Profiling Students by Perceived Immersion: Insights from VR Engine Room Simulator Trials in Maritime Higher Education

Abstract

Research on students’ immersive experiences with fully immersive virtual reality (VR) technologies is extensively documented across diverse educational settings; however, in maritime higher education, it remains relatively underrepresented. Therefore, by using segmentation analysis, this study aims to profile maritime engineering students at the Faculty of Maritime Studies, University of Rijeka, by perceived immersion (PIMM) within a Head-Mounted Display (HMD) VR engine room simulator and to explore differences in their perceived learning benefits (PLBs), future behavioural intentions (FBI), and satisfaction (SAT) with the HMD-VR experience. The sample comprised 84 participants who engaged in preliminary HMD-VR engine room simulator trials. A non-hierarchical (K-mean) cluster analysis, combined with the Elbow method, identified two distinct and homogeneous groups: Immersionists and Conformists. The results of an independent sample t-test indicated that Immersionists exhibited significantly higher scores regarding perceived learning benefits, future behavioural intentions, and overall satisfaction than Conformists. The study results underscore the significance of understanding students’ subjective perception of immersion in the implementation and further development of fully immersive VR technologies within maritime education and training (MET) curricula. However, as the study is based on a specific case within a particular educational context, the result may not directly apply to the broader student population.

1. Introduction

Shipping is an international business that employs about 1.9 million seafarers whose competencies play an indispensable role in maintaining safety and mitigating risks at sea [1]. Thus, the continuous investment in advanced technologies, such as HMD-VR simulators, and their incorporation into maritime education and training programmes is deemed a critical challenge to maintain a competitive edge in the shipping industry as a global economic leader [2,3,4].

Fully immersive virtual reality refers to monitoring a user’s movements and delivering VR content according to their location through an HMD [5,6]. The continuing drop in VR equipment prices, computational capacity advancements, and simulation technology enhancements have augmented the application of HMD-VR technology across various sectors, including entertainment (e.g., [7,8]), medicine and healthcare (e.g., [9,10]), tourism and hospitality (e.g., [11,12]), engineering (e.g., [13,14]), logistics (e.g., [15]), aviation (e.g., [16]), and maritime industry [17,18,19].

Implementing HMD-VR technologies in education and training has also become an interesting subject of an increasing body of research (e.g., [18,20,21,22,23,24,25]). The findings, though mixed, are encouraging, showing that HMD-VR simulators have the potential to provide numerous benefits to students, including boosting engagement [26], providing greater control over the learning process [27], improving knowledge retention [18,28,29], facilitating skill acquisition and problem-solving [30,31], deepening the understanding of previously learned concepts and boosting learning performance [27,32], and enhancing critical thinking [33]. A primary advantage of HMD-VR simulators as educational tools lies in their elevated perceptual fidelity and capacity to elicit a genuine sense of immersion [34], enabling end-users to interact with VR environments to an extent that closely resembles real-world interactions [26]. In the context of Educational Virtual Environments (EVE), perceived immersion (PIMM) has been highlighted as one of the key factors in influencing end users’ attitudes toward VR-based learning, future engagement and shaping positive experiences with VR technology [20,35,36,37,38,39,40].

Although research on HMD-VR technology in maritime education and training is well documented (e.g., [4,6,17,19,41,42]), studies specifically investigating affective experiences (e.g., perceived immersion) associated with HMD-VR usage remain limited [18,32]. Therefore, this study employs HMD-VR engine room simulator trials at the Faculty of Maritime Studies at the University of Rijeka, Croatia, as a case study to address this research gap. Specifically, it aims to investigate the extent to which perceived immersion shapes maritime engineering students’ (i) attitudes toward learning benefits, (ii) future behavioural intentions, and (iii) overall satisfaction with the HMD-VR engine room experience. Furthermore, drawing upon the work of [32], this study implements segmentation analysis utilising perceived immersion as a basis for classification. By grouping students into distinct segments, the study aims to generate deeper insights into underlying dynamics within each sub-group, thereby contributing to a more nuanced understanding of the role of affective experience in adopting VR technology in maritime education and training.

In line with the study objectives, the following research questions are formulated:

• Is perceived immersion a reliable criterion for segmenting maritime engineering students in the context of HMD-VR engine room simulator education and training?
• How significantly does perceived immersion differentiate maritime engineering students regarding their attitudes toward learning benefits, future usage intentions and overall satisfaction with the HMD-VR engine room simulator?

2. Literature Review and Theoretical Framework

2.1. Perceived Immersion

The concept of immersion is frequently regarded as the objective level of sensory fidelity offered by a VR system [43,44]. However, when perceived as a subjective experience, it reflects the extent to which the user feels physically (sensory) and mentally absorbed in the virtual world experience [35,36,45]. This paper focuses exclusively on subjectively perceived immersion, utilising the term immersion defined by [46]: “a psychological state characterised by perceiving oneself to be enveloped by, included in, and interacting with an environment that provides a continuous stream of stimuli and experiences”. Though perceived immersion and presence are often used as having the same meaning and origin, the latter is associated with a psychological state in which users recall virtual reality experiences as though they occurred in reality (e.g., [47,48]).

The influence of perceived immersion on various attitudinal, behavioural and experiential constructs has been extensively studied in the context of Educational Virtual Environments [20,24,35,49]. The empirical findings of different studies suggest that when evaluated as a subjective experience, immersion can serve as a significant predictor of various outcomes, including attitudes toward perceived learning benefits [36,37,49], future intentions to engage with VR technology [35], and overall satisfaction [38,45].

For instance, ref. [36] utilised immersive virtual field trips for science learning to investigate the relationship between students’ perceived immersion and the advantages of VR learning. They categorised perceived immersion into basic attention, temporal dissociation, transportation, emotional involvement, and enjoyment, observing that students’ immersive experiences, such as their level of attention, positively predict their attitudes towards VR-based learning.

Employing Structural Equation Modelling (SEM) [35] examined the associations between self-efficacy, perceived immersion, and intention to use VR training systems among high school students in Western China. Among others, they discovered that the student’s intention to use VR training systems in the future was directly affected by the quality of the immersion experience.

The study [37] applied a constructivist approach to understanding university students’ behaviour when engaged in a VR learning environment. Their research findings indicated a positive correlation between perceived immersion and students’ problem-solving capability, collaborative learning, and the intention to utilise VR technology in the future.

Using a flow-based conceptualisation of immersion, ref. [45] found that the experience quality (i.e., satisfaction) with VR technology is directly linked to the perceived immersion experiences, indicating that immersion, viewed as a subjective experience, stands as a reliable element in understanding users’ overall experience in the context of VR-based learning.

Similarly, ref. [38] investigated the potential of VR technology for environmental education at the Marine Life Center in the West of France. They discovered that perceived immersion played a significant role in shaping visitors’ experience quality and future behavioural intentions (e.g., recommending VR to others).

2.2. HMD-VR Simulators in Maritime Engineering Training and Education

In MET institutions, conventional semi-immersive and non-immersive Maritime Engine Room Simulators (MERSs) have been considered effective learning tools for educating and training maritime engraining students and officers for almost half a century [19,50]. Due to their numerous educational and training benefits, such as enhancing knowledge comprehension, improving information assimilation, and developing incident management and stress-coping skills in a risk-free environment [51,52,53,54], their use in maritime training has been regulated by the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), 1978, as amended [55,56]. However, given their relatively high costs, restricted immersive experiences and dependence on physical infrastructure and training personnel [50,56], recently, there has been a growing interest in the gradual integration of fully immersive VR technologies [17,32,41]. One notable example is the HMD-VR engine room simulator, developed by the Faculty of Maritime Studies at the University of Rijeka, Croatia [6,42]. Representing a new generation of fully immersive HMD-VR technology, this engine room simulator is expected to enhance maritime engineering training by providing students with time- and space-independent, interactive, experiential, and personalised learning sessions. Its key feature lies in its ability to enable students to simultaneously execute complex diagnostic procedures within various training scenarios that would otherwise be challenging, costly, or hazardous to replicate in real onboard settings [42].

Despite the growing interest of the scientific community in understanding the potential of HMD-VR technology in maritime higher education and training programmes, studies that have specifically investigated maritime engineering students and their experiences with fully immersive VR technology are still relatively scarce.

A pilot study [6] surveyed maritime engineering students during preliminary trials of the HMD-VR engine room simulator. It found that students who had never used the HMD-VR technology showed an optimistic standpoint when asked about the potential of implementing HMD-VR technology in maritime higher education curricula. Furthermore, the findings indicated that students perceived HMD-VR as more beneficial for improving learning effectiveness than traditional MERS.

Based on the work of [32], which explored the relationship between perceived ease of use (PEU), learning benefits and satisfaction among maritime engineering students who engaged in HMD-VR engine room simulator trials. Their study revealed that students who found the simulator more intuitive and user-friendly exhibited more positive attitudes toward its learning benefits, expressed greater intention to use it in the future and reported higher satisfaction with the overall VR experience.

2.3. Market Segmentation in Education

Market segmentation refers to categorising a population into distinct and internally homogenous sub-groups based on shared characteristics, including socio-demographics, attitudes, or behavioural traits [57,58]. For segmentation to be effective, the identified groups must reflect high quantifiability, stability, and internal similarity [57,59,60]. There are two primary segmentation approaches. A priori (common-sense) segmentation is based on predefined criteria (i.e., interest group or socio-demographic variables), where the segments are determined based on prior knowledge. This approach is regarded as more straightforward due to its lack of methodological limitations [61]. In contrast, a posteriori approach is utilised when no predefined group exists, thus requiring data-driven techniques (i.e., factor analysis or cluster analysis) to uncover naturally occurring segments [57,62]. For this study, the first phase entailed selecting maritime engineering students from the Faculty of Maritime Studies at the University of Rijeka, Croatia, following the common-sense approach. In a subsequent phase, a data-driven approach was adopted by rating the student experiences with the perceived immersion of the HMD-VR engine room simulator.

Market segmentation has been used as an effective management and decision-making strategic tool in different domains, including tourism and leisure [63,64], e-commerce and retailing [65], banking [66], sports industry [67], and medicine and healthcare [68]. Given the increasing competitiveness among higher education institutions in recent decades, market segmentation studies have become essential for positioning themselves effectively in the educational market [69]. To date, a variety of segmentation bases have been employed to distinguish the heterogeneity of the student population, including rational/emotional factors [70], perceived service quality expectations and requirements [71,72], attitudes toward social media [73], behavioural traits [74], and socio-demographic [75].

While most of the abovementioned studies have primarily been oriented toward student recruitment, this paper draws attention by employing a technology-driven approach to segmentation, using perceived immersion—one of the key subjective experiences provided by VR technology—as the foundation for student classification. Despite continuous investment in VR technologies across various educational domains [20], including maritime education and training [19], there remains a limited understanding of the potential of affective variables, such as perceived immersion, as a reliable foundation for profiling students’ attitudes toward learning outputs and future intentions of using VR technology. To the best of the authors’ knowledge, the only existing study that has explored the heterogeneity of student population in the context of VR-based maritime education is [32], which employed the Technology Acceptance Model (TAM) component perceived ease of use as the basis for classification. By extending this research to perceived immersion, this study aims to provide new insights into the potential of using affective variables as the classification criteria to distinguish student attitudes toward learning benefits, engagement, and adoption of HMD-VR technology in maritime higher education.

3. Materials and Methods

3.1. Hardware and Software

For the trial, the Meta Quest 2 (256 GB) virtual reality headset was employed, four equivalent VR headsets were used, each equipped with the corresponding controllers. Due to the specific requirements of the developed VR simulator and the limited hardware capabilities of standalone VR headsets, the devices themselves were incapable of running the simulation independently. To address this, each VR headset was wirelessly connected to a computer equipped with a dedicated graphics card to render the VR simulator effectively. This configuration allowed the headset to transmit control inputs and receive rendered video wirelessly, eliminating the need for a tethered connection and thereby providing greater freedom of movement for participants. Each computer was equipped with a wireless connectivity card, enabling it to establish a wireless hotspot for its respective VR headset. This setup helped optimise video quality and response time for an enhanced immersive experience compared to using a router or a network access point as the wireless network source. A visual representation of the VR laboratory, the equipment used, and the participant familiarisation process is shown in Figure 1.

The specifications of all the computers used to run the VR simulator, in conjunction with the Meta Quest 2 headsets, are detailed in

Table 1. Specifications of the Personal Computers (PCs) used in the study (all the used Central Processing Units (CPUs) and Graphics Processing Units (GPUs) were branded Intel and NVIDIA, respectively, while the Random Access Memory (RAM) was of various brands).

	CPU			RAM			GPU
PC	Gen.	Model	Frequency	Capacity	Type	Speed	Model	VRAM
1	13th	Core i7-13700F	2.10 GHz	32 GB	DDR5-4800	2400 MHz	GeForce RTX 4070 Ti	12 GB
2	13th	Core i7-13700F	2.10 GHz	32 GB	DDR5-4800	2400 MHz	GeForce RTX 4070 Ti	12 GB
3	13th	Core i7-13700F	2.10 GHz	32 GB	DDR5-4800	2400 MHz	GeForce RTX 3080	10 GB
4	12th	Core i7-12700F	2.10 GHz	8 GB	DDR4-2400	1200 MHz	GeForce RTX 4070 Ti	12 GB

. All the computers operated on a 64-bit Windows 10 Pro Version 22H2 operating system and used an Intel Wi-Fi 6E AX210 160 MHz wireless card. The wireless card was configured to broadcast a hotspot on the 5 GHz frequency band, leveraging its higher bandwidth. However, despite the Wi-Fi 6E AX210 supporting a channel width of 160 MHz, the Meta Quest 2 hardware is limited to only 80 MHz on the 5 GHz Wi-Fi spectrum, thereby restricting the maximum possible bandwidth usable. To ensure the best possible experience given the hardware limitations, the Meta Quest 2 Air Link functionality was utilised at its maximum possible bitrate of 200 Mbps.

The HMD-VR Engine Room simulator was developed using the 3D modelling software Blender 4.2 and the Unreal Engine 5.4 game engine. The ship’s engine room model was developed based on actual blueprints from a roll-on/roll-off (RO-RO) vessel and designed as a double-deck structure measuring 16.1 m × 19.6 m. The lower deck contains the two main four-stroke diesel engines with shaft generators and propeller shafts, along with three diesel generators, cooling systems, pipelines, valves, pumps, and other general equipment. The upper deck contains two starting air compressors, one service air compressor, two heavy fuel oil purifiers, a diesel oil purifier, lubricating oil and fuel pipelines, and tanks and valves.

The simulator was designed to be compatible with Windows operating systems and supports multiple VR headsets and systems, utilising both wireless and wired connections. Among its notable features are (i) X-ray vision, enabling users to inspect the equipment’s internal components; (ii) simultaneous interactions with various tools and equipment, such as running the engines, taking cylinder pressure measurements, valve manipulations, and fire extinguisher priming and utilisation; and (iii) multiple exercise scenarios such as fire, flood blackout, and evacuation (Figure 2 and Figure 3).

3.2. Sample

The study population included 84 students from the Faculty of Maritime Studies at the University of Rijeka, encompassing undergraduate, graduate, and special education maritime engineering programmes. Most of the sample (65.5%) comprised graduate and undergraduate engineering students. Those participating in special education maritime engineering programmes constituted 34.5% of the sample. All the participants were male and aged between 18 and 46 (Mdn = 22). Regarding the previous experience with the VR environment, for 73.8% of the participants, this was the first time they had experience with HMD-VR for educational purposes, 25% reported using it 1 to 5 times in their lifetime, and 1.2% had used it monthly.

3.3. Data Collection and Procedure

The sampling was conducted at the recently opened VR laboratory at the Faculty of Maritime Studies, University of Rijeka, from 15 May to 15 July and from 1 September to 14 October 2024.

Before and/or after lessons, students were invited to visit the VR laboratory and willingly familiarise themselves with the HMD-VR engine room simulator and participate in the trials and subsequent surveys. Individuals interested in participating received a 25-min oral introduction outlining the study’s objectives and providing an overview of the VR engine room simulator’s features. A lab member demonstrated the simulator’s basic features and capabilities using a VR headset and a wall projector, enabling the participants to familiarise themselves with the simulator in real time (Figure 1). Following the introductory session, the participants underwent a brief tutorial to test and experience the simulator first-hand under the supervision of lab staff members. Subsequently, they were allowed to independently engage in a series of trial exercises, with each participant spending an average of 20 min to complete the trials. Upon the completion of the trials, each participant was requested to complete a post-use questionnaire to assess their experiences with the HMD-VR engine room simulator. Data collection was facilitated through a computer-based self-administered questionnaire using the Lime Survey software (6.3.5). On average, students required approximately 10 min to complete the questionnaire. The respondents had a markedly high propensity for collaboration, as all 84 collected questionnaires were deemed suitable for further analysis. The introduction, trial, and survey procedure are shown in Figure 4.

3.4. Instrument Design

Though the original questionnaire consisted of eight main sections, for this study, four sections were used for analysis: (1) perceived immersion, (2) perceived learning benefits, (3) future behavioural intentions, and (4) satisfaction with HMD-VR experience. In sections one, two, and three, the students were asked to respond to each statement using a 5-point Likert scale, where one represents “strongly disagree” and five means “strongly agree”. Perceived immersion items (three) were adopted from [76]. Perceived learning benefits items (four) were adopted from [35,77]. Future behavioural intentions items (three) were adopted from [35]. All the items were accordingly adapted to align with the current research context. Overall satisfaction was assessed with a self-developed single item (i.e., “In general, how satisfied are you with your HMD-VR engine room experience?”) on a 5-point Likert scale, ranging from one (very unsatisfied) to five (very satisfied).

3.5. Data Analysis

The data were processed and analysed using the IBM Statistical Package for the Social Sciences 23.0 (SPSS). The K-means clustering procedure was used with the Elbow method to determine the optimal number of clusters. An independent sample t-test was performed to examine differences among the segments related to PLB, FBI, and SAT at the item level. The assumption of homogeneity of variances was satisfied, as indicated by Levene’s test (p > 0.05). For normality, skewness and kurtosis tests were used. The values satisfied the cut-off level of ±2.58 [78,79]. The data had no outliers, as assessed by inspecting a boxplot for values greater than 1.5 [79]. Internal consistency for all three constructs received satisfactory alpha values (α ≥ 0.7, [79,80]): perceived immersion (α = 0.7), perceived learning benefits (α = 0.84), and future behavioural intentions (α = 0.82).

4. Results

The K-mean clustering algorithm combined with the Elbow method was employed to determine the optimal number of clusters. The analysis considered solutions ranging from 2 to 6 clusters. The Within-Cluster Sum of Squares (WCSS) values for different cluster solutions were as follows: K = 2 (0.761), K = 3 (0.605), K = 4 (0.578), K = 5 (0.44), and K = 6 (0.372). The Elbow method indicated that the most significant reduction in WCSS occurred between K = 2 and K = 3. After evaluating two- and three-cluster solutions, the two-cluster solution was deemed most acceptable regarding segment stability and interpretability, as shown in Figure 5. Namely, given the relatively small sample (N = 84), increasing the number of clusters would result in groups with few cases, potentially reducing statistical power and making both the finding interpretation and applicability to the educational context less meaningful [81]. The first segment, designated as Conformists, constituted 22.6% of the sample. This group primarily comprised students who consistently scored below the mean (negative z-scores) across all three items of perceived immersion. The second group, identified as Immersionists, constituted 77.4% of the total sample. Within this group, the z-scores for all the perceived immersion items were positive (i.e., above the total mean). A t-test of independent samples demonstrated that the segments’ PIMM mean scores differed significantly at the probability level of p < 0.001, as shown in

Table 2. Characterisation of the PIMM-based segments.

	Segments
	Conformists		Immersionists		df	t	η2	p-Value
	M	SD	M	SD
Interaction naturalness with the virtual environment	3.00	0.47	3.94	0.43	82	8.21	0.45	<0.001
Real-world experience consistency	2.95	0.23	3.97	0.53	82	8.16	0.44	<0.001
Degree of virtual experience involvement	3.58	0.69	4.17	0.55	82	3.89	0.16	<0.001

.

The differences between segments in the perceived learning benefits, future behavioural intention, and overall experience are illustrated in

Table 3. Segment differences in perceived learning benefits, future behavioural intention, and satisfaction.

	Segments
	Conformists		Immersionists		df	t	η2	p-Value
	M	SD	M	SD
Perceived Learning Benefits (PLB)
Comprehension of the previously acquired knowledge	3.63	0.60	4.23	0.52	82	4.25	0.18	<0.001
Stimulation of responsiveness and active learning	3.74	0.65	4.14	0.58	82	2.58	0.07	0.012
Control over the learning process	3.53	0.70	3.94	0.73	82	2.20	0.06	0.031
Improving effectiveness in learning	3.63	0.76	4.03	0.68	82	2.19	0.06	0.032
Future Behavioural Intention (FBI)
Willingness for future use	3.47	0.70	4.17	0.98	82	3.83	0.15	<0.001
Willingness to use VR in studying	3.58	0.96	4.00	0.75	82	2.02	0.05	0.047
Willingness to recommend VR as a learning tool	4.00	0.88	4.43	0.61	82	2.43	0.07	0.017
Satisfaction (SAT)
Satisfaction	4.26	0.56	4.68	0.47	82	3.22	0.11	0.002

. The results of the t-test analysis showed that Immersionists exhibited statistically significantly higher mean scores for all the perceived learning benefits, future behavioural intentions, and overall experience items. Among them, the most significant discrepancy among segments was recorded for learning benefits related to comprehension of previously acquired knowledge (t (82) = 4.25, p < 0.001) and willingness to use VR in future (t (82) = 3.83, p = 0.001).

5. Discussion

The primary motivation of this applied research was to explore how and to what extent affective factors, such as perceived immersion with the HMD-VR engine room simulator, shape maritime engineering students’ perceived learning benefits, future behavioural intentions, and overall experience. The findings provide a valuable basis for further discussion, underscoring their practical implications for educators, software developers, and university decision-makers.

The data-driven segmentation analysis displayed two distinct groups that emerged based on their levels of perceived immersion: Conformists (22.6%) and Immersionists (77.4%). These findings are compelling evidence that maritime engineering students’ subjective perceptions of immersion are not homogeneous despite being exposed to an identical VR environment. To a certain extent, the findings of the present research reflect those of [32], where a similar pattern was identified when segmenting maritime engineering students based on the perceived ease of use of the HMD-VR engine room simulator. Taken together, the findings of both studies underpin the relevance of segmentation approaches as a reliable tool for a deeper understanding of students’ affective experiences with VR technologies in an educational context. The findings of the segmentation analysis have several significant implications for maritime educators. For instance, tailoring instructional strategies to bridge the gap between Immersionists and Conformists could ensure that all the students benefit equally from integrating VR technologies in higher maritime education. It is worth mentioning that nearly 90% of those identified as Conformists reported having no prior experience with VR technology, which might have limited their ability to engage in the immersive experience fully. Although not a primary focus of this study, one possible explanation could be an occurrence of cybersickness, which might have affected their capacity to process the immersive VR environment effectively. Previous research has reinforced the claim that prior experience with a VR environment significantly influences cybersickness susceptibility, with longer prior virtual experience correlating with higher resilience to VR-induced discomfort [82,83]. Given these findings, future research should further examine the relationship between prior VR experiences, cybersickness, and perceived immersion to mitigate the potential barriers to implementing HMD-VR technology in MET.

Segments exhibited significant differences when profiled according to the perceived learning benefits, future behavioural intentions, and satisfaction. Immersionists consistently reported statistically higher scores in all the investigated aspects than Conformists. Regarding the perceived learning benefits, the results indicate that students with more positive reflections on perceived immersion are more likely to find the VR environment beneficial for studying and learning, particularly in reinforcing and deepening their understanding of previously learned concepts. These findings seem consistent with other research in the EVE field [35,37,49], highlighting the potential of immersive VR technologies as a valuable tool in complementing traditional learning and training methods. However, as this study did not incorporate a standardised metric knowledge assessment before and after trial sessions, the effectiveness of VR-based learning in terms of knowledge comprehension remains uncertain. Further research, thus, should integrate objective pre- and post-training knowledge assessments, including various training scenarios (e.g., fire, flood, and evacuation), to quantify learning outcomes, providing a more precise evaluation of perceived learning benefits. Moreover, conducting a comparative analysis of perceived learning benefits between HMD-VR and semi-immersive (i.e., desktop) engine room simulators would also be beneficial. Assessing the advantages and limitations of fully immersive VR-based learning in relation to semi-immersive would contribute to a more informed approach to best practices in MET.

Perceived immersion also played a significant role in shaping students’ future behavioural intentions regarding the use of VR technology. Immersionists were significantly more likely to consider using VR educational tools in the future and recommend them to others. These findings corroborate similar studies [35,38], suggesting that students with positive immersion experiences are more likely to advocate for its continued use. Similarly, Immersionists reported significantly higher satisfaction levels with the HMD-VR engine room experience than Conformists, reinforcing the claim that perceived immersion positively influences users’ overall experience [38,45]. Despite being encouraging and thus hypothesising a direct link between perceived immersion, future behavioural intentions, and satisfaction, these findings should be interpreted cautiously. Previous research indicates that the relationship between affective variables, such as PIMM, and outcome variables (e.g., FBI) is not direct but often mediated by factors like perceived learning outcomes [77]. This mediation is especially critical in the educational context, where emotional engagement alone may not lead to sustained use or advocacy of the technology unless it provides measurable learning benefits. In other words, if students perceive that an immersive VR experience has enhanced their understanding of concepts, reinforced previously gained knowledge, or allowed them to practice necessary skills in a safe and realistic environment, they are more likely to feel satisfied with the technology and consequently intent to use it in future or recommend it to others. This particular dynamic underpins a key distinction between using VR technology for education and entertainment (i.e., gaming). In entertainment, perceived immersion often drives satisfaction and loyalty, as the primary entertainment push-up factors are frequently enjoyment or escape.

In contrast, an educational VR environment must go beyond just providing an immersive experience; it must contribute to tangible learning outcomes. With this in mind, the author’s present study advocates continuous collaboration between developers and educators to ensure that the VR simulator’s content keeps aligning with maritime education’s pedagogical and practical requirements and not exclusively with cutting-edge technology trends. Since many maritime educational institutions and training centres often operate with limited financial resources, keeping pace with rapid advances in VR software and hardware features poses a considerable financial challenge. Thus, future research should also prioritise conducting a comprehensive cost–benefit analysis to identify optimal limits for investments in VR technology, ensuring that financial constraints do not impede the quality of educational outcomes.

6. Conclusions

The segmentation analysis based on perceived immersion employed in this study offered a novel perspective in understanding maritime engineering students’ attitudes and experiences with using the HMD-VR engine room simulator. The findings emphasise the need for adaptive approaches in designing and implementing VR educational tools in maritime higher education. While the findings can contribute to the current body of knowledge, particularly in the context of maritime higher education, certain limitations should be acknowledged. The main drawback of the present study lies in the generalisability of its findings. First, the sampling was conducted during the trial period, as the VR laboratory is still in its developmental phase. As a result, study participants may not fully represent the broader population of maritime engineering students who will eventually utilise the VR laboratory once it is fully integrated into the MET curriculum. Second, the relatively small sample may also be considered a generalisability issue. Although the sample provides initial insights on the role of perceived immersion in shaping maritime engineering students’ attitudes toward VR learning benefits, behavioural intentions and satisfaction with VR experience, expanding the sample to include students from other maritime engineering faculties or institutions equipped with similar HMD-VR technology could provide a more comprehensive conclusion. Third, all the sampled participants in the study were male. This outcome was not intentional but a direct consequence of the gender structure of the maritime engineering programme. According to the faculty’s official record, the share of female students currently enrolled in undergraduate, graduate, and special maritime engineering programmes accounts for only 0.8% (2 students). The lack of female students in maritime engineering programmes at the Faculty of Maritime Studies, University of Rijeka, is not an isolated occurrence; it reflects a well-documented gender imbalance in the maritime industry, where technical and seafaring roles have historically been male-oriented [84,85]. This pattern aligns with findings from similar maritime and associated engineering education and training studies, which also report an overwhelming male participant pool (e.g., [3,42,86,87,88]). Thus, future research should critically examine the structural and cultural factors contributing to this gender imbalance and explore target interventions to increase female participation in maritime engineering academic formation. In the present research context, further investigation is needed to assess whether gender influences student experiences with VR-based learning environments in maritime education and training.

Finally, it would also be beneficial to employ the advantages of Structural Equation Modelling (SEM) to explore the complex direct and indirect mutual relationships between constructs such as perceived ease of use, usefulness, perceived immersion, learning benefits, loyalty, and satisfaction while taking into account students’ socio-demographic characteristics and previous experience with VR technology. By identifying these relationships, researchers could offer maritime educators and software developers actionable insights on improving VR design, thus augmenting student engagement and learning outcomes.