Maritime cadet experiences with bridge simulators in terrestrial navigation education

Bridge simulators are increasingly central to maritime education, particularly for developing skills in terrestrial navigation. However, research often prioritizes technical fidelity over the learner’s perspective. Adopting a qualitative descriptive methodological approach, this study explores how 30 final-year cadets at Politeknik Maritim AMI Makassar experience and interpret the use of bridge simulators. Data were triangulated from semi-structured interviews, direct observation, and document analysis. The findings reveal four consolidated themes: (1) Visual Realism and Situated Cognition; (2) Bridging Theory and Practice through Transfer of Learning; (3) Psycho-Social Dynamics; and (4) Instructional Scaffolding and Design Constraints. While the simulator effectively bridged the gap between abstract theory and practice for many, significant challenges emerged regarding cognitive overload, “transfer gaps” under pressure, and anxiety within the socio-technical environment. The study’s originality lies in unpacking the specific psycho-social barriers in Eastern Indonesian maritime education, offering novel insights beyond typical technical evaluations. The article concludes with actionable policy recommendations for curriculum integration and instructor training to optimize simulator pedagogy in developing maritime nations.

1 Introduction

Maritime education stands apart from general vocational disciplines due to its high-stakes nature; the margin for error in real-world operations is often non-existent, where incompetence can lead to catastrophic loss of life and environmental damage. Consequently, the sector relies heavily on simulation-based learning (SBL) to equip cadets with practical experience before they face the perils of the open sea. Ensuring maritime safety and operational excellence is a global imperative, making maritime education a critical concern for developed and developing nations (International Maritime Organization [IMO], 2019; Popa et al., 2023). Terrestrial navigation–encompassing skills such as chart work, compass bearings, and course plotting–remains a core competency that every cadet must master before embarking on a seafaring career (Mu’tamar et al., 2023; Sibali and Kurniawaty, 2024). Despite its foundational role, numerous studies have documented that cadets frequently struggle to fully grasp essential concepts in terrestrial navigation, particularly when instruction is limited to traditional classroom-based approaches (Barus and Simanjuntak, 2023; Sibali, 2024; Simanjuntak et al., 2023). This learning gap is significant, as it can compromise operational safety and readiness on board vessels.

This persistent theory–practice gap has prompted integrating advanced educational technologies, most notably bridge simulators, into maritime curricula worldwide (Chen et al., 2024; Durlik et al., 2024; Harrison, 2009). Bridge simulators are sophisticated systems designed to replicate real-time navigational challenges, allowing cadets to engage in experiential learning that mirrors real-world scenarios (de Oliveira et al., 2022; Rakka, 2022). The International Maritime Organization (IMO) underscores the centrality of simulation-based training within the Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) framework, highlighting its importance for competency-based learning and operational readiness (Eruaga, 2024; International Maritime Organization [IMO], 2019).

Despite the proliferation of simulator technology, especially in maritime institutions across Indonesia, such as Politeknik Maritim AMI Makassar, existing research remains limited in several important respects. First, much of the literature focuses on the general impact of simulators on navigational learning outcomes, often employing quantitative metrics and standardized assessment (Bekesiene, 2023; Fountoulakis, 2024; Smith, 2025). While these studies have demonstrated measurable gains in student performance, they overlook cadets’ lived experiences, cognitive processes, and emotional responses during simulation-based training (Ala et al., 2024; Wiig et al., 2023). Second, research tends to generalize findings across different types of navigation, giving minimal attention to the unique conceptual demands of terrestrial navigation–a domain that is profoundly abstract and cognitively challenging, especially for first-time learners (Fagerhaug et al., 2025; Jwo et al., 2023). Third, studies focusing on the Indonesian context remain scarce, despite the nation’s increasing reliance on maritime expertise for economic development and safety (Barus and Simanjuntak, 2023; Simanjuntak, 2024a).

This gap is significant, given Indonesia’s status as an archipelagic nation with a growing maritime sector, where educational innovation is essential for building human resource capacity and supporting sustainable economic growth (Ala et al., 2024; Sibali and Kurniawaty, 2024). The present study is grounded in experiential learning theory and simulation pedagogy, which posit that meaningful learning occurs when individuals actively engage with authentic, practice-based tasks in realistic environments (Kolb, 1984). Prior research suggests that simulation enhances procedural knowledge and technical proficiency and improves learner confidence, motivation, and critical thinking (Rakka, 2022; Wiig et al., 2023). Nevertheless, there remains a lack of in-depth qualitative studies that examine how these pedagogical benefits unfold in the context of terrestrial navigation, particularly in Indonesian settings.

Given these gaps, there is a compelling need to investigate how bridge simulators are experienced by Indonesian maritime cadets learning terrestrial navigation. Specifically, little is known about (a) cadets’ perceptions of simulation-based instruction; (b) the cognitive and emotional challenges they encounter; and (c) the factors that either facilitate or hinder the transfer of theoretical knowledge to practical competence. Addressing these questions is essential for advancing pedagogical innovation, informing curriculum design, and optimizing simulator technologies to meet learners’ needs. This study, therefore, seeks to address the following questions:

1. How do maritime cadets perceive the use of bridge simulators in learning terrestrial navigation?

2. What challenges and benefits do cadets experience when using bridge simulators to understand terrestrial navigation concepts?

Unlike prior studies that predominantly focus on technical fidelity or quantitative assessment outcomes, this study investigates the underexplored cognitive and emotional dynamics of cadets. By focusing on the specific context of Eastern Indonesia, it highlights how interface complexity and social hierarchies influence learning transfer. Preliminary findings suggest that while simulators serve as powerful “cognitive bridges,” their effectiveness is frequently undermined by transfer gaps and psycho-social anxiety. This article aims to fill this knowledge gap by providing a nuanced, qualitative analysis of these lived experiences, offering a roadmap for more empathetic and effective simulator pedagogy.

2 Literature review

2.1 Terrestrial navigation in maritime education

In this article, the term “Terrestrial Navigation” is adopted to align with the formal nomenclature established in the International Maritime Organization (IMO) Model Course 7.03 and the STCW Code. While often used interchangeably with “coastal” or “visual” navigation in operational contexts, “Terrestrial Navigation” specifically denotes the foundational competency of determining a ship’s position using visual observations of earth-based features–such as coastlines, landmarks, and navigation aids (Chan et al., 2022; Martínez de Osés and Uyà Juncadella, 2021). This terminology is chosen to emphasize the specific cognitive process of translating physical geography into navigational data via traditional chart work and manual plotting, a skill that requires technical proficiency, strong spatial awareness, and situational assessment (Fagerhaug et al., 2025; Jwo et al., 2023).

Most maritime curricula teach terrestrial navigation early, emphasizing its importance for safe passage in restricted waters. However, the literature consistently reports that cadets struggle with abstract concepts (e.g., cross bearings, running fixes, translating visual cues into accurate chart plots), especially when these are presented in lecture-based or highly theoretical formats (Popa et al., 2023; Simanjuntak, 2024b; Simanjuntak et al., 2024). A key trend is the documented gap between theoretical understanding and practical application, which can undermine navigational safety and operational readiness. Few studies offer detailed explanations for these persistent learning challenges or systematically evaluate pedagogical approaches to address them.

2.2 Simulation-based learning in maritime contexts

Over the past decade, simulation-based learning has become a mainstay in maritime training (Ala et al., 2024; Edgar et al., 2022). Bridge simulators enable cadets to repeatedly practice navigation in safe, controlled, and realistic environments, exposing them to varied scenarios, ranging from routine maneuvers to complex emergencies (Dewan et al., 2023; Wu et al., 2023). The pedagogical framework is often linked to Kolb’s experiential learning theory, which underscores the role of hands-on experience, reflection, conceptualization, and experimentation in skill acquisition (Kolb, 1984).

Empirical studies consistently find that simulators improve technical competence, lower error rates, and enhance teamwork and communication skills (Eruaga, 2024; Tusher et al., 2024). A strength of this body of research is its demonstration of simulation as a tool for bridging the gap between classroom theory and operational reality (Mallam et al., 2019; Ochea, 2025; Zghyer and Ostnes, 2019). However, a major limitation is that much of the research focuses on technical performance metrics, often neglecting the subjective and cognitive aspects of learning, such as motivation, engagement, or the nuanced development of decision-making skills (Baihaqi, 2024; Purba, 2025; Simanjuntak, 2024a). Furthermore, little attention is paid to how different types of navigation–mainly terrestrial–are affected by simulator-based instruction (Sellberg et al., 2017, 2018), leading to generalizations that may overlook unique conceptual challenges.

2.3 Bridge simulator and learning outcomes

Although simulators have proven effective in enhancing general navigational performance, research focusing specifically on terrestrial navigation skills remains underdeveloped. Most studies highlight the benefits of simulators for electronic navigation (such as radar and ECDIS) or bridge resource management, with terrestrial navigation often addressed only tangentially (Chen et al., 2024; Lista, 2023; Röttger and Krey, 2021).

A review of existing studies also reveals a tendency to prioritize quantitative assessments–such as test scores or competence ratings–while underexploring learner-centered perspectives. Cognitive engagement, confidence, and the perceived complexity of tasks are rarely investigated in depth, despite their potential impact on skill development (Bekesiene, 2023; Fountoulakis, 2024; Smith, 2025). This methodological limitation restricts understanding the full spectrum of learning outcomes, particularly for abstract, skill-based domains like terrestrial navigation.

Together, these trends highlight the progress and limitations in current research. While the field has advanced significantly in adopting simulation technologies, there remains a need for more integrative studies, particularly those that synthesize technical, cognitive, and affective dimensions of learning and that focus explicitly on terrestrial navigation. This gap underscores the rationale for the present study, which seeks to explore cadets’ perceptions, challenges, and experiences with bridge simulators in mastering terrestrial navigation.

3 Materials and methods

3.1 Research design

This study employed a qualitative descriptive approach, which is particularly appropriate for exploring the nuanced perceptions, lived experiences, and interpretations of individuals, in this case, maritime cadets’ engagement with bridge simulators in terrestrial navigation training. The qualitative descriptive design was chosen because it enables the researcher to remain close to participants’ words and meanings, offering a direct account without excessive abstraction. This approach is especially well-suited to investigating learning processes, challenges, and contextual factors often overlooked in quantitative studies or experimental designs. It also allows for flexibility in probing emergent themes and provides rich, context-sensitive insights for addressing the study’s research questions.

3.2 Research setting and participants

The study was conducted at Politeknik Maritim AMI Makassar, an Eastern Indonesian maritime academy. This institution was selected purposively due to its comprehensive integration of full-mission bridge simulators into its curriculum and structured simulation modules for terrestrial navigation. This site offers a distinctive regional context for examining simulation-based maritime training, as much of the existing literature has focused on larger or more established academies in urban centers, such as Jakarta or Surabaya. The selection thus addresses a gap in the scholarly attention paid to Eastern Indonesia’s maritime education landscape.

Participants were 30 final-year cadets from the Nautical Department, selected using purposive sampling. Inclusion criteria required cadets to have completed classroom theoretical coursework and participated in simulation-based terrestrial navigation training. Detailed demographic characteristics of the participants are presented in Table 1. The sample consisted predominantly of male cadets (n = 25) with a smaller representation of female cadets (n = 5), reflecting the gender distribution typical of the maritime industry. All participants were in their final year (Semester 7 or 8), meaning they had completed their mandatory sea project internship (Prala). This prior sea service experience is critical to the study’s transferability, as it enabled participants to reflect on simulator fidelity by comparing it with their real-world operational experiences on board merchant vessels.

TABLE 1

Table 1. Participant demographics and experience profile (N = 30).

Sample Justification and Data Saturation The sample size of 30 participants is robust for a qualitative descriptive study, exceeding the typical range of 15–25 participants often recommended for reaching thematic saturation. In this study, data collection continued until data saturation was achieved; specifically, by the 24th interview, no new significant codes or themes were emerging, and the remaining six interviews served to confirm established patterns.

3.3 Data collection and analysis techniques

This study employed multiple data collection methods to gain a rich and nuanced understanding of cadets’ experiences with using bridge simulators in learning terrestrial navigation.

1 Semi-structured in-depth interviews: interviews were conducted individually with 30 cadets from the Nautical Department who had completed classroom and simulator-based terrestrial navigation training. Data collection continued until data saturation was achieved; specifically, by the 24th interview, no new significant codes or themes were emerging, and the remaining six interviews served to confirm established patterns. Interviews were guided by an interview protocol of open-ended questions aligned with the research questions. Each interview lasted approximately 30–45 min and was conducted face-to-face in a quiet room on campus premises. Interviews were audio-recorded with participants’ consent and later transcribed verbatim for analysis.

2 Non-participant observation: the researcher conducted non-participant observation during three separate simulator training sessions, each involving 8–10 cadets. The observation focused on how cadets interact with the simulator, apply theoretical concepts in real-time, communicate with team members, and respond to instructor feedback. Observations were recorded as field notes and structured using an observation checklist developed from terrestrial navigation learning indicators.

3 Document analysis: relevant documents were collected and analyzed to provide institutional context and support the data from interviews and observations. These documents included course syllabi and lesson plans for terrestrial navigation, simulation training modules, and logbooks. Document analysis evaluated the alignment between curriculum goals and simulator practice and how cadets’ written reflections support their verbal narratives.

Once data were collected, they were analyzed using NVivo software to facilitate the coding and organization of data guided by Miles et al.’s (2019) model of qualitative data analysis, which includes: (1) Data Reduction, where transcripts and observation notes were coded thematically; (2) Data Display, utilizing thematic matrices; and (3) Conclusion Drawing and Verification.

To ensure the trustworthiness and reliability of the analysis, a rigorous coding process was employed. Initial coding was conducted independently, followed by peer debriefing sessions among the authors to resolve discrepancies and refine the coding scheme. This iterative process ensured that the final themes–merged from initial codes such as “fear of mistakes,” “peer reliance,” and “visual confusion”–accurately reflected the participants’ voices rather than researcher bias.

The study employed methodological triangulation to ensure data credibility and richness by combining three data sources: interviews, observations, and document analysis. Additionally, source triangulation was applied by comparing cadets’ data with simulation instructors’ feedback during informal discussions. Potential limitations include reliance on self-reported data, which may be subject to recall bias. However, triangulation and participant anonymity were maintained to mitigate these risks.

4 Results

The qualitative analysis of interview transcripts, observational data, and document reviews yielded a comprehensive understanding of how cadets experience bridge simulator training. Based on the recursive coding process described in the methodology, the findings were categorized into four consolidated themes that reflect the cognitive, technical, and psycho-social dimensions of learning. These themes illustrate the complex journey from visual perception to conceptual mastery, mediated by social interactions and instructional design. Table 2 provides a high-level summary of these themes, key findings, and the dominant cadet experiences associated with each. The subsequent sections present a detailed narrative of these themes, supported by direct participant quotations and observational evidence.

TABLE 2

Table 2. Summary of thematic findings.

Theme 1: Visual Realism and Situated Cognition

Cadets’ perceptions of bridge simulators were fundamentally shaped by the interplay between visual realism, interface usability, and spatial understanding. The findings suggest that the simulator functions as a platform for situated cognition, where learning is inseparable from the authentic context of a ship’s bridge. For the majority of participants, the high-fidelity visual environment bridged the gap between abstract theory and practical application, transforming navigation from a static paper-based exercise into an embodied experience.

This situated nature of learning was highlighted by cadets who found that realistic visualization made abstract concepts tangible. Cadet 06 noted the immersive quality directly supported his mental model, allowing him to project himself into the operational environment:

“It really feels like I’m on the bridge of a real ship. I can see the coastline, the lighthouse–it helps me imagine how it will be in real life.” (Cadet 06)

This immersion provided necessary scaffolding for spatial reasoning. Unlike 2D classroom charts, the simulator offered a 3D perspective that clarified the relationship between the ship and its surroundings. As Cadet 03 explained, this visualization was transformative for understanding bearing procedures:

“Before using the simulator, I couldn’t really “see” how to take a bearing. Now I can visualize it better because I see the compass, the object, and the direction clearly.” (Cadet 03)

Observational data corroborated this; cadets were seen physically leaning toward displays and gesturing at virtual objects, indicating they were inhabiting the simulated space rather than just processing data. However, the findings also reveal that high fidelity does not automatically equate to effective learning. When the “situation” became ambiguous due to graphical limitations or interface complexity, the learning process faltered. Several cadets reported that visual blurring disrupted their cognitive flow, preventing accurate plotting. Cadet 27 remarked:

“The graphics are blurry. I couldn’t tell which landmark I was supposed to use. It made me doubt my plot.” (Cadet 27)

This visual ambiguity was compounded by interface complexity, where the abundance of controls overwhelmed novice learners. Instead of focusing on navigational decision-making, cognitive effort was diverted to managing the machine mechanics. Cadet 14 expressed this frustration:

“Too many buttons. I don’t know which one does what.” (Cadet 14)

Similarly, Cadet 09 noted the difficulty in locating critical instruments amidst the visual noise: “Hard to find the compass on the screen. I got lost trying to align the heading.” Crucially, these limitations often cascaded into spatial disorientation and cognitive overload. Unlike the static stability of a classroom chart, the dynamic environment caused some learners to lose their bearings during maneuvers. Cadet 28 described this struggle:

“I still get confused about where I’m facing. The ship turns, and I lose my sense of direction. I think the interface needs to be simpler.” (Cadet 28)

Another cadet reinforced this sense of overload, noting the difficulty in filtering relevant information:

“Too many things to look at. I don’t know what to focus on sometimes.” (Cadet 05)

These mixed experiences indicate that while the simulator’s visual realism offers a powerful environment for situated cognition, it requires careful calibration. As evidenced by the contrast between Cadet 03’s clarity and Cadet 28’s confusion, effective learning depends on the interface’s ability to balance realism with usability. Without adaptive visual aids to mitigate ambiguity and complexity, the high-fidelity environment can paradoxically induce extraneous cognitive load, hindering rather than helping competence development.

Theme 2: Bridging Theory and Practice through Transfer of Learning

One of the primary pedagogical functions of the bridge simulator is to facilitate the transfer of learning from the abstract classroom environment to the dynamic context of the ship’s bridge. The findings reveal that this transfer is not automatic; rather, it manifests as a spectrum ranging from strong integration to a persistent “transfer gap.” Success depended heavily on whether cadets could translate their declarative knowledge (facts and formulas) into procedural knowledge (action) within the simulated environment. For a significant number of cadets, the simulator acted as a cognitive bridge, effectively contextualizing theoretical instruction. Cadet 01 described this realization:

“Class material really made sense when I used the simulator. I could finally understand how to apply the theories we learned.” (Cadet 01)

Similarly, Cadet 16 noted that the simulation transformed abstract mathematics into meaningful practice:

“I saw the use of what I learned–like how to take bearings and fix the position. Before, it was just formulas on the board.” (Cadet 16)

This sentiment was echoed by Cadet 02, who emphasized the superiority of dynamic visualization over static charting for grasping concepts:

“Better than drawing on paper. I need to see it in action, not just imagine the line on a chart.” (Cadet 02)

Observational data supported this positive transfer. In one session, a cadet was observed recalling a specific formula and exclaiming, “Oh! This is just like the exercise we did in class, but now I get it.” Document analysis further confirmed that such moments were often facilitated by curricular alignment; for instance, simulation scenarios explicitly referenced “Week 5 cross-bearing techniques,” creating a clear pedagogical link. However, a significant subset of cadets experienced a transfer gap, particularly when faced with the temporal pressure of real-time simulation. Unlike the static, self-paced nature of paper-based chart work, the simulator demands simultaneous processing and rapid decision-making. Cadet 07 highlighted this disconnect:

“In class, it’s different from the sim. We calculate slowly on paper, but in the simulator, everything moves fast.” (Cadet 07)

This gap was further exacerbated by a lack of conditional knowledge–knowing when and why to apply a specific rule. Some cadets possessed the theoretical formulas but failed to recognize the contextual cues to use them. Cadet 12 illustrated this confusion:

“I’m not sure how to apply the formulas. The teacher said one thing, but I don’t know when to use it during the exercise.” (Cadet 12)

Others differentiated between visual tasks (which transferred well) and computational tasks (which remained difficult). Cadet 24 noted that complex calculations were often harder to manage in the simulator than in the classroom:

“The simulator is good for seeing what’s happening, but some things like tide correction or deviation are still better explained on paper.” (Cadet 24)

Consequently, when the scaffolding of the classroom was removed, some cadets resorted to mimicry rather than independent application. Cadet 20 admitted:

“I still don’t understand cross-bearing. I just follow what the others are doing.” (Cadet 20)

These findings suggest that while simulators are excellent for procedural application and visualization, they may not effectively remediate existing conceptual gaps regarding complex calculations. Without explicit instructional intervention to link theory to practice, the high-fidelity environment can sometimes amplify a cadet’s confusion rather than resolve it.

Theme 3: Psycho-Social Dynamics: Confidence, Anxiety, and Collaboration

The simulator environment elicited a complex range of affective responses, creating a psycho-social learning environment where confidence, anxiety, and peer interaction deeply influenced learning outcomes. The findings align with Social Constructivism, suggesting that learning in the simulator is not an isolated cognitive act but a social process mediated by emotions and peer support.

Predominantly, the simulator provided a “safe space” for trial and error, which is essential for building self-efficacy. Unlike the high-stakes environment of a real vessel where errors can be catastrophic, the virtual bridge allowed cadets to experiment freely. Cadet 10 emphasized this psychological safety as a prerequisite for learning:

“It’s safe to make mistakes here. We can try again if we get it wrong. That makes it easier to learn.” (Cadet 10)

This sense of safety fostered intrinsic motivation and engagement. Cadet 09 described the experience as energizing compared to traditional lectures: “I was excited to try the simulator. It’s different from just sitting and listening to a lecture.” Furthermore, repeated successful exposure in this safe environment helped internalize confidence. Cadet 05 noted the shift from uncertainty to predictability:

“I’m more confident after doing it. At first, I was unsure, but after two or three sessions, I knew what to expect and how to react.” (Cadet 05)

Observation confirmed that this low-stakes environment encouraged resilience; cadets were overheard reassuring each other during errors, saying, “Don’t worry, we can redo this if it’s wrong,” effectively normalizing failure as part of the learning curve. Conversely, for some learners, the high-fidelity environment triggered performance anxiety, blurring the line between training and assessment. The presence of instructors and peers created a “fishbowl” effect, where the pressure to perform inhibited cognitive functioning. Cadet 29 confessed that the scrutiny was paralyzed:

“Simulator makes me nervous. The instructor watches everything I do. I’m afraid to make mistakes.” (Cadet 29)

This anxiety was often social in nature, rooted in the fear of embarrassment before peers. Cadet 14 admitted: “I worry I’ll make a fool of myself in front of my friends or instructor.” In extreme cases, this pressure led to a “cognitive freeze.” Cadet 21 described forgetting well-known procedures once the scenario started:

“I forgot what to do when the scenario started. It’s like I blanked out under pressure.” (Cadet 21)

Reflective logbooks reinforced this, with cadets noting that the simulation sometimes “feels like an exam” rather than practice. These emotional responses were significantly mediated by collaborative learning dynamics. The bridge simulator naturally fosters a Vygotskian “Zone of Proximal Development,” where peers scaffold each other’s learning through shared problem-solving. Cadet 11 explained:

“We learn from each other. When someone makes a mistake, we talk about it and figure it out.” (Cadet 11)

However, the data also revealed a risk of dependency, or “social loafing,” where less confident cadets relied too heavily on stronger peers rather than developing their own competence. As Cadet 13 candidly admitted:

“Sometimes I depend too much on my friend. If he’s not there, I’m lost.” (Cadet 13)

Observational data supported this, showing uneven participation in some groups where one cadet led while others followed passively. Thus, while the social dynamic generally reduces anxiety and builds confidence, it requires structured management to ensure that collaboration does not morph into dependency, ensuring individual competence is developed alongside team performance.

Theme 4: Instructional Scaffolding and Design Constraints

The final theme underscores that the bridge simulator is not a standalone teacher; its pedagogical effectiveness is heavily dependent on instructional scaffolding and the reliability of the system design. The findings suggest that while technology provides the immersive environment, the instructor provides the essential cognitive bridge between confusion and mastery. The instructor’s role emerged as the critical variable in transforming simulation experiences into meaningful learning. Cadets consistently reported that technology alone was insufficient to resolve conceptual impasses. Timely, constructive feedback allowed them to diagnose errors that the system itself could not explain. Cadet 07 recounted how human intervention salvaged a confusing session:

“Instructor helped me when I was stuck. I couldn’t figure out the cross-bearing, but he walked me through it step-by-step.” (Cadet 07)

Similarly, Cadet 02 noted that feedback was both corrective and preventative, fostering a deeper understanding of procedural logic rather than just trial-and-error:

“Feedback helped me improve. At first, I always plotted the wrong point, but after he showed me where I went wrong, I didn’t repeat the same mistake.” (Cadet 02)

Observational data confirmed that pauses for “debriefing-in-action”–where instructors froze the scenario to project correct chart work on a shared screen–were moments where deep learning occurred. This aligns with the concept of scaffolding, where the instructor temporarily supports the learner until they can perform independently. However, the pedagogical potential was sometimes constrained by technical barriers. Technical disruptions, such as system lag or crashes, broke the immersive flow (presence) and frustrated learners. Cadet 23 highlighted the issue of system overload:

“Simulator sometimes lagged, especially when too many cadets were logged in or when the weather simulation changed.” (Cadet 23)

Cadet 15 described the impact of a sudden system freeze, which shifted focus from navigation to troubleshooting:

“I clicked the wrong button, and the whole screen froze. It took a while for the instructor to reset everything.” (Cadet 15)

Observations revealed the tangible cost of these failures: in one session, a system restart caused a 10-min instructional delay, leading a cadet to lament, “We lost the whole bearing exercise.” Such interruptions eroded the perceived fidelity of the training and increased extraneous cognitive load. Furthermore, issues with scenario design–specifically repetition and lack of escalating challenge–led to disengagement for some cadets. The “novelty effect” of the simulator tended to wear off when tasks became routine “cookbook” exercises. Cadet 26 remarked on the monotony:

“Too repetitive. After a while, it just feels like doing the same thing again and again.” (Cadet 26)

Others expressed a desire for greater complexity to mirror real-world unpredictability, arguing that static scenarios failed to prepare them for stress. Cadet 19 stated:

“Boring without real challenge. I wish there were more complex scenarios, or maybe some unexpected problems to solve.” (Cadet 19)

This sentiment was reinforced by reflective journal entries, where cadets noted: “Real navigation isn’t always calm and clear. We need to practice with stress and uncertainty, too.” These findings highlight that for simulation to remain effective, the curriculum must offer a progressive trajectory of difficulty, supported by robust technical infrastructure and active, adaptive instructor facilitation.

5 Discussion

This study examined how maritime cadets perceive the use of bridge simulators in learning terrestrial navigation. The findings illuminate the multi-dimensional impact of simulation-based education–spanning conceptual, technical, emotional, and social domains. In line with Lusto (2025), this discussion interprets the significance of these findings sequentially, linking them to prior literature and the theoretical frameworks of Situated Cognition, Transfer of Learning, and Social Constructivism.

5.1 Visual realism and situated cognition

The findings regarding visual realism align with the principles of Situated Cognition, which posit that knowledge is inseparable from the context in which it is used. Kolb’s Experiential Learning Theory (1984) was borne out in the findings: cadets’ comments and observed behaviors showed that knowledge is solidified when it is enacted, visualized, and repeated in realistic scenarios (Chan et al., 2022; Fagerhaug et al., 2025; Kolb, 1984). The simulator’s role in making abstract navigation concepts concrete–such as bearing, fixing, and course plotting–was particularly crucial for visual and kinesthetic learners, supporting observations by Popa et al. (2023) and Simanjuntak et al. (2024). This demonstrates that the “situation” provided by the simulator transforms navigation from a static paper exercise into a dynamic, embodied practice.

However, the study also reveals that “authenticity” has limits. Technical barriers, specifically interface complexity and system reliability issues, were evident and led to cognitive overload, disrupting the situated learning flow (Simanjuntak, 2024b). This supports studies by Eruaga (2024), Lista (2023), and Röttger and Krey (2021), which emphasize that for situated learning to be effective, the design must be user-friendly. Progressive exposure to system features–rather than “all at once” immersion–is essential to reduce extraneous cognitive load, as suggested by Chan et al. (2022) and Sibali and Kurniawaty (2024).

5.2 Transfer of learning: bridging the gap

The simulator’s role in bridging theory and practice relates directly to the theory of Transfer of Learning. The results confirm that bridge simulators support the “near transfer” of classroom knowledge to authentic, operational contexts, effectively reinforcing conceptual knowledge and building technical proficiency (Dewan et al., 2023; Ghergulescu and Muntean, 2010; Wu et al., 2023). As observed in prior studies (Popa et al., 2023; Simanjuntak et al., 2024), the simulator’s ability to make abstract navigation concepts concrete–such as bearing, fixing, and course plotting–was particularly crucial for visual and kinesthetic learners, enabling them to internalize procedures that were previously theoretical.

However, the study also identified a persistent “transfer gap,” where a subset of cadets struggled to apply theoretical formulas under the temporal pressure and multi-tasking demands of the simulation (de Oliveira et al., 2022; Fountoulakis, 2024; Hix, 2013). This struggle suggests a failure in “far transfer,” indicating that the proximity of the simulation to reality does not guarantee the automatic application of knowledge. This gap highlights the critical need for tighter curriculum integration and the explicit demonstration of the theory–practice link, as argued by Manuel (2017) and Mori and Manuel (2023). Without these pedagogical bridges, high-fidelity environments may overwhelm rather than educate.

5.3 Social constructivism and psycho-social

Dynamics the complex emotional and social dynamics observed in this study support a Social Constructivist perspective on simulation-based learning. Peer collaboration emerged as a key facilitator, confirming Vygotsky’s (1980) view that learning is often scaffolded by more experienced peers (Ala et al., 2024; Wiig et al., 2023). This collaborative environment, combined with the immersive nature of the simulator, significantly increased intrinsic motivation and engagement, echoing conclusions by Deci and Ryan (2000), Ghergulescu and Muntean (2010), and Lusto (2025).

Furthermore, the concept of “safe failure” provided a space to correct mistakes without real-world consequences, supporting Bandura’s (1997) Social Cognitive Theory on self-efficacy and mastery learning (Edgar et al., 2022). Repeated exposure and supportive feedback led to measurable gains in cadet confidence and autonomy (Popa et al., 2023; Simanjuntak, 2024b; Simanjuntak et al., 2024).

However, the social environment also presented risks. The phenomenon of overdependence on peers echoes findings by Martínez de Osés and Uyà Juncadella (2021), suggesting a need for clear accountability to ensure equitable skill development. Additionally, the anxiety reported by some cadets–especially when simulations felt like high-stakes assessments–supports Krashen’s Affective Filter Hypothesis (Patrick, 2019) and findings by Mu’tamar et al. (2023) and Tusher et al. (2024). This indicates that high anxiety can inhibit technical learning, underscoring the critical importance of a supportive emotional climate as emphasized by Wu et al. (2023).

5.4 Scaffolding and instructional design

Finally, the findings reassert the centrality of Instructional Scaffolding and robust design. A critical limitation identified was the static and repetitive nature of scenarios, which undermined engagement and restricted the development of higher-order skills (Adhikari, 2024; Ghergulescu and Muntean, 2010). Such predictable, routine exercises risk stagnation at the lower levels of Bloom’s Taxonomy (Wiig et al., 2023) and fail to prepare learners for the ambiguity and stress of real maritime operations (Chen et al., 2024; Edgar et al., 2022; Fagerhaug et al., 2025).

To maximize pedagogical value, the curriculum must move beyond “cookbook” exercises toward dynamic, problem-based scenarios that progressively challenge learners (Dewan et al., 2023; Jwo et al., 2023; Popa et al., 2023; Rakka, 2022). Scenario diversity and adaptive challenge are necessary to foster critical thinking, flexibility, and adaptive expertise (Tusher et al., 2024). Ultimately, the simulator is not a standalone teacher; its effectiveness depends on instructional alignment, constructive feedback, and adaptive scenario structure that scaffolds the learner from novice to competent navigator.

5.5 Implications for practice and policy

The findings of this study offer critical implications for maritime education. First, regarding curriculum design, institutions must ensure that simulator scenarios are explicitly aligned with classroom instruction to bridge the identified “transfer gap.” Theoretical concepts should be refreshed immediately prior to simulation sessions to support cognitive retrieval.

Second, the role of the instructor must evolve from a mere operator to a pedagogical facilitator. Instructors require training not only in technical operations but also in providing constructive scaffolding and managing learner anxiety. Creating a psychologically safe environment–where errors are framed as learning opportunities rather than assessment failures–is essential to mitigate the “affective filter” that hinders performance.

Third, regarding scenario design, there is a pressing need for adaptive scenarios that offer progressive complexity. To prevent engagement stagnation, simulations should evolve from routine tasks to dynamic, problem-based challenges that require higher-order thinking and collaboration, thereby reducing the risk of cognitive overload while maintaining challenge.

5.6 Limitations and future research

Despite its contributions, this study has limitations. First, it was conducted at a single maritime institution in Eastern Indonesia with a sample of 30 cadets, which, while sufficient for qualitative depth, limits the statistical generalizability of the findings to the broader global maritime population. Second, reliance on self-reported interview data introduces the potential for recall bias, although this was mitigated through triangulation with observations and logbooks. Third, the study focused exclusively on terrestrial navigation, leaving open questions about how perceptions might differ in other domains like ECDIS or engine room simulations.

Future research should address these gaps through comparative multi-institutional studies to understand contextual variations in simulator efficacy. Longitudinal research is also recommended to assess whether the confidence gained in simulators translates into long-term competence on board real vessels. Additionally, investigating instructor perspectives would enrich the understanding of the pedagogical challenges in delivering effective simulation-based training.

6 Conclusion

This study investigated maritime cadets’ perceptions and experiences of using bridge simulators in learning terrestrial navigation, revealing new insights into the benefits and challenges of simulation-based maritime education. Through thematic analysis of interviews and observations with 30 cadets, the research identified six primary themes: realism and visualization, confidence and emotional response, bridging theory and practice, instructional feedback, motivation and engagement, and learning challenges. Additionally, the analysis uncovered specific challenges and benefits, ranging from pedagogical and cognitive advantages to technical, emotional, and scenario design limitations.

Key findings demonstrate that when thoughtfully integrated into the curriculum, bridge simulators are powerful tools for enhancing cadets’ conceptual understanding, technical skills, motivation, and confidence. The study highlights the value of authentic, hands-on experience and safe environments for learning from mistakes, collectively fostering deeper learning and self-efficacy. However, it also underscores the importance of careful instructional alignment, gradual complexity, intuitive interface design, and supportive emotional climates to prevent disengagement, cognitive overload, and anxiety. In sum, this research advances the field by clarifying the conditions under which simulation-based training is most impactful, offering guidance for future pedagogical practice, and contributing much-needed qualitative insight to the global body of maritime education research.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Universitas Cahaya Prima, Bone, Indonesia. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

AS: Data curation, Writing – review & editing, Writing – original draft, Conceptualization. KK: Formal analysis, Methodology, Writing – review & editing, Investigation, Writing – original draft. AA: Project administration, Writing – original draft, Validation, Writing – review & editing. MM: Writing – original draft, Validation, Writing – review & editing, Conceptualization.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. The author(s) verify and take full responsibility for the use of generative AI in the preparation of this manuscript. Generative AI was used to assist in improving the clarity of language, grammar, and structure of the text. The intellectual content, research design, data collection, analysis, and interpretation were entirely conducted by the authors. All ideas, arguments, and conclusions presented are the sole responsibility of the authors.

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