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Article

Ubiquitous Virtual Cognitive Practice Mode in Engineering Management Utilizing Web Map Panoramas: Application and Effectiveness Analysis

by
Yao Huang
,
Fubin Liu
,
Dingli Liu
*,
Weijun Liu
and
Rongwei Bu
School of Transportation, Changsha University of Science & Technology, Changsha 410114, China
*
Author to whom correspondence should be addressed.
Systems 2026, 14(5), 492; https://doi.org/10.3390/systems14050492
Submission received: 19 February 2026 / Revised: 26 April 2026 / Accepted: 29 April 2026 / Published: 30 April 2026
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Abstract

Traditional cognitive practices in the Engineering Management Major (EMM) are often constrained by safety risks, high costs, and geographical limitations. This study proposes a novel Virtual Cognitive Practice (VCP) mode that integrates ubiquitous learning (U-learning) with web-based panoramic maps to overcome these challenges. We developed a VCP system leveraging panoramic data of roads, bridges, and tunnels from commercial web mapping platforms to provide high-fidelity, interactive observation environments. To evaluate its effectiveness, 147 undergraduate students participated in a virtual practice course and subsequently completed a structured questionnaire. The results demonstrate that the accuracy on objective knowledge tests exceeded 80%, alongside a high mean score of 4.27/5 for visualization satisfaction. Statistical analysis using Chi-square tests indicates that students with prior on-site experience are significantly more confident in the VCP mode’s potential as a pedagogical alternative. This research bridges the technical gap in EMM practical education by providing a flexible, ubiquitous learning ecosystem.

1. Introduction

Practical learning is an essential component of professional training in higher education, particularly for majors related to civil engineering. As a core practical component of the Engineering Management Major (EMM), cognitive practices—comprising physical site visits or off-campus field trips—aim to provide students with an intuitive understanding of engineering scenarios, thereby facilitating perceptual understanding [1,2]. Through on-site observation of representative completed projects, students can enhance their understanding of project types, structures, and functions [3]. Consequently, cognitive practice serves as a critical link for EMM students to consolidate their theoretical foundations. However, the traditional mode of cognitive practice often faces challenges such as on-site safety risks and limited observation perspectives [4]. These challenges are further exacerbated by unforeseen public emergencies [5,6], making it a pressing research priority to explore methods that minimize exposure risks while effectively achieving the goals of cognitive practice.
In recent years, the integration of information technology and education has become a focal point for driving educational innovation and modernization [7,8,9,10,11]. This trend has led to the widespread adoption of web-based theoretical learning platforms—such as massive open online courses (MOOCs), flipped classrooms, and micro-lectures [12]—as well as experimental learning platforms utilizing virtual simulation technology. While these tools have revolutionized theoretical and experimental instruction, their application in on-site practical courses remains limited. In the specific context of cognitive practice in EMM, standard online videos often provide inadequate spatial representation and lack both operability and interactivity. Consequently, students struggle to independently explore building structures and layouts from multiple perspectives, which stifles their initiative and limits deep engagement. Furthermore, existing experimental platforms are often tailored to specific institutional needs, resulting in a scarcity of resources specifically designed for EMM cognitive practice. Addressing these limitations to foster a more immersive and interactive learning experience remains a pressing challenge in educational technology.
Ubiquitous learning (U-learning) refers to a remote, online, and self-directed learning paradigm [13]. Supported primarily by devices such as smartphones and tablets, this paradigm enables students to overcome spatial and temporal limitations [13,14,15]. This “anytime, anywhere” capability has fundamentally transformed how students access, comprehend, and interact with educational materials. When designing a U-learning approach, it is essential to consider the characteristics of the learning environment, particularly the degree of reality it aims to replicate. For instance, two primary environments are commonly employed: those that mimic real-world settings and those that are purely digitally created [16]. U-learning can be significantly augmented by Virtual Reality (VR) technology to provide an immersive experience through the simulation of physical reality [17,18]. Consequently, integrating VR technology to replicate authentic educational scenes offers an effective strategy to address the aforementioned challenges and enhance the overall efficacy of U-learning.
A comprehensive review of existing literature and practical educational applications reveals a clear research gap: despite the advances in online virtual learning and U-learning technologies, there is a lack of a mature, ubiquitous Virtual Cognitive Practice (VCP) mode for EMM that integrates web map panoramic data with U-learning’s “anytime, anywhere” learning characteristics. Current solutions either fail to meet the interactive and immersive requirements of EMM cognitive practice or lack validation of their pedagogical effectiveness with empirical data from EMM undergraduate cohorts. Against this backdrop, the primary purpose of this study is to propose and validate a novel ubiquitous VCP mode for EMM that integrates U-learning with commercial web map panoramas. By developing a VCP system to load panoramic data of constructed roads, bridges, and tunnels, this study enables students to perform virtual visits and remote observations. Ultimately, this research advocates for the integration of U-learning with engineering management practices, bridging the technical gap to provide a scalable alternative to traditional on-site practice. To systematically address this identified research problem, the study is guided by the following core research questions:
RQ1: To what extent does the ubiquitous VCP mode enhance professional knowledge acquisition and fulfill the perceptual learning needs of EMM students?
RQ2: How do students perceive the pedagogical value of the VCP mode, and what are their attitudes toward integrating it with or substituting it for traditional on-site practice?
The remainder of this paper is organized as follows: Section 2 presents a comprehensive literature review of existing offline and online cognitive practice modes, as well as current trends in ubiquitous and virtual learning; Section 3 details the research methodology, including the VCP mode design, system development, and experimental implementation; Section 4 analyzes the empirical data derived from the knowledge assessment and questionnaire survey; Section 5 discusses the key results, practical implications, and theoretical contributions; and Section 6 concludes the research and outlines directions for future work.

2. Literature Review

2.1. Existing Offline Cognitive Practice Modes

Currently, the standards and training protocols for cognitive practice in EMM at various universities remain predominantly rooted in traditional offline modes [19,20]. Despite advancements in information technology, existing cognitive practice modes have not undergone significant transformation [21]. Institutions typically arrange for EMM students to conduct on-site observations of completed engineering projects, encompassing various sectors such as transportation (infrastructure like roads and bridges), residential housing, and water conservancy. The primary objective is to comprehend the types, structures, and functions of these infrastructure facilities [3]. Nevertheless, this traditional mode presents several inherent shortcomings.
Due to the complex environments of many visiting sites (as depicted in Figure 1a,b), arranging cognitive practice for large student cohorts poses considerable logistical and safety challenges for both institutions and instructors [22]. For instance, in transportation engineering, observation points are often situated in areas with heavy vehicular or pedestrian traffic. The presence of large groups in these high-traffic areas for extended periods not only poses safety risks but also subjects students to environmental noise, which significantly compromises instructional quality. Furthermore, cognitive practice necessitates access to the periphery or the interior of structures, requiring cooperation from site management. However, these management teams often adopt an defensive stance toward large-scale visits, perceiving them as a burden [23]. They are often hesitant to assume responsibility for potential liabilities and fear that student presence may disrupt normal operations or public order.
Additionally, while such projects are common worldwide, students are frequently limited to local cases, lacking broader exposure to representative projects across wider regions [22,24]. Constraints in space or safety regulations often prevent students from closely examining critical structural details, such as those within bridges or tunnels. Moreover, the high economic and temporal costs associated with group travel limit the frequency of these activities. Finally, traditional modes are vulnerable to unforeseen emergencies [25].

2.2. Existing Online Virtual Learning Modes

Driven by the digital transformation of higher education, various online virtual learning modes have emerged [26]. These include virtual theoretical learning platforms based on video technology and virtual experimental learning platforms utilizing simulation technology. In recent years, a variety of virtual theoretical learning modes has been widely adopted, including MOOCs, micro-lectures, flipped classrooms, and small private online courses (SPOCs) [27]. These modes have yielded significant pedagogical benefits. For instance, at Xidian University, a hybrid learning approach combining online self-study with offline classroom discussions significantly enhanced the effectiveness of English language acquisition [28]. Similarly, at the Beijing University of Civil Engineering and Architecture, the integration of micro-lectures with question-guided approaches improved students’ utilization of their self-study time [29]. Furthermore, institutions such as the University of California, Los Angeles, and Salem State University have released a series of instructional videos for biology and chemistry on YouTube, which have been instrumental in helping students master key concepts independently [30,31].
Complementing these theoretical advancements, several educational institutions have developed virtual experimental learning platforms tailored to their specific professional requirements. At Beijing University of Chemical Technology, a virtual simulation platform for robot control technology was designed using the V-Rep platform, successfully bridging the gap between theoretical and practical learning. At Zhejiang University, a virtual platform for engineering drawing assembly combined VR and augmented reality (AR) to improve student initiative and pedagogical outcomes. Similarly, Tianjin University developed a virtual simulation platform for communication principles using LabVIEW, facilitating a deeper understanding of experimental processes. At the Technische Universität Wien, an AR-based virtual learning platform for building construction received positive feedback from both instructors and students [32]. By incorporating VR and AR, these Higher Education Institutions (HEIs) offer immersive and realistic experiences [33,34]. Such innovative approaches significantly enrich U-learning, facilitating educational activities regardless of time, location, or medium.

2.3. U-Learning

The concept of Ubiquitous Computing (Ubicomp), originally proposed by Mark Weiser [35], envisions a world where technology is seamlessly integrated into the fabric of everyday life, becoming “invisible” to the user while providing continuous support. In the pedagogical domain, this has evolved into U-learning, which reinvents traditional pedagogy by offering uninterrupted, on-demand access to educational resources beyond the confines of physical classrooms [36,37]. Leveraging information technology, mobile devices, and digital content, U-learning provides learners with opportunities to engage at any time, in any place, and through any medium [17,38]. This flexibility signifies a paradigm shift from location-bound learning toward a more adaptable, learner-centric methodology that serves as a bridge between formal and informal learning domains [15,36]. For instance, a student can seamlessly initiate a lesson on a school computer, progress via a smartphone while commuting, and conclude it on a tablet at home.
To promote this uninterrupted learning, U-learning increasingly strives to harness the potential of VR and AR [18,39]. Its objective is to create immersive, situated experiences where students interact with virtual elements to achieve meaningful learning. VR, in particular, enables the construction of virtual environments that facilitate activities in both classroom and blended settings. To promote uninterrupted learning, it is essential to minimize the disjunction between physical and virtual spaces [40]. By integrating VR, U-learning broadens the horizons of mobile learning, elevating the immediacy of content interaction and the continuity between virtual and physical realities [41]. It serves as a bridge between formal and informal learning domains, fostering perpetual curiosity by embedding educational material into routine life [14].
Regarding their value for university teaching, recent scholarly discourse emphasizes that ubiquitous virtual learning facilitates “context-aware” learning and “seamless” transitions [42,43]. In the context of engineering education, these technological advancements support a significant trend toward the democratization of high-quality resources [44]. By decoupling learning from physical proximity to project sites, higher education institutions can provide equitable pedagogical experiences regardless of a student’s geographic location. Furthermore, analytical reviews of immersive learning indicate that applying ubiquitous computing to cognitive practice enhances students’ “cognitive presence” [45]. It does not merely replicate the physical site but enhances it through data overlays and multi-dimensional perspectives—such as interior structural visualizations—that are physically impossible to achieve during traditional on-site visits [46].
Consequently, current research trends are shifting from simple content delivery to the creation of “adaptive virtual ecosystems,” where the educational environment dynamically responds to the learner’s progress. However, while online virtual modes for theoretical and experimental courses have demonstrated efficacy, a notable gap remains in online learning for cognitive practice within the EMM. Integrating U-learning concepts with traditional cognitive practice to establish a ubiquitous Virtual Cognitive Practice (VCP) mode is therefore of significant value. By enabling practice activities “anytime, anywhere, and in any manner,” the VCP mode addresses the inherent limitations of traditional engineering field trips and empowers EMM students to conduct widespread virtual site visits and remote observations of engineering projects.

3. Methodology

3.1. Research Questions and Hypotheses

The empirical evaluation phase is guided by two core research questions (RQs). The first question (RQ1) explores the extent to which the ubiquitous VCP mode enhances professional knowledge acquisition and fulfills the perceptual learning needs of EMM students. To systematically address this, two hypotheses were formulated: H1 posits that the ubiquitous VCP mode significantly facilitates professional knowledge acquisition, enabling students to achieve a high level of objective cognitive accuracy, and H2 proposes that the VCP mode fulfills the perceptual learning needs of students, yielding high subjective satisfaction regarding structural visualization and system interactivity.
The second question (RQ2) investigates how students perceive the pedagogical value of the VCP mode and their attitudes toward integrating it with or substituting it for traditional on-site practice. Correspondingly, H3 asserts that students with prior on-site practice experience (Cohort B) exhibit a significantly higher recognition of the VCP mode’s potential as an alternative or substitute compared to those without prior experience (Cohort A). Finally, H4 hypothesizes that regardless of prior on-site experience, EMM students predominantly recognize the pedagogical necessity of integrating the VCP mode with traditional on-site practice to create a blended learning environment.

3.2. Design and Development of the VCP Mode

(1)
Creation of virtual cognitive scenarios
A panoramic map, also known as a full-view map, is a three-dimensional (3D) representation of a real-world location [47]. Professional cameras capture image data of an entire scene, which is subsequently stitched together using specialized software. This process converts a two-dimensional planar view into a simulated spatial environment, enabling immersive panoramic browsing [48]. Viewers can interactively explore these scenes of real targets from various angles by dragging and rotating the view. The primary advantages of panoramic maps include: (1) the ability to observe targets from multiple perspectives, thereby eliminating restricted viewing angles and (2) a profound sense of spatial depth, providing an immersive experience. Currently, major services such as Google Maps, Baidu Maps, and Gaode Maps offer panoramic modes. In this study, a VCP mode based on Baidu Maps’ panoramic data was developed. According to official data, Baidu Maps’ panoramic coverage includes street views across 652 cities, spanning over 2.295 million kilometers of roadways, with extensive data on transportation infrastructure such as roads, bridges, and tunnels in China. Consequently, these web-based panoramas were utilized to construct representative virtual cognitive scenarios for transportation engineering.
The process for creating these scenarios is illustrated in Figure 2 and comprises the following steps: (1) Selecting and confirming the transportation engineering project (e.g., a specific road, bridge, or tunnel); (2) Designing a corresponding pedagogical framework, including key knowledge points such as project type, critical structural components, and functionality; (3) Retrieving panoramic images from Baidu Maps that align with each knowledge point; (4) Extracting the specific URLs for each panorama; (5) Integrating the knowledge points, panoramas, and URLs using HTML, CSS, and JavaScript to develop dedicated web pages; and (6) Merging these individual pages to form a cohesive virtual cognitive scenario module.
As shown in Figure 3, typical cognitive practice scenarios for bridges with representative structural characteristics have been designed. In each scenario, relevant knowledge points are labeled. The knowledge points in the system are native interactive text hotspots (not post-processed) embedded in the panoramic scene; clicking a hotspot displays detailed information in the central area marked in Figure 4. By navigating the interface, students can view different sections of the panorama; clicking on a specific “label” triggers a pop-up window displaying the corresponding name and structural description.
(2)
Development of the VCP system
Utilizing Baidu Maps’ panoramic data, scenarios for various traffic engineering projects—including roads, bridges, and tunnels—are created. By integrating these virtual cognitive modules with auxiliary functions (such as login, timing, and Effectiveness Analysis modules), the “Virtual Cognitive Practice System of Road, Bridge, and Tunnel Engineering” has been established. As illustrated in Figure 4, the main interface comprises login information at the top, a navigation bar on the left (with the Effectiveness Analysis as an independent functional entry), a knowledge point display area, and a panoramic display window that appears upon clicking the corresponding link buttons. The system is developed using HTML, CSS, and JavaScript, and is deployed on a web server running Internet Information Services (IIS). It adheres to a browser/server (B/S) architecture; Figure 4 presents a screenshot of the system running within the Safari browser.
The operational workflow of the VCP system is as follows: (1) Users access the login page via a web browser; (2) Upon entering a security key, they are redirected to the main interface; (3) Users browse various project scenarios via the navigation bar, engaging with knowledge points and their associated panoramic views; and (4) After completing virtual practice, users can access the Effectiveness Analysis function to generate a real-time learning evaluation report based on their practice data. Specifically, the underlying logic of this analysis module relies on basic user engagement metrics: it automatically records the student’s total learning duration within the system and calculates their accuracy rate on simple formative assessment questions related to the observed engineering scenarios. The output is a straightforward real-time evaluation report that summarizes the learning time and quiz scores, providing students with immediate, direct feedback on their cognitive engagement. Figure 4a,b demonstrate different vantage points of the H Bridge structure as visualized within the VCP system.
To facilitate U-learning, the VCP system is accessible via smartphones and tablets. The system offers two distinct viewing experiences: a standard panoramic mode and a split-screen panoramic viewing mode. Users equipped with basic head-mounted displays can engage with the split-screen mode, which slightly enhances the sense of spatial scale and depth for the panoramic images. While the standard viewing mode offers a conventional non-split experience, it remains highly accessible across all devices and provides high-fidelity image reproduction through panoramic maps. This flexibility allows a student to initiate a lesson on a campus computer, progress via a smartphone while commuting, and conclude it on a tablet at home.
AI tool use disclosure for Figure 5: The human figure in the center of Figure 5 was generated with the assistance of Doubao (online web version, powered by the Seed 2.0 foundation model, 2026). The author provided a detailed prompt describing the required appearance (casual clothing, neutral expression, wearing a head-mounted display) to generate the initial character image. The output was then manually cropped, edited, and integrated into the multi-device synergy schematic by the author. All other visual elements in Figure 5 (including device interfaces, panoramic screenshots, and layout) were created or captured by the author. The AI tool was used solely to generate this illustrative character element, and the author takes full responsibility for the final design and content of the figure.
Figure 5 illustrates the multi-terminal synergy design of the VCP mode, which is core to realizing the ‘anytime, anywhere’ U-learning concept based on web-map panoramas. The PC terminal (left panel) serves as the primary interface for in-class deep exploration, supporting the high-resolution display of panoramic scenes and detailed browsing of knowledge point labels. The smartphone and tablet versions (top and bottom panels) adopt a responsive web design, supporting ‘fragmented learning’ with touch-screen operations (e.g., sliding to switch perspectives) that align with mobile learning habits. Furthermore, the web-map panoramas support the aforementioned split-screen viewing mode (center panel). By utilizing basic head-mounted displays, students can experience a split-screen panoramic view that enhances spatial perception (e.g., recognizing the relative positions of tunnel emergency facilities). This multi-terminal design ensures that cognitive practice is entirely web-based yet highly adaptable to various viewing preferences.

3.3. Application of the VCP Mode

Two cohorts of undergraduate students majoring in EMM participated in a VCP course and a subsequent questionnaire survey. Cohort A had no prior experience with traditional on-site practice, whereas Cohort B had already completed on-site sessions. The application experiment was implemented in a unified computer laboratory environment with consistent PC terminal equipment. All students followed the same standardized experimental procedures to control variables and ensure the reliability of the research results. The VCP system featured independent learning modules containing standardized knowledge point labels and structural description materials, sorted by the structural complexity of the engineering projects.
The experimental procedure commenced with a ten-minute pre-experiment guidance session, during which instructors explained the system operations, learning requirements, and the assessment process. Following this, students engaged in a 180-min independent virtual cognitive practice session where they freely browsed panoramic scenes, interacted with knowledge point labels, and conducted multi-perspective observations of the structures. Immediately after the practice session, students completed a 40-min online objective knowledge assessment and subjective survey to systematically record their knowledge mastery, learning experience, and pedagogical attitudes.

3.4. Questionnaire Survey

To empirically answer the established research questions, a structured questionnaire was employed as the primary evaluation tool. Rather than relying solely on subjective feedback, the questionnaire was designed with a dual purpose: quantifying students’ objective cognitive outcomes (knowledge acquisition) and systematically capturing their subjective perceptions and attitudes toward the VCP mode. The assessment of knowledge points is grounded in a mature pedagogical framework utilized within traditional on-site internships, ensuring that all items are strictly aligned with the official course syllabus and the EMM training plan. By transitioning this established assessment tool from a post-site-visit format to a post-virtual-visit format, a standardized evaluation metric was maintained.
Furthermore, to ensure the instrument’s validity and reliability within this digital modality, the questionnaire was rigorously reviewed and validated by a panel of ten expert faculty members from the Engineering Management department. As illustrated in Table 1, the questionnaire is divided into three distinct functional sections. To provide a transparent and analytical view of the evaluation instrument, Table 1 details not only the survey themes but also the specific competencies assessed and representative examples of the exact questions asked. For instance, rather than generally asking about bridge engineering, the objective knowledge section specifically evaluates students’ competencies in identifying structural classifications and engineering parameters directly from the virtual scenarios. Similarly, the subjective evaluation section quantifies their spatial recognition competencies across diverse infrastructure components.
Given that this instrument is a knowledge-based feedback survey rather than a standardized psychometric scale, its robustness was established through rigorous content validity and procedural reliability controls. Content and face validity were ensured by deriving items directly from the official EMM cognitive practice syllabus to guarantee accurate pedagogical mapping. Prior to distribution, an academic panel comprising three engineering education specialists and seven educational measurement experts reviewed the instrument, followed by a pilot test to refine item phrasing. To minimize memory decay and eliminate external confounders, the survey was administered in a controlled laboratory setting immediately following the practice session. Ultimately, 152 questionnaires were collected, yielding 147 valid responses—an effective response rate of 96.71%.

3.5. Data Analysis Techniques for Hypothesis Verification

The quantitative data collected from the 147 valid questionnaires were analyzed using SPSS 26.0 software to verify the assumed hypotheses. For H1 and H2, descriptive statistics were employed; specifically, the accuracy rates of the objective knowledge assessment were calculated to verify H1, while the mean scores and frequency distributions of the 5-point Likert scale were analyzed to verify H2. To test H3 and H4, cross-tabulation and Pearson’s Chi-square (χ2) tests were utilized to analyze the differences in pedagogical attitudes between the two independent samples (Cohort A and Cohort B). A significance level of p < 0.05 was established to determine the statistical validity of the findings.

4. Data Analysis and Results

This study evaluated the VCP mode through an objective knowledge assessment and a subjective experience survey. The empirical results presented in this section are directly derived from the evaluation instrument detailed in Table 1, quantifying both objective cognitive accuracy and subjective spatial recognition satisfaction.
(1)
Basic information about respondents and objective knowledge assessment
Among the 147 valid responses, 68 (46.26%) were female and 79 (53.74%) were male. The sample comprised 60 students (40.82%) in Cohort A (no prior on-site experience) and 87 students (59.18%) in Cohort B (with prior on-site experience).
To provide readers with a transparent view of the system’s automated feedback mechanism, Figure 6 illustrates the user interface of the “Effectiveness Analysis” output screen. This module functions as a formative assessment tool; upon concluding the virtual cognitive practice, it generates a real-time report summarizing the individual student’s total learning time and preliminary quiz scores to help them gauge their immediate learning status.
However, to ensure methodological rigor and standardized evaluation across all cohorts, the formal objective cognitive accuracy results analyzed in this study were aggregated through the subsequent structured questionnaire survey. To systematically quantify objective knowledge mastery, explicit scoring criteria and analytical benchmarks were established for the assessment items. Each objective question embedded in the survey was evaluated using a strict binary scoring criterion: 1 point was awarded for a correct answer and 0 points for an incorrect one. The accuracy rate benchmark for each engineering category (road, bridge, and tunnel) was subsequently calculated as the ratio of the total correct scores achieved by all respondents to the maximum possible scores (i.e., the total number of valid responses, N = 147). The derived assessment results are intimately linked to the specific engineering parameters tested in the VCP scenarios (as exemplified in Table 1). Specifically, in the domain of road engineering—where students were asked to identify specific parameters such as the pavement material of Huangxing Avenue—the accuracy rate peaked at 95.24%. Similarly, when assessing structural classification competencies, such as identifying the stress form of the Yinpenling Bridge, students achieved an accuracy rate of 89%. Furthermore, the accuracy rate for recognizing tunnel geometric parameters, such as the lane configuration of the Liuyanghe Tunnel, stood at 88%. These variations suggest that students achieved a better grasp of less structurally complex projects compared to the intricate internal configurations of bridges and tunnels, which require higher levels of spatial visualization.
(2)
Evaluation of subjective experience
Consistent with the specific spatial recognition competencies assessed in the questionnaire (Table 1), student satisfaction scores for the VCP mode’s visualization effects are presented in Figure 7 and Figure 8. The mean score for the visual representation of roadbed forms (e.g., embankments, cuttings, and half-fill/half-cut types) was 4.22 out of 5, with the largest proportion of students (47.62%) rating it as “good”. Regarding bridge engineering, the display of cable-stayed bridge components (e.g., towers, cables, box girders, and piers) received a mean score of 4.28 (48.98% “good”), while ancillary bridge facilities (e.g., expansion joints and guardrails) scored 4.33 (44.90% “good”). For tunnel engineering, emergency facilities (e.g., transverse passageways) achieved a mean score of 4.35 (48.98% “very good”), and drainage facilities scored 4.18 (42.18% “very good”). Overall, the mean visualization score across all categories was 4.27, with more than 85% of valid subjective evaluations falling into the “good” or “very good” categories. In summary, these results indicate a high level of student satisfaction with the VCP mode’s ability to represent complex engineering scenarios.
The students’ perceptions of the advantages and disadvantages of the VCP mode are presented as word clouds in Figure 9a,b. In these visualizations, the font size correlates with the frequency and prominence of a particular viewpoint. The most frequently cited advantages indicate that the VCP mode enables them to observe diverse projects at any time and from multiple locations and angles, providing a robust sense of spatial visualization. On the other hand, the primary disadvantages noted were the “lack of custom-captured panoramas,” the absence of housing engineering projects, insufficient AR integration, and occasional image blurring or obstructions. These findings suggest that while the current advantages significantly enhance practical learning effectiveness, the identified drawbacks have only a limited impact on the overall educational outcome. Furthermore, the limitations highlighted by respondents provide clear directions for the future optimization and iterative development of the VCP system.
(3)
Difference analysis
The students from Cohort A who participated in this VCP and survey had no prior on-site cognitive practice experience, whereas the students from Cohort B had completed on-site sessions. The differences in feedback between these two groups were analyzed using the Chi-square (χ2) test.
Regarding Question 1—“Do you think it is possible to replace traditional cognitive practice with the VCP mode in EMM when off-campus visits are unfeasible?”—the results of the χ2 test are summarized in Table 2. Overall, 78.3% of respondents believed replacement was possible, 16.3% were uncertain, and 5.4% deemed it impossible. The p-value was 0.028 (p < 0.05); therefore, the null hypothesis is rejected, indicating that students with different experiential backgrounds held significantly different views on this issue. To contextualize these quantitative findings, representative qualitative feedback from the survey was analyzed. Cohort A students selected “Uncertain” (22.0%) and “No” (10.0%) more frequently. Their representative responses (e.g., “I haven’t participated in on-site practice, so I am not entirely sure if the VCP comprehensively covers all the content and problem-solving experiences of traditional practice”) suggest a lack of confidence due to the absence of a physical benchmark. In contrast, Cohort B students possessed a comparative perspective. Their feedback (e.g., “Having participated in both on-site and VCP practice, I find VCP covers all key observation points of traditional practice, making it a reliable alternative”) highlights a strong recognition of the VCP’s efficacy, leading to their significantly higher conviction (85.1%) in its potential as a viable substitute (verifying Hypothesis 3).
Regarding Question 2—“Do you think it is still necessary to integrate the VCP mode with the traditional cognitive practice mode in EMM when conditions for off-campus visits are met?”—the results of the χ2 test are summarized in Table 3. Overall, 89.8% of the respondents believed such integration was necessary, while 6.8% expressed uncertainty, and only 3.4% deemed it unnecessary. The p-value was 0.457 (p > 0.05); thus, we failed to reject the null hypothesis. The logic behind this universal consensus is further supported by the response characteristics. Regardless of prior experience, students explicitly recognized the complementary value of the system. Representative feedback from Cohort A emphasized pre-learning (e.g., “VCP can be used to preview key knowledge before on-site practice, so we can focus on observing difficult points during the visit”), while Cohort B emphasized post-visit review (e.g., “On-site practice is irreplaceable for sensory experience, but VCP allows post-visit review of structural details—integrating them makes the learning process more comprehensive”). These qualitative insights strongly support the conclusion that the VCP mode serves as a vital pedagogical complement to traditional on-site practice, optimizing the overall learning path (verifying Hypothesis 4).

5. Discussion

5.1. Discussion of Results and Suggestions

The analysis suggests that the VCP mode in EMM demonstrates promising effectiveness, serving as a viable supplementary or alternative approach to on-site cognitive practice. Specifically, the empirical findings reveal an objective knowledge test accuracy rate exceeding 80% across all engineering categories, coupled with a high overall visualization satisfaction score (mean 4.27/5). This aligns with recent studies indicating that context-aware virtual learning environments can effectively reduce cognitive load and enhance perceptual knowledge acquisition [1,17]. Furthermore, the difference analysis indicates that students with prior on-site experience (Cohort B) showed a significantly higher recognition of the VCP mode’s replacement value (p = 0.028). This resonates with existing theories that prior physical benchmarks fundamentally influence students’ perceptions and validation of ubiquitous learning tools [14]. When designing pedagogical plans, instructors can develop online and offline components simultaneously. While the VCP can effectively substitute for on-site visits when conditions are unfavorable, a hybrid approach (VCP combined with physical visits) can significantly amplify learning outcomes. Nevertheless, the VCP mode can be further optimized in the following ways:
(1) Curating Complex Engineering Cases: Incorporating a broader range of bridge and tunnel projects will facilitate knowledge reinforcement and comparative learning.
(2) Enhancing Panoramic Quality: Transitioning from reliance on third-party web maps to bespoke, high-definition panoramas (including those of residential engineering) will improve visual clarity and richness.
(3) AR Integration: Fusing the VCP mode with Augmented Reality (AR) will enhance immersion and interactivity, providing students with heightened sensory experiences.
(4) BIM Implementation: Utilizing Building Information Modeling (BIM) to render critical components in 3D can provide vivid representations of internal structures that are otherwise inaccessible during physical or standard virtual tours.

5.2. Theoretical and Practical Contributions

Theoretically, this study bridges the gap between U-learning and engineering management pedagogy, addressing technical hurdles in online cognitive practice. These findings innovate talent cultivation methods and confirm that U-learning enhances the flexibility and personalization of the educational process. This contributes to the broader academic discourse by providing empirical evidence from EMM undergraduate cohorts, paralleling the findings of Shojaei et al. [9] regarding the efficacy of virtual site visits in construction management distance education.
Practically, this approach effectively mitigates the core constraints of traditional on-site practice for EMM. As noted by Sun et al. [22], virtual collaborative spaces successfully decouple learning from physical proximity to project sites. Consequently, the web-map-based VCP mode alleviates challenges related to logistical restrictions, safety risks, limited perspectives, and the lack of diverse scenarios inherent in traditional field trips.

6. Conclusions

This study explores the integration of U-learning into engineering management education by proposing a VCP mode for EMM that utilizes web map panoramas. Through the development and implementation of the VCP system, students conducted virtual practices, the effectiveness of which was validated via a comprehensive questionnaire survey. The findings demonstrate that the VCP mode allows EMM students to perform virtual visits and remote observations of engineering projects anytime and anywhere, effectively overcoming the technical hurdles of online cognitive practice. This approach fosters innovation in pedagogical methods, addressing the inherent safety and logistical limitations of traditional on-site internships.
Based on the empirical data analysis, the formulated research questions and hypotheses were systematically addressed and verified. Regarding pedagogical effectiveness (RQ1), the VCP mode significantly enhances professional knowledge acquisition and fulfills perceptual learning needs. Specifically, students achieved an objective knowledge accuracy rate exceeding 80% across all engineering categories, and their subjective visualization satisfaction reached a high mean score of 4.27/5. These robust quantitative results directly verify H1 and H2, confirming that the panoramic displays successfully provide the necessary spatial depth and structural clarity comparable to on-site practice. Concerning students’ pedagogical attitudes (RQ2), the evaluation revealed nuanced perceptions based on experiential backgrounds. Pearson’s Chi-square analysis showed a statistically significant difference (p = 0.028 < 0.05) between cohorts regarding the mode’s substitution value, thereby verifying H3 and indicating that prior physical benchmarks positively influence students’ confidence in virtual substitution. Concurrently, H4 was verified by the lack of significant difference (p = 0.457 > 0.05) between cohorts regarding mode integration. This demonstrates a universal and consistent consensus—supported by nearly 90% of all respondents—on the necessity of integrating the VCP with traditional on-site visits to form a complementary, hybrid practice ecosystem. Nevertheless, future research can further enhance VCP effectiveness by generating bespoke high-definition panoramas, incorporating housing engineering projects, and integrating AR technology to improve visual clarity and immersion. Furthermore, since operational production practice is another critical facet of EMM that faces similar safety and accessibility challenges, subsequent studies should explore virtual production practice modes. By utilizing 3D animations to depict key structural procedures, higher education can further dissolve the bottlenecks of practical learning and significantly bolster student learning outcomes. Additionally, this VCP methodology has significant potential for broader applications beyond civil engineering, such as virtual visits to industrial facilities or cultural heritage sites.

Author Contributions

Conceptualization, D.L. and Y.H.; methodology, F.L. and W.L.; validation, D.L. and R.B.; investigation, F.L. and Y.H.; writing—original draft preparation, Y.H.; writing—review and editing, R.B. and W.L.; funding acquisition, D.L. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Hunan Provincial Research Project on Teaching Reform in Regular Undergraduate Colleges and Universities (No. 202401000607), the Hunan Province Degree and Graduate Teaching Reform Research Project (No. 2023JGYB167), and the Changsha University of Science & Technology Degree and Graduate Teaching Reform Research Project (No. CLYJSJG25007).

Institutional Review Board Statement

According to the policies of relevant institutions and the requirements of national guidelines, this non-interventional educational study meets the conditions for exemption from formal ethical review. Ethical review and approval were waived by Changsha University of Science & Technology due to all the survey data having been anonymized and not containing personal identification information.

Informed Consent Statement

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

Data Availability Statement

Data are available upon request.

Acknowledgments

During the preparation of this manuscript, the author used Doubao (online web version, powered by Seed 2.0 foundation model, 2026) for the creation of character images in Figure 5. The author has reviewed and edited the output and takes full responsibility for the content of this publication.

Conflicts of Interest

No potential conflicts of interest were reported by the author(s).

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Figure 1. Complex site environments: (a) Students gathered near the river; (b) Students gathered alongside the main road.
Figure 1. Complex site environments: (a) Students gathered near the river; (b) Students gathered alongside the main road.
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Figure 2. Creation process of virtual cognitive scenarios in traffic engineering.
Figure 2. Creation process of virtual cognitive scenarios in traffic engineering.
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Figure 3. Heishipu Bridge virtual cognitive scenarios: (a) Bridge Deck; (b) Steel Arch; (c) Bridge Approach; (d) Concrete Arch.
Figure 3. Heishipu Bridge virtual cognitive scenarios: (a) Bridge Deck; (b) Steel Arch; (c) Bridge Approach; (d) Concrete Arch.
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Figure 4. Operation interface of the VCP system: (a) Panoramic view of the bridge structure observed from the deck of H Bridge; (b) Panoramic view of the bridge structure observed from the west bank of H Bridge. The central blank area is the knowledge point display area, triggered by clicking interactive hotspots in the panorama.
Figure 4. Operation interface of the VCP system: (a) Panoramic view of the bridge structure observed from the deck of H Bridge; (b) Panoramic view of the bridge structure observed from the west bank of H Bridge. The central blank area is the knowledge point display area, triggered by clicking interactive hotspots in the panorama.
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Figure 5. The VCP mode.
Figure 5. The VCP mode.
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Figure 6. The output screen of the Effectiveness Analysis function.
Figure 6. The output screen of the Effectiveness Analysis function.
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Figure 7. The evaluation score for the display effect of various scenarios.
Figure 7. The evaluation score for the display effect of various scenarios.
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Figure 8. The proportion of different levels of satisfaction for various scenarios.
Figure 8. The proportion of different levels of satisfaction for various scenarios.
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Figure 9. The evaluation results of the advantages and disadvantages: (a) Advantages; (b) Disadvantages.
Figure 9. The evaluation results of the advantages and disadvantages: (a) Advantages; (b) Disadvantages.
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Table 1. Structure, assessed competencies, and specific examples of the evaluation questionnaire.
Table 1. Structure, assessed competencies, and specific examples of the evaluation questionnaire.
Questionnaire DivisionQuestion TypeAssessed Knowledge/CompetenciesSpecific Question Examples
Basic information about respondentsSingle-choiceSample Demographics: Establishing sample characteristics and prior on-site experience.“Which class Cohort do you belong to?”
“Gender.”
Objective Knowledge AssessmentSingle-choice (Direct testing)Engineering Parameter Recognition: Ability to observe and identify specific geometric and material parameters.
Structural Classification: Competency to categorize infrastructure based on force characteristics and structural forms observed in the VCP.		“How many lanes does the Liuyanghe Tunnel have? (Options: 4, 6, or 8 lanes)”
Evaluation of subjective experience5-point Likert Scale and Multiple-choicePedagogical Integration Attitudes: Students’ analytical judgment on the system’s advantages, disadvantages, and its value as a substitute or complement.“Rate the visual recognition effect of tunnel emergency facilities (e.g., vehicle and pedestrian cross passages) on a scale of 1 to 5.”
Table 2. Difference analysis for relevant issues without the conditions for going out and visiting.
Table 2. Difference analysis for relevant issues without the conditions for going out and visiting.
QuestionChoiceCohortSummationχ2 (df)Value of p
AB
1Yes68.0%85.1%78.3%7.1 (2)0.028
No10.0%2.3%5.4%
Uncertain22.0%12.6%16.3%
Note: p < 0.05. The null hypothesis is rejected, indicating a statistically significant difference between Cohort A (no on-site experience) and Cohort B (with on-site experience) in their attitudes toward the replacement.
Table 3. Difference analysis for relevant issues when conditions for going out and visiting are met.
Table 3. Difference analysis for relevant issues when conditions for going out and visiting are met.
QuestionChoiceCohortSummationχ2 (df)Value of p
AB
2Yes88.3%90.8% 89.8% 1.6 (2)0.457
No5.0%2.3% 3.4%
Uncertain6.7%6.9%6.8%
Note: p > 0.05. No statistically significant difference was found between the two cohorts, indicating a consistent strong preference for a hybrid approach across groups.
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Huang, Y.; Liu, F.; Liu, D.; Liu, W.; Bu, R. Ubiquitous Virtual Cognitive Practice Mode in Engineering Management Utilizing Web Map Panoramas: Application and Effectiveness Analysis. Systems 2026, 14, 492. https://doi.org/10.3390/systems14050492

AMA Style

Huang Y, Liu F, Liu D, Liu W, Bu R. Ubiquitous Virtual Cognitive Practice Mode in Engineering Management Utilizing Web Map Panoramas: Application and Effectiveness Analysis. Systems. 2026; 14(5):492. https://doi.org/10.3390/systems14050492

Chicago/Turabian Style

Huang, Yao, Fubin Liu, Dingli Liu, Weijun Liu, and Rongwei Bu. 2026. "Ubiquitous Virtual Cognitive Practice Mode in Engineering Management Utilizing Web Map Panoramas: Application and Effectiveness Analysis" Systems 14, no. 5: 492. https://doi.org/10.3390/systems14050492

APA Style

Huang, Y., Liu, F., Liu, D., Liu, W., & Bu, R. (2026). Ubiquitous Virtual Cognitive Practice Mode in Engineering Management Utilizing Web Map Panoramas: Application and Effectiveness Analysis. Systems, 14(5), 492. https://doi.org/10.3390/systems14050492

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