already emphasized, this transfer is an essential educational objective in VET alongside the acquisition of knowledge, as trainees should not only acquire knowledge but also apply and implement it in their training companies or future workplace (Wirth, 2013).
In educational research, transfer is predominantly examined within the context of workplace training. However, it is also a critical issue in vocational schools, where the disconnect between theoretical instruction and practical application is frequently cited as a concern by students, particularly in commercial VET programs (Wirth, 2013). As outlined in the previous chapter, IVR offers numerous opportunities to support and enhance transfer through the characteristics of presence and agency (Makransky & Petersen, 2021). By simulating requirements of real-world workplace practice, IVR can help bridge the gap between classroom learning and practical experience, thereby establishing a more direct connection to trainees’ operational environments within the context of school-based instruction.
The measurement of transfer varies across scientific studies (Hense & Mandl, 2011). While some studies rely on observations, others employ self-reports from learners regarding their application of learned knowledge in practice. The latter approach is widely used due to its operational simplicity and lower resource requirements (Hense & Mandl, 2011). However, actual transfer depends not only on the training itself, but also on the actual opportunities for application in the professional context (Kauffeld et al., 2009). This aspect makes measuring the intended transfer considerably more difficult. In the specific case of measuring transfer between vocational schools and training companies, this challenge is further compounded by varying transfer opportunities across different companies due to different organizational contexts (e.g., industry, company size, supervision by trainers). Additionally, the timing of the measurement plays a central role (Cheng & Ho, 2001), as not all apprentices have the opportunity to apply what they have learned immediately after the theoretical instruction in school. These factors underscore the complexity of examining learning transfer, which is influenced by the context and practical applicability. As measuring the actual transfer is challenging, it is often decided to focus on relevant predictors of transfer, which are also highly relevant for successful transfer from a practical perspective (Hense & Mandl, 2011). In her systematic literature review, Wißhak (2022) identifies relevant transfer determinants that can be grouped into three main categories: participant characteristics, training design, and work environment (Hense & Mandl, 2011; Wißhak, 2022). With regard to participant characteristics, age, gender, motivation to learn, and motivation to transfer play an important role in actual transfer. Furthermore, it has been shown that learners’ self-efficacy regarding the application of learned material after training positively influences transfer (Granados Cannawurf, 2005; Huang et al., 2015). The findings suggest that transfer-related self-efficacy (von Solga, 2011) can be considered as a predictor of actual transfer success. Additionally, the degree of subject knowledge acquisition has been identified as a contributing factor to actual transfer success (Huang et al., 2015). In relation to learner characteristics, it can, therefore, be concluded that teachers have a direct influence on key transfer determinants, including motivation, self-efficacy, and knowledge acquisition of the learners in school lessons.
In terms of training design, teachers should focus on promoting social interactions when planning transfer-oriented vocational school lessons (Gegenfurtner, 2011; Gegenfurtner & Vauras, 2012). They should also provide opportunities for the practical application of learning content, either during training or directly in the classroom, to reinforce the learned material and facilitate its transfer to workplace practice (Wißhak, 2022). In this regard, realistic application scenarios play a crucial role for transfer facilitation (Hense & Mandl, 2011). Concurrently, the integration of realistic scenarios within vocational education curricula poses a considerable challenge, particularly in the context of commercial professions. As Wirth (2013) argues, educational institutions frequently lack the spatial resources and equipment necessary to translate the abstract and complex learning content of commercial occupations into a tangible classroom environment.
In this context, the integration of IVR technology offers valuable opportunities to enhance transfer in vocational education. Consequently, by leveraging the affordances of IVR (presence and agency), realistic work-related tasks can be simulated in the classroom to enhance both transfer and learning (Makransky & Petersen, 2021; Wißhak, 2022). Moreover, IVR learning environments can support social interactions (Makransky & Petersen, 2023), which, from a social constructivist perspective, has been shown to positively impact learning outcomes and transfer (Gegenfurtner & Vauras, 2012). In conclusion, IVR emerges as a promising technology for enhancing learning transfer in the vocational education context.

2.4. State of Research

Studies investigating learning transfer following IVR-based training are predominantly found in the fields of corporate training, safety training, medicine, and military contexts, as shown in a meta-analysis (Howard et al., 2021). In these studies, the operationalization of actual transfer varies depending on the training context, which complicates comparability. Nevertheless, the overall results indicate that IVR-based training has a positive impact on transfer success (Howard et al., 2021).
However, it is evident that existing research on the effectiveness of IVR-assisted teaching and learning predominantly emphasizes knowledge acquisition, most commonly assessed through post-instruction test scores rather than examining transfer as a further critical outcome variable (Concannon et al., 2019; Conrad et al., 2024; Hamilton et al., 2021; Howard et al., 2021). The findings in this area suggest that IVR is advantageous for acquiring procedural knowledge compared to other media and methods (Conrad et al., 2024; Hamilton et al., 2021). However, the evidence regarding declarative knowledge acquisition remains inconclusive, with some studies indicating potential advantages over traditional analog teaching methods (Conrad et al., 2024).
As outlined in the previous chapter, self-efficacy, alongside knowledge acquisition, represents a key determinant of transfer (Huang et al., 2015; von Solga, 2011). Studies from various professional contexts suggest that IVR training can also promote self-efficacy in professional activities (Lowell & Tagare, 2023; Nissim & Weissblueth, 2017, 2024). In this context, current studies indicate that IVR-based training can not only facilitate knowledge transfer but also positively influence related determinants such as knowledge acquisition and self-efficacy. However, the current state of research shows that there are relatively few studies examining the learning effectiveness of IVR-integrated instruction in the context of VET (Thomann et al., 2024). Furthermore, existing studies focus primarily on knowledge acquisition with IVR, while other transfer determinants, such as transfer-related self-efficacy, receive less attention. Consequently, there is a lack of research regarding the use of IVR in the field of VET, especially with respect to bridging school and company-based training, where transfer plays an important role.
Referring to the relationships outlined in the CAMIL regarding the central IVR characteristics and the cognitive and affective factors that, in turn, promote knowledge acquisition and transfer, few studies have empirically validated the model to date. Petersen et al. (2022) demonstrate that presence is linked to several cognitive and affective factors. In contrast, agency only shows a connection with embodied learning. Regarding learning outcomes, self-efficacy, interest, and embodied learning were identified as key predictors. The study highlights that not all pathways proposed in the model can be empirically validated so far. However, further research is needed to examine these relationships in more detail, providing a comprehensive investigation of the model and potentially incorporating additional relevant factors (Makransky & Petersen, 2021).

3. Hypotheses

Previous findings demonstrate that IVR is a medium that can support knowledge transfer in VET. However, it remains unclear as to what extent IVR-based transfer facilitation is effective in school-based settings and whether IVR-specific characteristics, as outlined in the CAMIL, serve as predictive factors in this context. The present study aims to contribute further to the field by examining transfer facilitation in vocational school education, focusing on knowledge acquisition and transfer-related self-efficacy as predictors of actual transfer (Huang et al., 2015; von Solga, 2011; Wißhak, 2022). Given the limited empirical validation of the CAMIL to date, this study additionally aims to examine the relationships proposed in the model to identify IVR-specific predictors that influence knowledge acquisition and transfer-related self-efficacy. Based on the theoretical assumptions and the existing empirical findings outlined above, the following hypotheses are formulated:
Hypothesis 1 (H1).
There is a difference between IVR and non-IVR-integrated teaching in learning with regard to knowledge acquisition and transfer-related self-efficacy.
Hypothesis 2 (H2).
There are relationships between the IVR characteristics (agency and presence) and the cognitive and affective factors of the CAMIL (e.g., interest, motivation, general self-efficacy, embodiment, self-regulation, and cognitive load) in IVR-integrated teaching-learning settings.
Hypothesis 3 (H3).
There are relationships between the cognitive and affective factors of the CAMIL (e.g., interest, motivation, general self-efficacy, embodiment, self-regulation, and cognitive load) and the knowledge acquisition in IVR-integrated teaching-learning settings.
Hypothesis 4 (H4).
There are relationships between the cognitive and affective factors of the CAMIL (e.g., interest, motivation, general self-efficacy, embodiment, self-regulation, and cognitive load) and transfer-related self-efficacy in IVR-integrated teaching-learning settings.

4. Materials and Methods

4.1. Research Design and Sample

In order to empirically investigate these hypotheses, a quasi-experimental field study was conducted with retail apprentices at a German vocational school during the 2022/23 and 2023/24 school years. The sample consisted of 141 first-year retail apprentices from eight classes and was divided into an IVR and a non-IVR group to analyze the differences between IVR-based and non-IVR-based teaching regarding the formulated hypotheses. Four classes were assigned to the IVR group (n = 77), while the other four formed the non-IVR group (n = 64). This approach allows for a comparison of transfer-related effects between IVR-based and non-IVR-based learning units.
Table 1 shows that 72 trainees were male, and 55 were female. On average, the participants were 19.82 years old (SD = 2.66). In the IVR group, 49.3% of the trainees had no prior experience with the technology. Due to the grouping of the participants based on their existing classes, it was not possible to implement a randomized allocation of participants. For this reason, instruction was delivered by the regular subject teachers assigned to each class, which entailed the involvement of different teaching personnel.
The teaching and learning units focused on salesroom design covering topics, such as managing high- and low-turnover areas, using various display units, guiding customer flow, and applying product placement strategies. These subjects have traditionally been taught using analog materials such as worksheets and textbooks. For example, trainees typically use paper sketches of salesrooms to identify sales zones. However, this two-dimensional approach fails to reflect the real-world complexity of retail environments. By using IVR, the intervention aimed to overcome this limitation by providing interactive, immersive simulations of actual salesroom settings, thereby enabling a more hands-on, practice-oriented learning experience (Conrad et al., 2024).
The curriculum was divided into four key topics: sales zones, product carriers, routing in a supermarket, and product placement. These topics were selected due to their relevance for designing retail salesrooms. Accordingly, the intervention consisted of four 90 min lessons, with the instructional design developed and implemented by the participating teachers. All classes were conducted during regular school hours.
The following sections describe the teaching units on product presentation and the differences between the IVR and non-IVR groups in more detail.

4.1.1. IVR Group

The IVR learning environment was implemented using the WIS VR Toolbox, which simulates a realistic supermarket. Participants used Oculus Quest 2 headsets to interactively design sales areas, place shelves, and arrange products. A brief introduction to the controls and interactions was provided in an onboarding session beforehand. By allowing customization of the virtual salesroom to fit specific learning objectives (e.g., placing different products on shelves), the IVR setting enabled a hands-on, practice-based learning experience. Figure 2 provides an insight into the virtual supermarket.
The instructional design for the IVR group followed a consistent structure across all four lessons. Each unit began with the presentation of essential foundational information through worksheets and textbooks to ensure necessary background knowledge for all participants. The central part of each unit then took place in a virtual environment, where the participants worked mainly in a self-directed manner on tasks related to a specific topic. The participants, divided into small groups of three to four individuals, entered and collaborated in a virtual supermarket environment. The primary task for each group was to design and set up a virtual salesroom based on sales-promoting guidelines that were topic-specific for each unit. Group members worked simultaneously within the virtual setting, communicating via built-in earphones. On average, each of the four IVR-units took approximately 31 min (SD = 4.54 min) to be completed in the virtual environment. After each IVR session, the group gathered for an instructor-led analog debriefing session that included reflection tasks, feedback, and a class-wide discussion. This phase allowed students to present and analyze the results of their virtual salesrooms, fostering a deeper understanding of the concepts.

4.1.2. Non-IVR-Group

The non-IVR group was taught using traditional, analog methods, relying on paper- and textbook-based materials. Like the IVR group, the non-IVR group’s instruction was divided into four 90 min units, with the core content delivered via textbooks. Instead of virtual environments, participants worked with floorplans where they manually designed sales zones and different routing strategies for a salesroom. Tasks in the non-IVR group were also completed in small groups of three to four learners. At the end of each unit, the results were discussed and consolidated in a plenary session with the entire class. Accordingly, the difference between the two groups was in the mode of presentation and processing of the assigned tasks

4.2. Instruments

The data collection instruments consisted of several components, which are described in detail below. An overview of the sample items and the reliability values can be found in Table 2.
  • Relevant subject knowledge was assessed at two measurement times (before and after the intervention) using a specially developed test to examine the performance development between the two groups (IVR group and non-IVR group). The test included 26 items that covered content taught during the intervention. It was time-limited to 30 min and administered in a single-choice format. One point was awarded for each correct answer, resulting in a maximum score of 26 points. The content of the items focused on the four main topics: sales zones, product carriers, supermarket routing, and shelf placement. The reliability of the test was calculated using Item Response Theory (IRT) analysis, resulting in an EAP/PV reliability (expected a posteriori/plausible values) of 0.71. The EAP/PV reliability indicates how well the estimated person’s abilities reflect the individual’s actual abilities.
  • Additionally, a questionnaire was distributed to the IVR group immediately after the fourth lesson to assess the factors described in CAMIL. The items for IVR affordances (presence and agency) as well as cognitive and affective factors were adapted from the work of Petersen et al. (2022). In addition, the assessment of self-regulation was based on the work of Tiaden (2006), while the extraneous cognitive load was assessed according to Klepsch et al. (2017). All items were measured using 5-point Likert scales.
  • In order to obtain more insights regarding the transfer-enhancing potential of IVR, the measurement of transfer-related self-efficacy was used, as it can easily be indicated by the learners themselves. The assessment of transfer-related self-efficacy was conducted using four items based on the works of Kauffeld et al. (2009), Stäudel (1988), and Snyder et al. (1991). These items were also assessed using a 5-point Likert scale.

5. Results

The first hypothesis (H1) examines the group differences between the IVR and non-IVR group in terms of knowledge acquisition and transfer-related self-efficacy (see Table 3). In regard to the results of the subject test employed to assess knowledge acquisition, the findings were analyzed based on the percentage of correctly answered questions and, consequently, the percentage of points achieved (maximum of 26 points).
The IVR group demonstrates a higher mean score (M = 56.36; SD = 13.51) compared to the non-IVR group (M = 48.31; SD = 14.22), with a statistically significant difference (p = 0.03; d = 0.39). Furthermore, a significant mean difference can be observed in the dimension of transfer-related self-efficacy (p < 0.01; d = 0.58). The IVR group scored a mean of 3.66 (SD = 0.77) on the 5-point Likert scale, while the non-IVR group rated this dimension with a mean of M = 3.31 (SD = 0.94). The findings in both dimensions reflect small to moderate effect sizes. Overall, the results indicate that learners in the IVR group show better knowledge acquisition and higher transfer-related self-efficacy compared to those in the non-IVR group.
Regarding knowledge acquisition, an additional analysis incorporates the learners’ prior subject-specific knowledge (pretest) to evaluate the learning gain and investigate the potential interaction effect of group*time. This analysis aims to assess whether the development of test performance differs between the two groups over time. The results of the conducted repeated measures ANOVA reveal a significant main effect for both factors time (F [1,99] = 159.80; p < 0.01, η2p = 0.617) and group (F [1,99] = 4.17; p = 0.04, η2p = 0.040). Additionally, a significant interaction effect for time × group was identified (F [1,99] = 5.44; p < 0.02, η2p = 0.052), indicating a higher increase in test performance over time in favor of the IVR group (see Table 4).
Figure 3 illustrates the development in test performance over time for both the IVR and non-IVR groups, based on the percentage of correct answers at both measurement points. In the pretest, both groups show almost identical mean percentages of correct answers (IVR group M = 33.99%, SD = 12.04; non-IVR group M = 33.1%, SD = 12.70). However, by the time of the posttest, the IVR group reached a mean of 56.57% (SD = 13.76) correct answers, whereas the non-IVR group achieved 48.66% (SD = 14.05).
Taken together, the results for Hypothesis 1 (H1) present a consistent picture. Both test performance and transfer-related self-efficacy are significantly higher in the IVR group, confirming the hypothesis.
To address hypotheses 2–4 (H2–H4) regarding transfer-related predictors, the relationships described in the CAMIL were examined based on bivariate correlations within the IVR group. The results of the correlation analysis are presented in Table 5. Hypothesis 2 (H2) addresses the relationships between the IVR affordances (agency and presence) and the affective and cognitive factors of the model. The findings reveal that presence correlates significantly with the cognitive and affective factors, indicating significant correlations with interest (p < 0.01; r = −0.59), motivation (p < 0.01; r = −0.61), general self-efficacy (p = 0.04; r = −0.27), embodiment (p < 0.01; r = −0.62), and self-regulation (p = 0.01; r = −0.30). The relationship between presence and cognitive load is the only one that is not significant. In contrast, agency correlates positively with interest (p = 0.02; r = 0.29) and negatively with cognitive load (p < 0.01; r = −0.35). The analysis thus does not confirm all of the relationships postulated in the model, although both agency and the experience of presence are related to certain individual affective and cognitive factors.
Hypothesis 3 (H3) explores the relationships between the affective and cognitive factors of the CAMIL and the learning outcome, which was measured by posttest performance in this study. The analyses reveal that there are significant relationships with interest (p < 0.01; r = 0.39), motivation (p = 0.02; r = 0.29), and self-regulation (p = 0.04; r = 0.28). However, the relationships proposed in the model with general self-efficacy, embodiment, and cognitive load are not supported by the findings of this study.
Hypothesis 4 (H4) investigates the relationships between the affective and cognitive factors of the CAMIL and transfer-related self-efficacy. The findings demonstrate a robust correlation between transfer-related self-efficacy and several factors, including interest (p < 0.01; r = 0.53), motivation (p < 0.01; r = 0.50), general self-efficacy (p < 0.01; r = 0.78), and embodiment (p < 0.01; r = 0.48). A significant relationship with self-regulation is also observed (p = 0.02; r = 0.33). Contrary to the model-based assumptions, no significant relationship with cognitive load can be found.
Additionally, direct relationships are identified between presence and transfer-related self-efficacy (p = 0.01; r = 0.37) as well as between test performance (posttest) and agency (p < 0.01; r = 0.46). These direct relationships are not described in the CAMIL but are mentioned here for completeness.

6. Discussion

Due to the relevance of transfer-promoting teaching in VET and the particular potential of IVR to support knowledge transfer, this study investigated knowledge acquisition and transfer-related self-efficacy in the context of IVR-supported teaching for retail trainees (n = 141). Furthermore, IVR-specific characteristics were examined to determine possible correlations with learning success based on the CAMIL framework (Makransky & Petersen, 2021).
With regard to Hypothesis 1 (H1), the results indicate that the IVR group significantly outperformed the non-IVR group on both knowledge acquisition and transfer-related self-efficacy, with the effect sizes ranging from small to medium. These findings are consistent with the meta-analysis by Howard et al. (2021), which highlights the effectiveness of IVR in enhancing knowledge acquisition and promoting self-efficacy (Howard et al., 2021). However, while Howard et al. (2021) included studies focusing on IVR in workplace training and general education, the present study extends this research by exploring transferability of IVR-based instruction in vocational school settings. Specifically, the findings suggest that IVR’s transfer-enhancing potential may also extend to the knowledge transfer from vocational school to workplace training environments. Nevertheless, the methodological limitations of comparative media studies must be acknowledged when interpreting these results. The observed differences between the IVR- and control-groups may stem not only from the medium itself but also from broader methodological considerations (Buchner & Kerres, 2023). Despite the quasi-experimental design, which allowed variation only in task processing, it cannot be ruled out that the IVR-group experienced a higher level of practice orientation due to the immersive and interactive environment. This increased practice orientation may have contributed to improved test performance that was independent of the actual instructional content. Furthermore, the immersive and interactive nature of IVR may enhance learning efficiency by promoting sustained attention to the instructional material. By minimizing external distractions, IVR allows learners to fully immerse themselves in the learning experience. This distinctive characteristic of IVR—its ability to foster deep engagement—should be carefully considered when interpreting the results from IVR-based educational studies, and warrants further investigation in future research.
To identify predictors of IVR-supported transfer effectiveness, Hypotheses 2 to 4 (H2–H4) were tested using bivariate correlations, based on the relationships proposed in the CAMIL framework. The findings largely support the model’s assumptions: Presence was significantly associated with the affective and cognitive factors, with the exception of cognitive load, for which no significant relationship was observed. In contrast, agency was only correlated with interest and extraneous cognitive load, and did not show significant associations with motivation, embodiment, self-efficacy, or self-regulation. These results align with the findings of Petersen et al. (2022), who also reported that not all of the relationships proposed in the model could be empirically validated. Notably, both studies highlight that presence has significantly stronger associations with cognitive and affective factors than agency. Consequently, both studies emphasize that presence shows significantly stronger associations with affective and cognitive factors compared to agency, suggesting that presence may function as the primary mechanism underlying immersive learning processes. Additionally, the present study identified direct relationships between agency and posttest performance, as well as between presence and transfer-related self-efficacy—associations not explicitly formulated within the CAMIL framework. Therefore, future studies employing larger sample sizes and advanced multivariate analytical methods should investigate the relationships outlined in the CAMIL to empirically evaluate and refine its assumptions concerning learning outcomes.

7. Conclusions

This study’s examination of the relevant predictors of knowledge transfer (knowledge acquisition and transfer-related self-efficacy) indicates that the effective integration of IVR into instructional design can enhance knowledge transfer in vocational education. These findings suggest that IVR holds promise as a medium for promoting both learning and transfer in vocational contexts. However, the results also underscore the importance of instructional design. In particular, the post-IVR consolidation phase may play a critical role in reinforcing learning, indicating that future research should place greater emphasis on investigating instructional components.
The present study has several limitations that should be considered when interpreting the findings. First, the relatively small sample size limited the feasibility of conducting more sophisticated statistical analyses (e.g., structural equation modeling), particularly for validating the relationships proposed by the CAMIL model. Additionally, because the study was embedded within the regular school curriculum, random assignment of participants was not possible, which limits the generalizability of the results. Moreover, teacher effects also represent a potential source of bias, as the participating classes were taught by different teachers, which may have led to unintentional variations in instructional delivery. Further, the low reliability score in the agency scale should be considered when interpreting the findings, as it represents a limitation of the study. Another methodological limitation concerns the measurement of transfer. Due to the different company backgrounds and the frequent lack of opportunities to apply what they learned in their training companies, the conduciveness to transfer was measured using knowledge acquisition and transfer-related self-efficacy as key predictors of actual knowledge transfer. While these measures offer valuable insights, they cannot fully substitute for the direct assessment of observable transfer behaviors. Therefore, future research should examine transfer effects over longer periods and incorporate behavioral outcome measures in real workplace settings.
Despite these limitations, the present study provides valuable insights into the potential of IVR as an instructional tool in vocational education. From a research perspective, this study contributes to the empirical validation of the CAMIL model by confirming key theoretical assumptions while also identifying areas that warrant further investigation. In particular, the relative influence of presence and agency in immersive learning processes should be examined more thoroughly in future studies. Moreover, further studies should place greater emphasis on evaluating learning transfer at the behavioral level in order to gain a more comprehensive understanding of IVR’s long-term effects in vocational education. In this context, it is also important to consider additional factors and predictors that may influence successful knowledge transfer. For example, Wißhak (2022) emphasizes the critical role of training design in enabling effective transfer. Accordingly, future research should systematically investigate the instructional design conditions that contribute to the effectiveness of IVR-based learning environments. Particular attention should be given to identifying the contextual and pedagogical factors that enhance the educational impact of immersive technologies and to exploring how educators can meaningfully integrate IVR into their teaching practice. A thorough exploration of these dimensions is essential for developing evidence-based design principles and for promoting sustainable knowledge transfer in IVR-supported educational settings.

Funding

This research received no external funding.

Institutional Review Board Statement

Institutional Review Board (IRB) of the University of Konstanz IRB25KN006-01/w 2 June 2025.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Allcoat, D., & von Mühlenen, A. (2018). Learning in virtual reality: Effects on performance, emotion and engagement. Research in Learning Technology, 26, 1–13. [Google Scholar] [CrossRef]
  2. Bossard, C., Kermarrec, G., Buche, C., & Tisseau, J. (2008). Transfer of learning in virtual environments: A new challenge? Virtual Reality, 12(3), 151–161. [Google Scholar] [CrossRef]
  3. Buchner, J., & Kerres, M. (2023). Media comparison studies dominate comparative research on augmented reality in education. Computers & Education, 195, 104711. [Google Scholar] [CrossRef]
  4. Buehler, K., & Kohne, A. (2019). Lernen mit virtual reality: Chancen und möglichkeiten der digitalen aus-und fortbildung [Learning with virtual reality: Opportunities and potentials of digital initial and continuing education]. In M. Groß, M. Müller-Wiegand, & D. F. Pinnow (Eds.), Zukunftsfähige unternehmensführung: Ideen, konzepte und praxisbeispiele [Sustainable corporate management: Ideas, concepts, and practical examples] (pp. 209–224). Springer. [Google Scholar] [CrossRef]
  5. Cheng, E. W. L., & Ho, D. C. H. (2001). A review of transfer of training studies in the past decade. Personnel Review, 30(1), 102–118. [Google Scholar] [CrossRef]
  6. Concannon, B. J., Esmail, S., & Roduta Roberts, M. (2019). Head-mounted display virtual reality in post-secondary education and skill training. Frontiers in Education, 4, 80. [Google Scholar] [CrossRef]
  7. Conrad, M., Kablitz, D., & Schumann, S. (2024). Learning effectiveness of immersive virtual reality in education and training: A systematic review of findings. Computers & Education: X Reality, 4, 100053. [Google Scholar] [CrossRef]
  8. Gegenfurtner, A. (2011). Motivation and transfer in professional training: A meta-analysis of the moderating effects of knowledge type, instruction, and assessment conditions. Educational Research Review, 3(6), 153–168. [Google Scholar] [CrossRef]
  9. Gegenfurtner, A., & Vauras, M. (2012). Age-related differences in the relation between motivation to learn and transfer of training in adult continuing education. Contemporary Educational Psychology, 37(1), 33–46. [Google Scholar] [CrossRef]
  10. Granados Cannawurf, R. A. (2005). Trainings-transfer: Eine langzeitstudie der zugrunde liegenden prozesse [Training transfer: A long-term study of the underlying processes]. JLUpub. [CrossRef]
  11. Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2021). Immersive virtual reality as a pedagogical tool in education: A systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1–32. [Google Scholar] [CrossRef]
  12. Hellriegel, J., & Čubela, D. (2018). Das potenzial von virtual reality für den schulischen unterricht: Eine konstruktivistische sicht [The potential of virtual reality for classroom instruction: A constructivist perspective]. MedienPädagogik, Occasional Papers, 58–80. [Google Scholar] [CrossRef]
  13. Hense, J., & Mandl, H. (2011). Transfer in der beruflichen weiterbildung [Transfer in vocational continuing education]. In O. Zlatkin-Troitschanskaia (Ed.), Stationen empirischer bildungsforschung: Traditionslinien und perspektiven [Milestones of empirical educational research: Traditions and perspectives] (pp. 249–263). VS Verlag. [Google Scholar] [CrossRef]
  14. Howard, M. C., Gutworth, M. B., & Jacobs, R. R. (2021). A meta-analysis of virtual reality training programs. Computers in Human Behavior, 121, 106808. [Google Scholar] [CrossRef]
  15. Huang, J. L., Blume, B. D., Ford, J. K., & Baldwin, T. T. (2015). A tale of two transfers: Disentangling maximum and typical transfer and their respective predictors. Journal of Business and Psychology, 30(4), 709–732. [Google Scholar] [CrossRef]
  16. Kablitz, D., Conrad, M., & Schumann, S. (2023). Immersive VR-based instruction in vocational schools: Effects on domain-specific knowledge and wellbeing of retail trainees. Empirical Research in Vocational Education and Training, 15(1), 9. [Google Scholar] [CrossRef]
  17. Kauffeld, S., Brennecke, J., & Strack, M. (2009). Erfolge sichtbar machen: Das maßnahmen-erfolgs-inventar (MEI) zur Bewertung von trainings [Making success visible: The measures success inventory (MEI) for the evaluation of training programs]. In S. Kauffeld, S. Grote, & E. Frieling (Eds.), Handbuch kompetenzentwicklung [Handbook of competence development] (pp. 55–78). Schaffer-Poeschel. [Google Scholar]
  18. Kim, K. G., Oertel, C., Dobricki, M., Olsen, J. K., Coppi, A. E., Cattaneo, A., & Dillenbourg, P. (2020). Using immersive virtual reality to support designing skills in vocational education. British Journal of Educational Technology, 51(6), 2199–2213. [Google Scholar] [CrossRef]
  19. Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 16(8), 1997. [Google Scholar] [CrossRef] [PubMed]
  20. Lee, I. J. (2020). Applying virtual reality for learning woodworking in the vocational training of batch wood furniture production. Interactive Learning Environments, 31(3), 1448–1466. [Google Scholar] [CrossRef]
  21. Loke, S. K. (2015). How do virtual world experiences bring about learning? A critical review of theories. Australasian Journal of Educational Technology, 31(1), 112–122. [Google Scholar] [CrossRef]
  22. Lowell, V. L., & Tagare, D. (2023). Authentic learning and fidelity in virtual reality learning experiences for self-efficacy and transfer. Computers & Education: X Reality, 2, 100017. [Google Scholar] [CrossRef]
  23. Makransky, G., & Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33(1), 937–958. [Google Scholar] [CrossRef]
  24. Makransky, G., & Petersen, G. B. (2023). The theory of immersive collaborative learning (TICOL). Educational Psychology Review, 35(4), 103. [Google Scholar] [CrossRef]
  25. Marougkas, A., Troussas, C., Krouska, A., & Sgouropoulou, C. (2023). Virtual reality in education: A review of learning theories, approaches and methodologies for the last decade. Electronics, 12(13), 2832. [Google Scholar] [CrossRef]
  26. Moore, J. W., & Fletcher, P. C. (2012). Sense of agency in health and disease: A review of cue integration approaches. Consciousness and Cognition, 21(1), 59–68. [Google Scholar] [CrossRef] [PubMed]
  27. Mulders, M., Buchner, J., & Kerres, M. (2022). Virtual reality in vocational training: A study demonstrating the potential of a VR-based vehicle painting simulator for skills acquisition in apprenticeship training. Technology Knowledge and Learning, 29(2), 697–712. [Google Scholar] [CrossRef]
  28. Nissim, Y., & Weissblueth, E. (2017). Virtual reality (VR) as a source for self-efficacy in teacher training. International Education Studies, 10(8), 52–59. [Google Scholar] [CrossRef]
  29. Nissim, Y., & Weissblueth, E. (2024). Virtual reality as a vehicle to transform teachers’ personal self-efficacy into professional self-efficacy. Cogent Education, 11(1), 2372155. [Google Scholar] [CrossRef]
  30. Petersen, G. B., Petkakis, G., & Makransky, G. (2022). A study of how immersion and interactivity drive VR learning. Computers & Education, 179, 104429. [Google Scholar] [CrossRef]
  31. Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. [Google Scholar] [CrossRef]
  32. Raja, M., & Lakshmi Priya, G. G. (2020). Factors affecting the intention to use virtual reality in education. Psychology and Education, 57(9), 2014–2022. [Google Scholar]
  33. Schwan, S., & Buder, J. (2006). Virtuelle realität und E-learning [Virtual reality and E-learning]. Goethe-Universität Frankfurt. [Google Scholar]
  34. Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., Yoshinobu, L., Gibb, J., Langelle, C., & Harney, P. (1991). The will and the ways: Development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60(4), 570–585. [Google Scholar] [CrossRef] [PubMed]
  35. Stäudel, T. (1988). Der kompetenzfragebogen—Überprüfung eines verfahrens der selbsteinschätzung der heuristischen kompetenz, belastender emotionen und verhaltenstendenzen beim lösen komplexer probleme [The competence questionnaire—Evaluation of a self-assessment method for heuristic competence, stressful emotions, and behavioral tendencies in solving complex problems]. Diagnostica, 34, 136–148. [Google Scholar]
  36. Thomann, H., Zimmermann, J., & Deutscher, V. (2024). How effective is immersive VR for vocational education? Analyzing knowledge gains and motivational effects. Computers & Education, 220, 105127. [Google Scholar] [CrossRef]
  37. Tiaden, C. (2006). Selbstreguliertes lernen in der berufsbildung: Lernstrategien messen und fördern [Self-regulated learning in vocational education and training: Measuring and promoting learning strategies]. Available online: https://edudoc.ch/record/17453?ln=de (accessed on 21 July 2025).
  38. von Solga, M. (2011). Förderung von lerntransfer [Promoting learning transfer]. In J. Ryschka, M. Solga, & A. Mattenklott (Eds.), Praxishandbuch personalentwicklung: Instrumente, konzepte, beispiele [Practical handbook of human resource development: Instruments, concepts, examples] (3rd ed., pp. 339–368). Gabler. [Google Scholar] [CrossRef]
  39. Wirth, K. (2013). Verknüpfung schulischen und betrieblichen lernens und lehrens–erfahrungen, einstellungen und erwartungen der akteure dualer ausbildung [Linking school-based and workplace learning and teaching—Experiences, attitudes, and expectations of stakeholders in dual vocational education and training]. bwp@ Spezial, 1–19. [Google Scholar]
  40. Wißhak, S. (2022). Transfer in der berufsbezogenen weiterbildung: Systematisches literaturreview und synthese mit blick auf die handlungsmöglichkeiten der lehrenden [Transfer in vocational continuing education: A systematic literature review and synthesis with a focus on teachers’ options for action]. Zeitschrift für Weiterbildungsforschung, 45(1), 69–88. [Google Scholar] [CrossRef]
  41. Zender, R. (2020). Workshop HandLeVR 2020. In Proceedings of DELFI workshops 2020. Gesellschaft für Informatik eVz. [Google Scholar]
  42. Zinn, B. (2019). Lehren und lernen zwischen virtualität und realität [Teaching and learning between virtuality and reality]. Journal of Technical Education, 7(1), 17–31. [Google Scholar] [CrossRef]
Education 15 00959 g001
Figure 1. Cognitive Affective Model of Immersive Learning (CAMIL) illustration based on (Makransky & Petersen, 2021, p. 943).
Figure 1. Cognitive Affective Model of Immersive Learning (CAMIL) illustration based on (Makransky & Petersen, 2021, p. 943).
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Figure 2. Screenshots of the virtual salesroom.
Figure 2. Screenshots of the virtual salesroom.
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Figure 3. Percentage of correct answers in the pretest and posttest.
Figure 3. Percentage of correct answers in the pretest and posttest.
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Table 1. Socio-demographic characteristics of the sample.
Table 1. Socio-demographic characteristics of the sample.
IVR Group
(n = 77)		Non-IVR Group
(n = 64)		Total
(N = 141)
Gender
 Malen45 (58.4%)27 (42.2%)72 (51.1%)
 Femalen28 (36.4%)27 (42.2%)55 (39.00%)
 n.a.n4 (5.2%)10 (15.6%)14 (9.9%)
AgeM (SD)19.58 (2.90)20.15 (2.26)19.82 (2.66)
Table 2. Overview of measured variables.
Table 2. Overview of measured variables.
Scales (No. of Items)M (SD)ReliabilityExample Item
Domain-specific knowledge EAP/PV
Pretest (26) 18.66 (3.27)0.62Which of the following areas is typically found in a salesroom?
Posttest (26) 113.58 (4.72)0.76What is understood by the term “secondary placement”?
Further Variables * α
Agency (4)4.04 (0.75)0.68During the lesson, my experiences and actions were under my control.
Physical Presence (5)3.51 (0.92)0.90The virtual environment seemed real to me.
Academic self-efficacy (4)4.13 (0.86)0.94I’m confident I can understand the basic concepts of salesroom design.
Situational interest (6)4.12 (0.84)0.93The lesson captured my attention.
Motivation (5)4.03 (0.84)0.92I enjoyed working on the topic of salesroom design.
Embodiment (3)3.31 (1.15)0.91Performing gestures/movements during the lesson helped me learn.
Extraneous cognitive load (3)2.21 (0.90)0.82During the lessons it was difficult to link the most important content together.
Self-regulation (3)3.71 (0.84)0.78While working on the task, I kept asking myself whether I could improve my approach.
Transfer-related self-efficacy (4)3.50 (0.81)0.79I have the confidence to apply what I have learned to my work.
M = mean, SD = standard deviation, α = Cronbach’s alpha. * 5-point Likert scale (strongly disagree–strongly agree); 1 max. score 26 points.
Table 3. Differences between IVR and non-IVR group.
Table 3. Differences between IVR and non-IVR group.
IVR Group
(n = 77)		Non-IVR Group
(n = 64)
Dimension M (SD)pd
Transfer-related self-efficacy 13.66 (0.77)3.34 (0.82)0.0360.39
Knowledge acquisition (posttest) 256.36 (13.51)48.31 (14.22)0.0020.58
d Cohen’s d; 1 5-point Likert-scale; 2 mean values in percent.
Table 4. Repeated Measures ANOVA.
Table 4. Repeated Measures ANOVA.
(DV: Posttest Score)FpPartial Eta Squared
Time159.79<0.0010.617
Group (IVR/non-IVR)4.170.0440.040
Time × group5.44<0.0220.052
DV = dependent variable.
Table 5. Correlations among CAMIL dimensions.
Table 5. Correlations among CAMIL dimensions.
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)
Transfer-rel. self-eff. (1)--0.100.200.37 *0.53 **0.50 **0.78 **0.48 **−0.200.33 *
Posttest (2) --0.46 **0.000.39 **0.29 *0.200.04−0.090.28 *
Agency (3) --0.220.29 *0.150.230.12−0.35*0.04
Presence (4) --0.59 **0.61 **0.27 *0.62 ** 0.030.30 *
Interest (5) --0.89 **0.60 **0.49 **−0.110.45 **
Motivation (6) --0.51 **0.50 **−0.080.35 **
Self-efficacy (7) --0.29 *–0.24 *0.41 **
Embodiment (8) --−0.040.23
Cognitive load (9) --0.14
Self-regulation (10) --
* p < 0.05, ** p < 0.001.
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Kablitz, D. Bridging Theory and Practice with Immersive Virtual Reality: A Study on Transfer Facilitation in VET. Educ. Sci. 2025, 15, 959. https://doi.org/10.3390/educsci15080959

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Kablitz D. Bridging Theory and Practice with Immersive Virtual Reality: A Study on Transfer Facilitation in VET. Education Sciences. 2025; 15(8):959. https://doi.org/10.3390/educsci15080959

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Kablitz, David. 2025. "Bridging Theory and Practice with Immersive Virtual Reality: A Study on Transfer Facilitation in VET" Education Sciences 15, no. 8: 959. https://doi.org/10.3390/educsci15080959

APA Style

Kablitz, D. (2025). Bridging Theory and Practice with Immersive Virtual Reality: A Study on Transfer Facilitation in VET. Education Sciences, 15(8), 959. https://doi.org/10.3390/educsci15080959

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