Network Analysis of Outcome-Based Education Curriculum System: A Case Study of Environmental Design Programs in Medium-Sized Cities

Abstract

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes.

1. Introduction

With the deepening of global higher education reforms, teaching models grounded in outcome-based education (OBE) principles have garnered widespread attention in the international education community [1]. Currently, Chinese higher education is undergoing an extensive specialty structure adjustment of academic disciplines, characterized by professional optimization and restructuring of unparalleled depth and breadth [2,3]. Real estate industry structural adjustments have created profound developmental challenges for Environmental Design, manifesting in multidimensional erosion of professional sustainability—encompassing disciplinary legitimacy (questioning of the program’s academic relevance and social value), pedagogical effectiveness (declining coherence between curriculum content and evolving industry demands), and career pathway viability (diminishing confidence in graduates’ professional prospects)—alongside significantly contracted graduate employment market opportunities [4]. Medium-sized cities’ universities face acute challenges in Environmental Design: declining student source quality reflecting broader concerns about program relevance and career security; reduced applicants indicating weakened market confidence in the discipline’s future prospects; inadequate teaching resources characterized by faculty struggling to bridge traditional design pedagogy with emerging digital competencies; and shallow university–enterprise cooperation stemming from industry uncertainty about long-term partnerships with programs perceived as potentially unstable. These interconnected challenges create major transformation difficulties that transcend simple resource allocation issues, requiring fundamental reconceptualization of the discipline’s identity and educational mission within the contemporary urban development context Issues include declining student source quality, reduced applicants, inadequate teaching resources, and shallow university–enterprise cooperation, creating major transformation difficulties [5]. Facing this structural sustainability crisis—where professional sustainability encompasses the program’s capacity to maintain educational coherence, preserve stakeholder confidence, and ensure graduate readiness for evolving professional landscapes—ow to systematically reconstruct Environmental Design talent cultivation systems through OBE principles to enhance students’ core competencies and market adaptability has become a critical research question requiring urgent investigation. Professional sustainability, in this context, represents the dynamic equilibrium between educational integrity, industry relevance, and student development outcomes that enables programs to thrive rather than merely survive institutional and market pressures. Therefore, this study takes a breakthrough approach using OBE-based pedagogical design and network analysis to construct indirect course dependencies and influence pathways. Previous studies on OBE have predominantly relied on linear analysis methods, which fail to capture the complex interdependencies and temporal dynamics among courses, leaving a gap in understanding how curriculum structures influence teaching effectiveness [6,7]. This study addresses this gap by employing network analysis to model the Environmental Design curriculum system, revealing structural patterns and temporal dependencies that inform targeted reforms. Network analysis is particularly suited to this context because it quantifies complex relationships, identifies critical courses as knowledge hubs, and provides a data-driven basis for optimizing curriculum design, extending beyond traditional qualitative evaluations [8]. This approach builds on prior work by integrating network centrality metrics with OBE principles, offering a novel framework for curriculum evaluation and reform. Through a detailed examination of complex supportive relationships and temporal dependency characteristics among courses using network centrality metrics, this research reveals the fundamental structural patterns of curriculum systems and the varied expressions of teaching effectiveness. Targeted policy strategies and methods, grounded in network optimization, are proposed. These aim to improve the quality of educational and teaching quality, refine talent training models, and foster all-round development of students.

2. Review of Related Research

2.1. The Theoretical Basis and Development of OBE

Since its emergence in the 1990s, OBE has evolved from an educational concept into a key driver of global education reform. The core value of this educational paradigm lies in its fundamental change from teacher-centered to student-centered approaches, with the entire educational process designed backward from clearly defined learning outcomes [9]. Its theoretical basis integrates constructivism learning theory, competence-based education theory, and constructive alignment theory, forming a comprehensive theoretical framework [1]. The introduction of inverse design theory has further refined the operational methods of OBE, enabling the systematization implementation of an achievement driven teaching model, thereby transforming the traditional content-driven teaching approach [10,11,12].

The successful application of OBE in engineering education has provided valuable practical experience for other disciplines. The Accreditation Board for Engineering and Technology has established OBE as a core accreditation standard, effectively addressing the disconnection between theory and practice [13]. Although China’s research on OBE started relatively late, it has made significant progress in theoretical research and application promotion through native interpretation and systematization implementation [14]. Chinese scholars have not only developed implementation strategies tailored to domestic education Practice but also systematically validated the rationality and effectiveness of the OBE teaching model from multiple perspectives, including its theoretical foundations and practical feasibility, providing critical theoretical support and practical guidance for Chinese higher education reform [15,16,17].

2.2. Limitations and Challenges of Existing OBE

Although the theory of OBE has become mature, deep-seated issues exposed during its implementation have hindered the effective dissemination of this educational paradigm. The chasm between theory and practice remains the most prominent obstacle. Many research findings remain at an abstract level, failing to provide practical educators with actionable guidance, while some practical explorations lacking a theoretical basis struggle to yield replicable models of experience [6,7]. Meanwhile, the imperfect problems of evaluation systems have become more obvious. Current evaluation methods primarily rely on qualitative analysis, lacking systematic rigor and scientific validity in key aspects such as indicator design, method selection, and result application [18,19]. This is particularly pronounced in long-term impact assessment and cross-disciplinary comparisons, where effective evaluation tools and feedback mechanisms are absent.

Insufficient localization and methodological limitations have further exacerbated the difficulties of implementing OBE. The transfer of OBE principles from Western to other cultural contexts often overlooks local educational traditions and cultural characteristics, resulting in suboptimal implementation outcomes. This is particularly evident in developing countries such as China, where constructing an OBE implementation model with local characteristics remains a critical area for further exploration [20,21]. Meanwhile, traditional curriculum system analysis methods rely on linear thinking, struggling to accurately capture the complexity and systemic nature of curriculum systems. They overlook interdependencies and supportive relationships among courses, lacking quantitative tools and objective metrics to scientifically evaluate the structural characteristics of curriculum systems [22,23,24]. The methodological limitations not only hinder a correct grasp of the current state of curriculum systems but also undermine the scientific rigor of curriculum reform and optimization, thereby impeding the deepening application of OBE principles across diverse educational domains.

2.3. Innovation and Application of Network Analysis Methods in OBE

Network analysis methods, as systemic analytical tools, have enhanced the vitality and scientific rigor of OBE. This methodological innovation not only enhances the scientific rigor and accuracy of curriculum system analysis but also provides more precise guidance for curriculum optimization and teaching reform. Previous educational network studies have primarily focused on (1) student social networks and academic performance correlations [25,26], (2) knowledge concept mapping in STEM education [8,27], and (3) institutional collaboration networks [24,28,29]. However, these studies differ fundamentally from our approach in several key aspects: First, existing studies analyze static relationships, while our dual-network model (CSCN and CTN) captures both structural dependencies and temporal dynamics simultaneously. Second, previous research focuses on individual student outcomes rather than systematic curriculum structure evaluation. Third, no prior study has integrated OBE graduation indicators with network centrality metrics to quantify course importance in competency development frameworks.

The successful application of network analysis methods in fields such as engineering, medical, and business education demonstrates their practical value, yet their use in OBE continues to face methodological challenges [30,31,32]. The scientific rigor and accuracy of network model construction are critical to the reliability of analytical results. Ensuring that these models accurately reflect the structural characteristics of curriculum systems remains a key challenge in applying this methodology [33,34]. More critically, the interpretation and application of network analysis results must be integrated with specific educational contexts and objectives, and the effective translation of quantitative network metrics into concrete educational improvement measures represents the determining factor for whether this methodology can truly demonstrate its efficacy. These challenges highlight the need for further theoretical refinement and empirical validation to effectively translate network analysis methods into educational practice in OBE research.

Based on a comprehensive critique of existing OBE limitations and systematic evaluation of network analysis advantages, this study investigates how network methods can construct scientifically rigorous assessment models for OBE curriculum systems. The study addresses the critical challenge of traditional analytical methods not being able to precisely identify key nodes and weak links within curriculum frameworks. This research provides innovative frameworks for understanding complex course interdependencies.

The innovative point of this study is primarily reflected in four key breakthroughs: At the network construction level, the structural characteristics of the curriculum system are comprehensively depicted through two network models: the curriculum support and contribution network (CSCN) and the course temporal network (CTN). At the network centrality analysis level, quantitative indicators are employed to evaluate the importance and influence of individual courses. At the structural diagnosis, structural problems and weak links within the system are identified. Finally, at the optimization strategy level, specific improvement recommendations are proposed based on the analytical results. The proposed theoretical framework provides a rigorous analytical pathway for this study and bridges the gap between theory and practice in OBE by integrating network analysis with it principles, offering significant theoretical innovation and practical value (Figure 1).

3. Data Description

This study focuses on Environmental Design majors at Hubei Engineering University’s School of Arts and Design (https://msxy.hbeu.edu.cn/wzsy.htm, accessed on 12 March 2025). A dataset comprising 46 specialized courses and 6 graduation requirement indicators was constructed to assess pedagogical effectiveness (Figure 2). This study includes two consecutive cohorts of Environmental Design undergraduates enrolled in 2022 ($n=74$) and 2023 ($n=73$), representing the entire target population of 147 students (https://jwc.hbeu.edu.cn/xxpt.htm, accessed on 12 March 2025). All participants completed a sequence of specialized core courses, spanning Foundational Sketching to Environmental Design Innovation. As a representative medium-sized city in central Hubei Province, Xiaogan City possesses both a geographically strategic location and a moderate level of economic development, making it an ideal contextual sample for practice-oriented research on environmental design education in local universities. The dataset systematically integrates graduation requirement indicators across 6 dimensions: ideological literacy, professional quality, technical language, knowledge theory, professional integration, and development. This structure forms a multi-level evaluation system that spans from ideology and morality to professional skills. The findings from the Environmental Design dataset provide valuable insights and practical guidance for optimizing curricula and enhancing quality at universities in central China.

4. Research Method

This study employs network analysis to evaluate the structure and effectiveness of the environmental design curriculum at Hubei Engineering University, with a focus on the relationships between courses and their temporal sequences. Data were sourced from course syllabi, teacher evaluations, and student feedback for 46 specialized courses. Syllabi were analyzed using content analysis to identify prerequisite relationships and knowledge support dependencies, coded as binary relationships (1 for direct dependency, and 0 otherwise). Teacher evaluations and student feedback were qualitatively coded to validate these dependencies, ensuring alignment with pedagogical objectives. For the CSCN, adjacency matrix A was constructed, where element $a_{ij}=1$ if course i directly supports course j’s learning outcomes (e.g., through shared knowledge or skills), and $a_{ij}=0$ otherwise, based on syllabus objectives and validated by faculty input. For the CTN, adjacency matrix B was constructed, where $b_{ij}=1$ if course i is a prerequisite for course j in the temporal sequence, and $b_{ij}=0$ otherwise, derived from curriculum scheduling records. These rules ensure that the matrices accurately reflect logical and temporal relationships. The application of constructing an adjacency matrix to capture indirect relationships between courses and applying three key network centrality metrics: degree centrality, betweenness centrality, and closeness centrality to quantify the importance of courses within the system.

4.1. Network Construction

This study constructs two distinct networks (CSCN and CTN) to model the curriculum system. These networks use adjacency matrices to represent relationships between courses. To capture indirect relationships between courses, this study calculates adjacency matrix for both networks [35]. For the CSCN, the adjacency matrix $A^{\left(2\right)}$ is derived as $A^2$, where the element $a_{ij}^{\left(2\right)}$ represents the number of two-step paths from the course i to course graduation indicator j via an intermediate course indicator. Two-step paths were selected because they effectively capture indirect dependencies (e.g., a foundational course influencing an advanced course through an intermediate course) while maintaining analytical tractability and practical interpretability for identifying key curriculum connections for optimization. Higher-order paths were avoided to prevent overcomplication, as they add marginal analytical value in this context. Similarly, for the CTN, the element $b_{ij}^{\left(2\right)}$ represents the number of two-step paths from course i to course j via an intermediate course, with $B^{\left(2\right)}=B^2$ reflecting two-step time-dependent relationships. This approach enhances analytical depth by identifying courses that indirectly influence others through intermediate nodes. To visualize the strength of relationships in the network figures, edge weights are introduced in the adjacency matrices for both CSCN and CTN. For the CSCN, edge weights are calculated based on the number of shared graduation requirement indicators (e.g., ideological literacy, professional quality, technical language) between courses, with higher weights assigned to course pairs sharing multiple indicators, reflecting stronger knowledge dependencies. For the CTN, edge weights are determined by the strength of temporal dependencies, quantified by the frequency and significance of sequential course prerequisites, where stronger prerequisites (e.g., mandatory sequential courses) are assigned higher weights. These weights are represented in the figures by varying edge thicknesses and color intensities, with thicker and more intense lines indicating stronger relationships.

The adjacency matrix of the CSCN is defined as follows:

$$a_{ij}^{\left(2\right)}=\left\{\begin{array}{c}1\mathrm{If}\mathrm{the}\mathrm{course}i\mathrm{support}\mathrm{to}\mathrm{graduation}\mathrm{indicators}j\hfill \\ 0\mathrm{otherwise}\hfill \end{array}\right.$$

(1)

The data used to construct this network were derived from course syllabi, teacher evaluations, and student feedback to ensure a comprehensive representation of support relationships.

The adjacency matrix of the CTN is defined as

$${\mathrm{b}}_{ij}^{\left(2\right)}=\left\{\begin{array}{c}1\mathrm{If}\mathrm{the}\mathrm{course}i\mathrm{Support}\mathrm{to}\mathrm{the}\mathrm{course}j\hfill \\ 0\mathrm{otherwise}\hfill \end{array}\right.$$

(2)

Temporal relationships are determined based on the official curriculum schedule and teaching plan of the four-year environmental design course cycle.

4.2. Assessment of Network Centrality Indicators

This study employs three centrality metrics to evaluate the importance of courses in constructing the network, with each metric quantifying course importance from a distinct perspective [36]. Degree centrality measures the number of direct connections of a course, reflecting its local influence (Equation (3)). Betweenness centrality quantifies the bridging role of a course in the network by measuring the proportion of shortest paths passing through it (Equation (4)). Closeness centrality evaluates the global influence of a course by calculating the reciprocal of the average shortest path distance to all other courses (Equation (5)). Thus, using a comprehensive analysis of these metrics, key courses and weaknesses in the curriculum system can be identified, providing a quantitative basis for optimizing course design (

Table 1. The network analysis index.

Name	Formula	Description
Degree Centrality ($D_{Ci}$)	$$D_{Ci}=\sum _{j=1}^n{a}_{ij\left(ji\right)}\left(i\ne j\right)$$ (3)	$a_{ij\left(ji\right)}$ represents an element of the adjacency matrix A, indicating whether nodes i and j are connected.
Closeness centrality ($B_{Ci}$)	$$C_B\left(i\right)=\sum _{s\ne i\ne t}{\displaystyle \frac{{\sigma }_{st}\left(i\right)}{{\sigma }_{st}}}$$ (4)	${\sigma }_{st}$ denotes the total number of shortest paths from course s to course t, and ${\sigma }_{st}\left(i\right)$ represents the number of shortest paths from course s to course t that through course i.
Closeness centrality ($C_{Ci}$)	$$C_C\left(i\right)={\displaystyle \frac{n-1}{{\sum }_{j=1:j\ne i}^{n}d\left(i,j\right)}}$$ (5)	$d\left(i,j\right)$ denotes the shortest path distance from course i to course j.

).

4.3. Data Standardization

To enable a comparison across networks of different sizes, the centrality metrics were standardized. This approach ensures consistent metric values across networks of different sizes, enabling analysis of network structure stability and clearly reflecting the relative importance of nodes.

$$z={\displaystyle \frac{x-\mu }{\sqrt{\frac{1}{N}{\sum }_{i=1}^N{\left(x_i-\mu \right)}^2}}}.$$

(6)

Here, x is the actual variable value and ∑ is the mean value.

5. Results

5.1. Computer Basics Courses Serve as Critical Hubs and Interdisciplinary Bridges Within Knowledge Transmission Networks

Network centrality analysis reveals significant differences in node importance between CSCN and CTN, reflecting complex patterns of interdisciplinary knowledge transmission and temporal dependencies (Figure 3). In the CSCN, degree centrality analysis reveals that computer basics courses such as Design Application (DAP) and Professional Knowledge (PK) occupy central positions within the network. These courses exhibit the highest connectivity, indicating their pivotal role as knowledge hubs in the overall curriculum system. They provide essential foundational knowledge for numerous subsequent courses while simultaneously depending on support from multiple prerequisite courses. Betweenness centrality analysis corroborates these findings, demonstrating that DAP and PK course graduation indicators exhibit high connectivity while strategically positioning themselves as critical intermediary nodes across multiple knowledge transmission pathways, thereby serving as interdisciplinary bridges essential for student progression in computer science education. Closeness centrality metrics indicate that these foundational course graduation indicators exhibit minimal average path lengths to all other network nodes, thereby facilitating efficient knowledge propagation and demonstrating their strategic positioning for network-wide influence. The CTN analysis demonstrates that the course network exhibits strong sequential ordering, with early-stage foundational courses including 3DSAMX+VRAY Indoor Digital Environment Design (3dmaxV) and Interior Design Methods (idm) showing elevated centrality values across multiple metrics, thereby validating the hierarchical structure of knowledge prerequisites. Integrative centrality analysis across CSCN and CTN reveals that foundational courses maintain central positions within the educational ecosystem, serving as critical determinants of student learning trajectories and knowledge acquisition efficiency, while substantially influencing the overall quality of professional talent cultivation.

5.2. The OBE Support Network Shows Core–Periphery Structure with Professional Courses as Central Hubs

Statistical analysis of network centrality metrics demonstrates significant structural differentiation between the OBE CSCN and CTN, with distinct patterns of node importance distribution that reflect the multidimensional complexity of inter-course relationships (Figure 4). Degree centrality metrics demonstrate pronounced core–periphery structures within the CSCN, characterized by high-degree professional core course graduation indicators and courses ($PK=20$, $Dap=23$, design reflection $\left(Dre\right)=14$, professional integration $\left(PI\right)=12$), an intermediate stratum with degree values of 3 and 5, and peripheral nodes with zero connectivity (The Second Classroom and Innovation and Entrepreneurship Practice (TsCIp), Comprehensive Practical Training on Spatial Innovation (cptsi), Learning Ability (Lab)), reflecting stratified functional specialization. Betweenness centrality metrics reveal pronounced pathway concentration within the knowledge transmission network, characterized by dominant bridging nodes including Research on Thesis Writing Methods and Design Proposal ($Rtwmdp=806.474$), competition practice ($Cp=768.102$), and planning and design of beautiful villages ($Pdbvil=680.708$), contrasted with moderate-range values (100 and 500) for most courses and near-zero values for peripheral nodes (Tool Language (TL), Information Acquisition (IA)). Network analysis reveals that core course graduation indicators, PK, Dap, and Dre, achieve high closeness centrality values ($0.41$ and $0.47$), indicating their strategic positioning for efficient knowledge dissemination throughout the curriculum network, with overall network closeness centrality values concentrated between 0.25 and 0.45.

In contrast, the CTN exhibits distinctly different centrality distribution characteristics: In terms of degree centrality, the network structure is more flattened, with the highest degree value being only 8 (3dmaxV and idm courses). The Graduation Project and Design Report (Gpdre) and Landscape Design Methods (Lame) courses rank in the second tier with a degree value of 6, while the majority of courses have degree centrality ranging from 2 to 5. The uniform distribution of courses within the CTN reflects balanced scheduling and highlights temporal constraints on course connections. Betweenness centrality analysis reveals critical temporal nodes within the CTN. The Gpdre course emerges as the core regulator of the CTN with the highest betweenness centrality value of $942.452$, followed by the History of Chinese and Foreign Art Design (HCFa) course ($843.159$) and the Lame course ($801.347$) ranking second and third, respectively. These high betweenness centrality courses play pivotal roles in curriculum scheduling and temporal coordination. Notably, numerous courses exhibit zero betweenness centrality, indicating that they primarily function as terminal or initial nodes in the temporal network without participating in intermediary transmission processes. In terms of closeness centrality, the CTN exhibits relatively low closeness centrality values overall. The highest value is $0.425532$ for the Office Space Design (offsd) courses, with most courses concentrated within the range of 0.25–0.38. This relatively low closeness centrality reflects the complexity and path-dependent characteristics of knowledge transmission among courses under temporal constraints. Comparative analysis of the two networks reveals that the support and contribution network emphasize logical dependency relationships among courses, exhibiting pronounced centralization characteristics where a few core courses undertake primary connectivity and intermediary functions. In contrast to static networks, the CTN reflects course schedules’ chronological sequence and progression, with a dispersed structure retaining key regulatory courses at critical temporal nodes. The integrated centrality analysis of both networks demonstrates that the OBE curriculum system exhibits reasonable hierarchical and systematic characteristics in both logical structure and temporal arrangement. This system ensures the knowledge framework’s integrity and coherence, balancing methodological rigor with instructional feasibility, thus providing data-driven guidance for optimizing curriculum structure and enhancing teaching effectiveness.

5.3. Course Network Integration Effects and Optimization Outcomes

The network construction of OBE based on environmental design exhibits multi-level and systematic characteristics of curriculum support and contribution relationships (Figure 5). The network system establishes a knowledge chain from theory to application via the CSCN. The core courses and course graduation indicators represented by yellow nodes form the backbone of technical skill development, while the complex interdependencies and support relationships among these courses and objectives are illustrated through connecting lines of different colors (red, blue, green, etc.). This ensures that students can build a comprehensive professional cognitive system based on mastering core knowledge in environmental design fundamentals, design principles, and technical specifications. Simultaneously, the system facilitates progressive advancement into specific project practice, innovative design thinking cultivation, and interdisciplinary integrated applications. The CTN on the right side of the network further reveals the temporal logic and sequential order of curriculum implementation, clearly delineating a progressive pedagogical pathway from foundational environmental design cognition through professional skill training to comprehensive design competency development. However, the purple nodes representing course modules such as Innovative and Comprehensive Design of Interior Space (Incdois), cptsi, and TsCIp lack effective associative connections with the main network, exhibiting a pronounced state of isolation. This fragmentation phenomenon not only undermines the coherence and synergistic effects of the entire curriculum system but may also prevent these course modules from fully realizing their intended educational value and supportive functions in actual teaching processes, thereby compromising the integrity of students’ knowledge structures and hindering the optimization of learning outcomes. Therefore, there is an urgent need to re-examine the curriculum system architecture and establish more cohesive inter-course connection mechanisms. This requires optimizing the temporal arrangement of courses, strengthening the organic integration between isolated course modules and core curriculum clusters, and constructing a truly comprehensive and multi-dimensional curriculum support network. Such a framework would ensure that each course can maximize its effectiveness under the guidance of overall educational objectives, thereby achieving the expected outcomes of comprehensive competency development for students within the OBE framework.

5.4. Comparative Analysis of Course Indicators and Statistical Study of Teaching Effectiveness for 3dmaxV and idm (2022–2023)

The 3dmaxV and the idm courses demonstrate significant complementarity and hierarchical characteristics in terms of their training objectives and instructional content. The 3dmaxV and the idm course exhibited distinctly different developmental trajectories and pedagogical effectiveness characteristics during the 2022–2023 academic year (Figure 6). The former achieved significant advancements through technology-driven reforms, while the latter maintained high standards in theoretical instruction.

From a comparative analysis of curriculum objective setting and content structure, the 3dmaxV course employs a core framework of tool language software and technology design application, emphasizing progressive skill development from fundamental software operations to advanced rendering techniques. Its three curriculum objectives focus, respectively, on the theoretical understanding of software, practical operational competency enhancement, and innovative application expansion, reflecting the dual emphasis on technical skills and innovative capabilities required for talent cultivation in the digital era. In contrast, the idm course establishes a multi-dimensional instructional system based on professional knowledge design and application competition practice, emphasizing interdisciplinary knowledge integration, design thinking cultivation, and practical competency enhancement. These two courses form a complementary framework in terms of goal orientation, with technology-driven and theory-guided approaches, respectively.

Based on observations of statistical trends in pedagogical effectiveness, the 3dmaxV course achieved significant quality improvements during the 2022–2023 academic year. The average score increased from $87.06$ to $88.87$ points, the standard deviation decreased substantially from $6.81$ to $2.70$, and the minimum score surged from $50.00$ to $82.00$ points. This series of data changes indicates that the course achieved breakthrough progress in improving overall instructional quality, narrowing student performance gaps, and assisting academically struggling students. The substantial decrease in standard deviation ($60.4\%$ reduction) and the significant improvement in minimum scores ($64\%$ increase) particularly demonstrate the marked advantages of digital teaching in promoting educational equity and enhancing pedagogical effectiveness. From an analysis of grade distribution structure optimization, the 3dmaxV course demonstrated exceptional improvement with its excellent grade rate increasing from $31.08\%$ to $41.10\%$, representing a $10.02$ percentage point increase. Simultaneously, it completely eliminated moderate and below-average grades, achieving the ideal state where all students attained good or above performance levels. This achievement holds significant demonstrative value for curriculum construction in higher education. Although the idm course showed relatively moderate changes in statistical indicators, its excellent grade rate increased from $21.62\%$ to $30.14\%$, representing an $8.52$ percentage point increase. The proportion of good and above grades consistently maintained a high level, exceeding $90\%$, demonstrating the stability and maturity of this course under traditional teaching models while providing reliable quality assurance for professional foundational education.

Through an in-depth comparative analysis of the pedagogical effectiveness of these two courses, it can be observed that technical courses possess greater potential for improvement and transformation in instructional reform and innovation, while theoretical basis courses emphasize the stability and continuity of teaching quality. The coordinated development of these two distinct course types has constructed a comprehensive talent cultivation system for the interior design program that combines solid theoretical foundations with advanced technical skills, effectively addressing the diversified demands of the contemporary design industry for high-caliber professional talent.

6. Discussion

This study employed network analysis methods to construct CSCN and CTN for an Environmental Design curriculum system based on the OBE framework. The research evaluated 46 courses involving 147 students at Hubei Engineering University, revealing complex support relationships and temporal dependency characteristics among courses, as well as differentiated teaching effectiveness patterns. The research findings indicate that computer basic courses serve as critical hubs in the knowledge transmission network, professional core courses exhibit a distinct core–periphery structure within the network, while certain innovative practice courses demonstrate isolation phenomena. Technology-oriented courses show greater improvement potential in instructional reform compared to theory-based foundational courses. These findings provide preliminary empirical support for curriculum optimization in Environmental Design education at Hubei Engineering University, offering insights into course dependencies and teaching effectiveness that may inform similar institutions, pending broader validation with larger, multi-institutional samples. The study also expands new research perspectives for the application of network analysis in educational assessment, though the methodological framework requires further testing across diverse institutional contexts.

6.1. Theoretical Significance and Practical Value of the Core Status of Computer Basics Courses

The central hub position demonstrated by computer basics courses in specialized Environmental Design courses’ networks reflects the fundamental transformation of design education in the digital era. Network centrality analysis results indicate that graduate requirement indicators such as Dap and PK exhibit the highest degree centrality and betweenness centrality in the CSCN. This finding marks a fundamental transformation in environmental design education from the traditional aesthetic theory and hand-drawing skills-dominated model toward digital competency development [1]. This aligns with the principle that nodes with the highest centrality in networks typically assume critical functions of information transmission and resource integration [36], and it further validates research findings regarding the importance of foundational courses in curriculum structures [30].

This structural change reflects the significantly enhanced dependence on digital tools in modern design practice, particularly within the context of large-scale specialty structure adjustment currently underway in Chinese higher education [2,3]. Computer basic course has become bridges connecting various professional fields, challenging the traditional pedagogical philosophy of theory-first and technology-assisted instruction. The foundational position of technological tools in the curriculum system not only reflects the progressive adaptation of design education to contemporary developments but also emphasizes the crucial role of digital competency in the formation of modern design thinking [4]. CTN analysis further validates this trend. Core courses including 3dmaxV and idm exhibit significant centrality, indicating the logical curriculum sequence and progressive learning structure. This network structure evolution provides crucial theoretical support for constructing future-oriented environmental design education systems.

6.2. Characteristics of the OBE Curriculum System and the Core–Periphery Structure Model

The curriculum support network revealed in this study exhibits distinct core–periphery structural characteristics, which highly aligns with theoretical expectations of structural differentiation in complex systems and conforms to the outcome-based curriculum design principles in OBE philosophy [1,8]. Degree distribution analysis indicates that professional core graduation indicators occupy central positions in the network, with the PK indicator ranking at the network core with a degree value of 20, followed by key indicators such as Dap, Dre, and PI; however, most indicators exhibit relatively low degree centrality, predominantly distributed between 3 and 5. This distribution pattern demonstrates pronounced hierarchical features within the logical framework of the existing curriculum system [10,11]. Betweenness centrality analysis provides additional validation for the centralization of knowledge transmission pathways. Research findings show that key nodes Cp and Professional Development (Pd) indicators function as bridges for inter-module knowledge integration, suggesting that few core course graduation indicators drive knowledge exchange and synthesis in the network [28]. These network structural features strongly correspond to core–periphery structure theory, revealing the hierarchical and specialized nature of knowledge transfer in the curriculum system [9].

Nevertheless, the research concurrently revealed structural flaws within the curriculum system design. Critical courses such as cptsi and TsCIp demonstrate severe isolation, creating substantial deviations from optimal OBE educational models. Additionally, isolated nodes generally indicate system design flaws or resource allocation inefficiencies, consistent with the findings of [35]. Of particular concern, these isolated courses constitute essential components for fostering students’ innovative and integrated practical capabilities, while their inadequate linkage to the primary network poses substantial barriers to achieving holistic competency development goals within the OBE framework. These structural inadequacies illuminate underlying flaws in existing curriculum system design and simultaneously establish concrete pathways for curriculum restructuring and systematic improvement [5]. Conversely, the CTN displays increasingly flattened architectural features. The degree centrality values across courses demonstrate relatively even distribution, peaking at 8, with primary courses predominantly clustered within the 2–5 range. This equilibrated network configuration reveals the systematic constraints imposed by the CTN on teaching advancement and rhythm management, prioritizing integrated scheduling of instructional timelines to guarantee educational consistency and implementation practicality.

6.3. Overstepping of Teaching Effectiveness in Technical Courses and Its Methodological Implications

The transformative development trajectory demonstrated by the 3dmaxV course during the 2022–2023 academic year provides crucial empirical support for pedagogical reform in technical course. The course demonstrated a substantial improvement in academic performance, with the average score increasing from $87.06$ to $88.87$ points, while the standard deviation decreased dramatically from $6.81$ to $2.70$. The excellence rate rose from $31.08\%$ to $41.10\%$. This series of data variations presents a striking contrast to the general patterns of incremental improvement typically observed in traditional educational assessments. According to mastery learning theory, effective instructional design should enable the majority of students to achieve predetermined learning objectives [9]. The present study’s findings, wherein the 3dmaxV course achieved universal attainment of good or excellent performance levels among all participants, provide compelling validation of this theoretical viewpoint. More importantly, the substantial reduction in the standard deviation (a $60.4\%$ decrease) represents a critical statistical characteristic that reveals the unique advantages of digital teaching approaches in promoting educational equity. This finding resonates with multiple intelligence theory, which posits that diverse instructional tools and technological tools can better accommodate individual differences and learning characteristics among students [12,24].

In contrast to the relatively stable instructional outcomes observed in idm courses, technical courses demonstrate significantly greater potential for improvement and transformative change. This disparity reflects the distinct responsive characteristics exhibited by different course types in teaching reform initiatives. Technical courses, characterized by their strong practical orientation and high operability, are more conducive to rapid improvement in teaching effectiveness through pedagogical innovation and technological applications. The interactivity and immersive learning experiences provided by digital tools facilitate students’ mastery of complex design skills, which aligns with international research findings that digital teaching in design education can effectively enhance student engagement and learning outcomes [15].

6.4. Challenges of Isolation and Integration Strategies for Innovative Practice Courses

Some innovative practice courses exhibit isolation within the network structure, and this fragmentation phenomenon partially undermines the holistic effect of outcome-oriented education and competency development emphasized in OBE educational philosophy. The study reveals that courses such as icdisp, Incdois, and TsCIp demonstrate weak associations with the main network. Several courses, including Lab and cptsi, exhibit 0 values and are positioned at the network periphery. Course isolation may result from independent course design or insufficient prerequisite connections, thus hindering knowledge integration and application [20,21]. Traditional course design is typically oriented toward disciplinary knowledge systems, while the OBE philosophy emphasizes the achievement of learning outcomes and comprehensive competency development [1]. This paradigmatic shift needs to be adequately reflected during the construction of curriculum networks.

To address this issue, it is necessary to re-examine and restructure the curriculum system architecture: First, innovative courses should be closely integrated with core course clusters in terms of knowledge points and skill development through the addition of transitional or bridging courses and the establishment of collaborative learning projects. Second, course sequencing should be optimized to ensure that peripheral courses connect with core course content at appropriate temporal nodes, thereby promoting continuity in knowledge accumulation. Finally, cross-curricular projects can be introduced, or course dependency relationships can be adjusted to ensure better integration of peripheral courses into the overall system. These strategies contribute to eliminating fragmentation between courses, incorporating previously isolated nodes into the overall network, and enhancing the global connectivity of the curriculum system.

6.5. Innovative Applications and Research Contributions of Network Analysis Methods in Educational Assessment

This study achieves significant methodological innovation in the educational applications of network analysis methods, providing a novel analytical framework for curriculum system evaluation. Using constructing two distinct analytical models, CSCN and CTN, this study enables comprehensive examination of the structural characteristics of curriculum systems from both logical dependency relationships and temporal arrangement perspectives [6].

The application of the adjacency matrix further enhances the analytical depth, enabling the capture of indirect relationships and multi-step dependency paths between courses [27]. The methodological framework exhibits strong potential for cross-disciplinary application, particularly in practice-oriented fields sharing similar competency-based learning objectives. Engineering disciplines, where technical skills’ progression mirrors our computer basic course centrality patterns, would likely benefit from similar network analysis approaches. Medical education, with its emphasis on clinical skill integration, could adapt our CSCN-CTN dual-network model to analyze relationships between theoretical knowledge and practical competencies. However, purely theoretical disciplines such as philosophy or literature might require methodological modifications to capture the more abstract nature of knowledge dependencies.

Network centrality metrics enable use to carry out an assessment of course importance and connectivity across multiple dimensions in the curriculum structure. The introduction of this quantitative analytical approach provides a more scientific and reliable foundation for educational management decision making. Additionally, this research reveals the mechanisms by which course characteristics influence reform effectiveness through a comparative analysis of teaching effectiveness changes across different course types (technical and theoretical courses), providing important reference for differentiated guidance and precision-targeted policy implementation. Furthermore, this study provides targeted guidance for curriculum reform in similar institutions based on empirical data from local universities in medium-sized cities, demonstrating significant practical value for broader implementation.

6.6. Research Limitations and Future Directions

This study provides both theoretical and practical foundations for curriculum optimization in environmental design education, holding significant theoretical and practical implications. Theoretically, this study enriches curriculum evaluation methodologies by introducing network centrality indicators into curriculum system analysis, providing a novel framework for understanding complex interdependencies and hierarchical structures among courses. Practically, by revealing inter-course dependencies and their impact on teaching effectiveness, the research findings can guide curriculum reform and enhance the coherence and efficiency of curriculum systems. For example, strengthening the position of computer basics courses helps improve students’ learning pathways and knowledge acquisition efficiency. However, this study has certain limitations regarding sample representativeness and the generalizability of research results. The research sample is primarily drawn from a single institution—Hubei Engineering University—located in the central region of China. This lack of cross-regional and cross-institutional comparative validation may partly limit the generalizability and practical application of the research findings. The geographical and economic context of Xiaogan City may have shaped specific curricular demands, and this characteristic may not necessarily apply to other cities. Furthermore, the analysis focuses on specific courses and course graduation indicators without covering broader educational outcomes such as student satisfaction or long-term career development. Although network analysis methods can effectively reveal structural relationships among courses, their explanatory power for deeper issues such as course quality and teaching effectiveness remains to be further enhanced. Future research should expand the sample scope to include universities across diverse regions (e.g., coastal and western China) and conduct multi-institutional comparisons to enhance generalizability. Longitudinal studies tracking student competency development over 3–5 years can validate the long-term impact of curriculum reforms.

7. Conclusions

This study investigates the structural framework of environmental design curriculum systems based on outcome-based education (OBE) concepts and their teaching effectiveness evaluation. By constructing the curriculum support and contribution network (CSCN) and course time network (CTN) and employing key indicators from network analysis methods, this study conducted systematic analysis of 46 courses and 6 course graduation indicators in the environmental design program at Hubei Engineering University, yielding the following main conclusions:

• The computer basics course plays a crucial hub role in the knowledge transmission network. Network centrality analysis reveals that fundamental computer science courses such as Design Application (Dap) and Professional Knowledge (PK) occupy central positions in the CSCN, exhibiting the highest degree and betweenness centrality, serving as bridges between different disciplinary domains and providing essential knowledge foundations for subsequent specialized courses.
• The OBE curriculum support network exhibits a distinct core–periphery structure. Within the curriculum system, core professional courses occupy critical hub positions, with the PK ranking first with a degree value of 20, establishing it as the most highly connected core node in the network. However, certain courses such as Innovative and Comprehensive Design of Interior Space (Incdois) and Comprehensive Practical Training on Spatial Innovation (cptsi) exhibit isolated states, presenting issues of inadequate connectivity with the main network.
• Teaching effectiveness of technical courses demonstrates significant improvement. The 3DSAMX+VRAY Indoor Digital Environment Design (3dmaxV) course achieved remarkable progress during the 2022–2023 academic year, with average scores increasing from 87.06 to 88.87 points, standard deviation dramatically decreasing from 6.81 to 2.70, and excellence rate rising from $31.08\%$ to $41.10\%$. These improvements fully demonstrate the significant advantages of digital teaching instructional tools.
• Structural differences exist between the CSCN and CTN. The CTN exhibits a relatively flat structural profile, highlighting the temporal sequencing and scheduling of course offerings, whereas the CSCN emphasizes inter-course logical dependencies. Together, these two networks form a comprehensive analytical framework for examining curriculum structure.

This study holds significant theoretical and practical implications for structural optimization and curriculum reform in environmental design education at local universities in medium-sized cities. Theoretically, the research findings enrich the application of network analysis in curriculum evaluation and propose a novel framework for understanding course dependencies and temporal dynamics. Practically, the study provides reference criteria for optimizing curriculum design and rationally allocating teaching resources, contributing to the enhancement of environmental design education quality and student development outcomes in similar universities across medium-sized cities in China. However, as the research sample is drawn from a single institution located in the central region of China and lacks cross-regional and cross-institutional validation, the study is limited in both sample representativeness and results generalizability. Future research should expand the sample scope to include universities across diverse regions (e.g., coastal and western China) and conduct multi-institutional comparisons to enhance generalizability. Longitudinal studies tracking student competency development over 3–5 years can validate the long-term impact of curriculum reforms. Additionally, integrating qualitative methods, such as interviews with students and faculty, alongside network analysis can deepen our understanding of course quality and teaching effectiveness, improving the explanatory power of the proposed framework.