Evaluation of Instructor Capability: Perspective from Building Information Modeling Competition Students in Mainland China

Abstract

Building information modelling (BIM) technology, which has experienced rapid development, has become the focus of digital education learning in architecture, engineering, and construction (AEC) related disciplines. However, BIM education is still confronted with the disconnection between theoretical education and engineering practice. In mainland China, BIM competitions are an important digital platform for higher education practice teaching. As the organizer and leader of student participants, BIM instructors, especially their BIM capability, have an important impact on digital education. Previous research on BIM capability did not involve the field of BIM education, and the existing BIM capability framework is not entirely applicable to evaluating the BIM capability of instructors. Semi-structured interviews based on grounded theory (GT) and structural equation modeling (SEM) were used to construct a five-dimensional model containing 23 capability indicators. The research findings highlighted the multi-dimensional nature of BIM capability for instructors and indicated that personnel capability was the most important for instructors, while process capability was considered the most dispensable. This is significantly different from the emphasis on technical and process capability in BIM capability research in the traditional AEC industry. It was also found that students from different levels of universities and different educational backgrounds had different demands for the BIM capability of instructors. The results of this study will help universities select excellent instructors, improve the quality of BIM education in universities, and cultivate more outstanding BIM talents for the development of BIM in the AEC industry, thereby promoting the digital practice of BIM education in universities.

1. Introduction

The interdependence between different stakeholders is becoming increasingly complex, and construction projects are becoming more difficult to manage [1,2,3]. To cope with the increasingly complex situation of projects, information and communication technology (ICT) has been developing at a very fast pace, and building information modeling (BIM) has emerged [4]. BIM is a new method of approaching the design, construction, and maintenance of buildings. It is defined as “a set of interacting policies, processes, and technologies that generate a method for managing basic architectural design and project data in digital format throughout the entire lifecycle of a building” [4]. BIM is also regarded as a digitalized technology under a collaborative working platform [5].

As a revolutionary technology and process, BIM has received enormous attention from both academia and industry [6]. And BIM has been regarded by many people as a significant opportunity in the architecture, engineering and construction (AEC) industry [7]. BIM technology has enormous advantages in the modern construction industry. BIM not only brings technical benefits to the development process, but delivers an innovative and integrated working platform to improve productivity and sustainability throughout the project life cycle [8]. The virtual construction capabilities of BIM technology provide project teams with a powerful tool to assess the feasibility and constructability of buildings during the pre-construction phase. BIM enables project owners to review designs and provide feedback through the visualization of three-dimensional (3D) building information models before facility construction. By leveraging this technology, uncertainties are eliminated, designs are optimized, resource waste is minimized, and passive design strategies are effectively implemented. This comprehensive approach ensures seamless project progression, significantly elevates building quality, and promotes sustainability throughout the construction industry, all while adhering to the rigorous standards expected by the scientific community [9].

BIM promotes project constructability, improves productivity, and ensures time and money savings. A study by Stanford University’s Center for Integrated Facilities Engineering (CIFE) showed that using BIM can reduce the project budget estimation time by 80%, total project duration by 7%, contract value by 10%, and project total cost estimation with a 3% profit margin [10]. Due to the practicality and economic value of BIM, it has been widely recognized in the AEC industry [7]. Companies and educational institutions related to the AEC industry have widely adopted BIM technology to meet market demands [11,12,13]. China is a huge AEC market, accounting for 47.9% of the construction industry in the Asia Pacific region. It is expected that the construction industry will continue to grow at an average rate of 12.6% from 2013 to 2018 [14]. The Chinese AEC market is also constantly increasing its demand for BIM applications [15,16].

As more and more companies use BIM, the demand for BIM talents in the market is steadily increasing significantly [6,17]. However, improving the utilization rate of medium BIM still faces numerous difficulties. Research has shown that the lack of knowledgeable and experienced professionals in the AEC industry is a key obstacle to adopting BIM in projects [18,19,20]. In the past few years, countries such as the United States, the United Kingdom, Singapore, and Australia have all carried out BIM standardization and policy-making activities, thereby increasing the adoption rate of BIM [21]. However, industry and academia recognize that BIM education in university courses is an important requirement to meet industry and educational needs [22]. Based on the expectations of the current AEC industry and strong government efforts, many educational institutions are researching the establishment of BIM courses in higher education systems [23,24]. Globally active BIM educators and researchers are also striving to provide BIM education frameworks and design BIM courses [25]. After several years of development, the widespread presence of BIM in today’s university curriculum has led to significant progress in BIM education [25,26]. This is considered a win-win situation, which not only reduces the cost of BIM adoption but also significantly improves the employment opportunities for AEC graduates [27,28,29].

However, BIM education still faces the problem of a shortage of BIM learning resources. And BIM education in universities mostly focuses on the teaching of basic BIM skills and theories. There is still a lack of interaction between theoretical education and engineering practice in BIM learning [30,31]. The practical BIM capabilities of students cannot meet the requirements of the industry. Therefore, the focus of BIM teaching has shifted from technical aspects to emphasizing the implementation process and value proposition of BIM [32]. Project based learning (PBL) is one of the effective methods to improve students’ practical abilities [33]. This method actually refers to learning through practice. Borkowski also emphasized the importance of experiential learning in BIM education. Solving practical problems in projects can more effectively enhance students’ BIM capabilities [34]. Team members work together to solve real-world challenges and acquire knowledge and skills [35].

BIM competition is one of the most common PBL activities, which allows participating teams to solve BIM challenges designed by the organizers in a certain period of time, such as designing a building (BIM Valladolid), designing energy efficient construction process, or urbanizing an urban space [36]. There are various types of BIM competitions in China, including the National BIM Graduation Design Innovation Competition and BIM Technology Application Competition. These competitions have attracted active participation from numerous university students, such as Yanshan University. Participants usually need to form a team, use Revit 2023to complete designated architectural design tasks, and submit corresponding design proposals and technical reports with the help of the instructors. Ao et al. [37] thought that the performance of students participating in competitions will be affected by the instructors. In this team, as the organizer and leader of the participating students, instructors play a crucial role. Their role is not only to provide technical support and knowledge transfer, but also to guide students’ thinking patterns, stimulate innovative thinking, cultivate teamwork skills, and enhance project management capabilities. Hence, the selection of instructors has a significant impact on the BIM practice and competition results of participating teams. However, there is no research on the selection criteria for excellent instructors. Due to the diversity of team roles played by instructors, this article quantifies the important role played by mentors as relevant BIM capability indicators based on BIM capability theory.

There are not many studies that provide a precise definition of BIM capability, and a small number of studies that define BIM capability are based on Succar’s concept of BIM capability: “BIM capability is the basic capability required to perform tasks or provide BIM services and products. BIM capability is a direct reflection of BIM requirements and deliverables” [38]. On the basis of this definition, Munianday et al. [39] provided a definition of individual BIM capability: “A person’s personal BIM capability depends on their personal qualities, professional knowledge, and the technical ability required to integrate BIM activities or generate BIM related outputs”. Yilmaz et al. [40] believed that BIM capability was the characteristic of the ability to achieve the defined BIM results and BIM attribute outcomes. Many scholars have studied and developed the theory of BIM capability, proposing various BIM capability frameworks and evaluation indicators for designers [41], contractors [42], and owners [43]. In construction projects, managers with excellent BIM capabilities can effectively improve project productivity and performance [44,45]. In BIM competitions, participants usually need to use BIM technology for modeling, analysis, and optimization tasks to solve practical engineering problems or propose innovative solutions. From an academic perspective, BIM competitions are actually a typical BIM activity. However, the BIM capability of the BIM competition instructors is not directly equivalent to the BIM capability of any party in the construction project. The capability of instructors in BIM competitions are more comprehensive, which can be seen as a combination of partial BIM capabilities for various roles such as owners, project managers, and supervisors in actual engineering projects. This study compared their capability from multiple dimensions to establish preliminary indicators for evaluating the capabilities of instructors in the BIM competition scenario.

To achieve the research objectives, the structure of this paper is outlined as follows. The existing literature on BIM capability was analyzed and analogized to the role of instructors in digital teaching practice, obtaining a preliminary list (V1) of BIM capability indicators. Subsequently, semi-structured interviews based on GT were employed to validate the content and completeness of the BIM capability indicators (V1), leading to the refinement of the instructor BIM capability indicators (V2). Afterward, utilizing the relevant data collected from the questionnaire survey, structural equation modeling (SEM) was applied to verify the BIM capability indicators (V2) and determine the weight coefficients of various dimensions and secondary capability indicators. Ultimately, five primary dimensions and 23 key BIM capability indicators for instructors were identified. The theoretical contributions and practical implications are presented in the discussion section. This study aimed to construct an evaluation system for instructors’ BIM capability, providing guidance for enhancing their BIM capability and thus promoting digital education practices in AEC-related disciplines in universities.

Various BIM capability and maturity models have been developed to assist architecture, engineering, construction, and facility management (AEC/FM) organizations in measuring their performance in BIM utilization. However, the applicability and focus of these models are different, and there is no universally accepted or widely used model [46]. The existing BIM capability model is not suitable for BIM education scenarios. In addition, there is currently no research on the BIM capability model of instructors. Therefore, it is necessary to evaluate instructor BIM capabilities. This article constructed a BIM capability evaluation system for competition instructors from the perspective of participating students using a questionnaire and SEM, filling the gap in the field of BIM education. The research results indicated that the personnel capability of instructors is the most important, followed by their support capability, while the importance of process capability is the lowest. This is significantly different from the emphasis on technical and process capabilities in BIM capability research of the traditional AEC industry. This also proves that the existing BIM capability framework is not suitable for screening excellent instructors. This research result will help universities select more excellent competition instructors, improve the quality of BIM education in universities, and cultivate more outstanding BIM talents for the development of BIM in the AEC industry. What’s more, it has laid a theoretical foundation for research on BIM capabilities in the field of BIM education, thereby promoting the digital practice of BIM education in universities. The research framework of this article is shown in Figure 1.

2. Literature Review

BIM is a microcosm of policies, processes, and technologies that will enable the construction industry to generate, manage, and store project data in digital format for lifecycle management [47]. BIM can effectively solve communication bottlenecks and lack of collaboration in the traditional construction industry, thereby improving the performance of the construction industry [48]. The communication gap and low utilization of lifecycle data are the core issues of low process efficiency related to the industry, and BIM is expected to solve these problems [4]. Although BIM has significant advantages over traditional building technologies, the lack of experience in organizing BIM has hindered its widespread use [49]. Therefore, it is necessary for organizations to evaluate and understand their current BIM capability, improving project performance [50]. In recent years, the theory of BIM capability has emerged, but there are not many studies that provide a precise definition of BIM capability. The definition of BIM capability in most of the literature originates from Succar’s concept of BIM capability: “BIM capability is the basic capability required to perform tasks or provide BIM services and products. BIM capability is a direct reflection of BIM requirements and deliverables” [38]. On this basis, scholars have defined the BIM capability of individuals and various organizations. Munianday et al. [39] argued that individual BIM capability depended on their personal qualities, professional knowledge, and technical abilities required to integrate BIM activities or generate BIM related outputs. Ahuja et al. [42] believed that organizational BIM capability referred to the ability of an organization to know how to use technical expertise and manage BIM implementation, and to transform BIM technical knowledge into expected project outcomes. To meet the different BIM evaluation objectives of BIM users, various BIM capability and maturity models have been developed, such as owners [43,51,52], consultant companies [53], assessors [54] and contractors [42]. However, there have been few studies on the capability assessment of instructors in the education field. Next, based on the role played by instructors in competitions, we analyzed the previous literature and preliminarily established the BIM capability indicator system V1.

An emerging core competitive area for pre-qualification and selection was BIM maturity of organizations [55]. This was also why some BIM standards and national implementation agendas required evaluating the BIM capability of project teams before selection. For example, in the UK, the BIM execution plan must include a Supply Chain Capability Summary (SCCS), which indicated the BIM capability of all companies in the supply chain of major suppliers and contractors. Scotland had also proposed a similar framework for evaluating an organization’s BIM capability before project selection or commencement [56]. These emerging standards, frameworks, and tools laid the foundation for determining BIM capability pre-qualification and standards [4,51,57].

Succar et al. [38] proposed a BIM capability framework of criteria, namely, technology, process, and policy. The technology dimension of this framework refers to physical artifacts, including software, hardware and network capability, while process dimension encompasses attributes such as BIM resources, activities and workflows. The policy dimension covers procedures related to contracts, benchmarks and guidance documents that support BIM implementation. In the BIM competition scenario, it is evident that the policy dimensions and subordinate indicators in the BIM capability framework are not applicable to evaluating the abilities of competition instructors. Dib et al. [58] classified BIM capability as planning, management, process, team structure, hardware, process definition and information management by Principal Component Factor Analysis (PCFA). On the other hand, Mahamadu et al. [57] classified BIM capability attributes for pre-qualification and selection activities, namely, competence, resources, culture, attitude, and cost. Obviously, attitude and cost indicators are not applicable as capability evaluation indicators for instructors.

To assess the BIM capability of design, construction and facilities management processes in the construction industry, Yilmaz et al. [40] developed the BIM Capability Assessment Reference Model (BIM-CAREM) and demonstrated its usability. This model divided BIM capability into five categories: technical, process, organizational, human factors, and BIM standards, and decomposed them into 48 capability assessment indicators. BIM QuickScan was a tool for providing a way for companies to quickly assess the BIM level of their construction projects in the Netherlands. This tool contained an online Survey with 44 questions that divided BIM capability into four categories: organization and management, mentality and culture, information structure, and information flow. Every category was composed of weighted key performance indicators (KPI) [59]. Mahamadu et al. [60] divided BIM capability into 11 criteria, which were divided into four main dimensions: capability, capability and resources, culture and attitude, and cost. These standards included qualifications, specific BIM modeling capability, organizational experience, administrative and strategic capability, technical (physical) resources, employee experience, suggested methods, organizational structure, technical readiness, organizational reputation, and costs. They emphasized the importance of BIM software and software availability for modeling.

In the framework of Messner [61], strategic and personnel capability were considered the most important criteria in capability assessment. Giel and Issa [51] argued that operational capability was the most important BIM capability in owner organizations, followed by strategic capability. On the other hand, Van Berlo et al. [59] argued that mindset and BIM cultural attributes were the most important, followed by information processing abilities. Although these frameworks are proposed for different assessment environments, they highlight the significant differences and importance of BIM capability assessment standards. This also emphasized the necessity and value of this study. Yilmaz et al. [40] concluded that BIM standards and human factors were unnecessary evaluation factors for evaluating BIM capability. Usually, completing the modeling tasks of actual projects was complex and time-consuming. Siebelink et al. [49] explained the importance of goals and visions, which provided employees with motivation and belief in promotion and salary increases. Juul and Jensen [62] emphasized the important role of teacher relationship capability in the educational environment. In the context of BIM competitions, the relationship capability of instructors is more reflected in their teamwork and communication capability with participating students.

Although these frameworks were proposed for different assessment environments, they highlighted significant differences in BIM capability assessment standards and their importance. However, there was no research on the selection criteria for instructors. Therefore, this study aimed to construct a framework for evaluating the BIM capability indicators of instructors from the perspective of participating students and identify the most important capability of instructors. This also explains the necessity and practical value of constructing a framework for evaluating the capabilities of instructors. The BIM capability indicators V1 of instructors obtained from literature review are shown in Appendix A.

3. Research Methodology

The research methodology is schematically outlined in Figure 2. It started with the identification of 19 instructor BIM capability indicators and their attributes through literature review. It is worth noting that some of the BIM capability frameworks in Appendix A were designed to evaluate organizations such as owners and contractors, and were considered to expand the scope of indicators. The decision to merge these models was based on the understanding that certain aspects of these models are relevant and applicable to instructor BIM capability. Then, semi-structured interviews based on GT were conducted on the award-winning participating students, with the aim of collecting their opinions on the definition and modification of BIM capability indicators for instructors, examining the content validity and saturation of the indicators. Using the identified indicators, an online survey questionnaire was designed and distributed to the participating students of the 10th National BIM Graduation Design Competition in China. To assess the validity of the factor structure, confirmatory factor analysis (CFA) was utilized. Finally, path analysis utilizing structural equation modeling (SEM) was conducted to establish the weights assigned to every dimension and indicator.

3.1. Instructor BIM Capability Indicators V2

Through literature review, the BIM capability indicators V1 for instructors was obtained, as shown in Appendix A. Semi-structured interviews were considered as other qualitative data collection techniques because interviews are the fundamental source of evidence [63] and an important mechanism for establishing structure in the data collection process [64]. The results of the interview helped to obtain in-depth information on the research content [65]. In order to test the validity and saturation of the capability indicators V1, semi-structured interviews based on GT were conducted until there were no different opinions. GT is a general methodology for developing theory based on empirical data that are systematically gathered and analyzed [66]. Content analysis is a research tool used to determine the existence of concepts, which includes concept analysis and concept mapping [67]. The target audience was award-winning students from universities at different levels. After every interview, we provided a summary to the interviewees to help validate the results. Interviews were terminated after the eighth interview as a result of saturation, as suggested by Guest et al. [68]. Finally, 12 online semi-structured interviews were conducted to be more rigorous.

The information of the 12 respondents is shown in

Table 1. Background information of 12 respondents.

Code	Educational Background	Major	University Level	Award Situation
R1	Doctoral Candidate	Civil Engineering	Project 985	BIM Competition Grand Prize
R2	Postgraduate	Civil Engineering	Project 985	First Prize in BIM Competition
R3	Undergraduate Student	Civil Engineering	Project 985	First Prize in BIM Competition
R4	Postgraduate	Civil Engineering	Project 211	First Prize in BIM Competition
R5	Undergraduate Student	Architecture	Project 211	BIM Competition Grand Prize
R6	Undergraduate Student	Architecture	Project 211	First Prize in BIM Competition
R7	Undergraduate Student	Civil Engineering	Ordinary undergraduate	BIM Competition Grand Prize
R8	Undergraduate Student	Civil Engineering	Ordinary undergraduate	BIM Competition Grand Prize
R9	Undergraduate Student	Architecture	Ordinary undergraduate	First Prize in BIM Competition
R10	Junior College Student	Civil Engineering	Junior college	BIM Competition Grand Prize
R11	Junior College Student	Civil Engineering	Junior college	First Prize in BIM Competition
R12	Junior College Student	Civil Engineering	Junior college	First Prize in BIM Competition

. Although the number of interviews was limited, they covered award-winning students from different levels of universities, and they all provided valuable feedback on the BIM capability indicators of instructors [69].

To promote outstanding research, the Chinese government has been providing significant funding to support a small group of selected universities through three national research projects (Project 211, Project 985, and Double First Class). The “Double First Class” initiative was launched by the Chinese government in 2017, aiming to establish an outstanding higher education system encompassing “First-Class” universities and “First-Class” disciplines, following the official conclusion of Project 985 and Project 211. However, the concepts of Project 985 and Project 211 still remain in use, not only in academia but also in the industry, where graduation from universities under these projects was often considered a threshold for recruitment. These universities had higher financial support and more academic achievements compared to ordinary colleges, but it was more difficult for students to obtain admission qualifications [70].

Based on the opinions of award-winning students during the interview, the definition of the BIM capability indicators for instructors was determined, and four new capability indicators were added: psychological counseling capability, civil engineering professional knowledge, financial support, and technical assistance. Ultimately, the instructor BIM capability indicators V2 was formed, as shown in Appendix B.

Munianday et al. [39] explained the importance of financial support provided by organizational management in BIM organizational activities, which was beneficial for the sustainable development of organizational operations. In the interview, participating students from Project 985 universities emphasized the importance of instructors applying for funding support. And this was widely recognized by other interviewees. They believed that having financial support allowed them to participate in professional training or seminars, thereby mastering more BIM skills and knowledge. This helped to enhance the competitiveness of students in competitions, giving them an advantage in competition with other teams. In addition, it also helped students reduce their financial burden, allowing them to focus more on the competition itself and improve the effectiveness of the competition. Respondents from vocational colleges explained the necessity of having a solid knowledge of civil engineering as a supervising teacher. When interviewed with other award-winning students, they also explained that they would seek advice from their supervising teachers on civil engineering knowledge during the competition process. Therefore, knowledge of civil engineering was also an effective indicator for evaluating the capability of instructors.

Maintaining close and trustworthy connections with other organizations with rich BIM related knowledge can provide more professional knowledge in BIM applications [71]. Respondents from Project 211 universities mentioned the significance of inviting technical assistance for competition results. They believed that inviting technical assistance played an important role in enhancing team technical strength, broadening student perspectives, enhancing team collaboration, and improving competition results. Respondents from other universities also stated that inviting technical assistance from instructors would be helpful for the competition performance. On the other hand, due to the tight competition time and heavy tasks, participating students face great pressure. Respondents from regular undergraduate universities hoped that their mentors could provide psychological counseling to alleviate stress. When conducting follow-up visits to other interviewees, they stated that they also faced similar issues. Therefore, psychological counseling capability was also an important capability that instructors possess.

3.2. Data Collection and Screening

3.2.1. Design of Questionnaire

On the basis of preliminary research and the tracking survey of the 10th National BIM Graduation Design Innovation Competition, the questionnaire design of this study mainly included two parts: the first part comprised basic information of participating students, including university level, education level, major, competition track, years of exposure to BIM, and the number of times participating in BIM related competitions; the second part consisted of the evaluation table for the importance of instructor BIM capability indicators. The survey respondents were asked to use a five-point Likert scale to rate the importance of these 23 identified indicators, ranging from 1 (disagree), 2 (slightly disagree), 3 (neutral), 4 (slightly agree) to 5 (strongly agree). The 5-point Likert scale is very popular because it can provide clear results [72]. The 5-point Likert scale is more popular than the 7-point Likert scale because it improves response rates and quality, while reducing the level of frustration among respondents [73]. It is more simple for the interviewer to read out the complete list of a scale [74]. Nevertheless, at the end of the survey questionnaire, the respondents were given space to describe and evaluate any other factors related to the instructors’ capability.

3.2.2. Questionnaire Distribution

The questionnaire data were mainly collected online. The target audience included participating students from different universities, educational backgrounds, majors, and tracks. Due to the lack of a sampling framework, the samples used in this study were non probability samples [75], which were used to create representative samples. Respondents might choose to participate in the study based on their interest in participating in the questionnaire [76]. Therefore, the snowball sampling method was used to determine the overall size of the sample, which had the advantage of collecting and sharing data and responses through recommendations or social networks [77]. In the end, a total of 628 questionnaires were distributed online.

3.2.3. Collection and Screening

The importance of an evaluation of BIM capability indicators for instructors by participating students in BIM competitions was collected through an online questionnaire. After distributing 628 questionnaires, a total of 358 responses were received. The response rate was 57%, which was considered acceptable [78]. Before conducting data analysis, the raw data were screened to identify potential issues, including missing data, low data reliability, and collinearity. The screening criteria were as follows: (1) If the questionnaire was not fully completed and there were omissions, the missing data were not used [79]; (2) Due to the short response time, questionnaires completed within less than 120 seconds (with at least 3 seconds allocated to each question) were excluded. The implementation of this standard was to ensure that participants had sufficient time to provide thoughtful and meaningful responses to the survey form; (3) When participants consistently followed specific patterns in their responses, it might introduce bias and affect the accuracy of the study. These questionnaires were considered invalid. Finally, a total of 118 invalid questionnaires were screened out from 358 questionnaires, resulting in 240 valid questionnaires. To ensure sufficient sample size, it is generally recommended that the ratio of sample size to scale items in the survey form be greater than 5:1 [80,81]. In this study, the sample size met this requirement. The profile of the 240 respondents is shown in Figure 3.

3.3. Data Analysis and Conclusions

Data analysis consisted of conducting multivariate data analysis on the collected survey questionnaire data to determine the key BIM capability of instructors. This article developed a BIM capability framework for instructors through two program steps of multivariate analysis: confirmatory factor analysis (CFA) and SEM. Finally, path analysis was used to calculate the weights of each level of instructor BIM capability indicators. A similar process was also used to develop knowledge management maturity [82] and construction project success [83]. The following sections provide a more detailed explanation of each analysis.

The Kaiser Meyer Olkin (KMO) test and Bartlett sphericity test were first used to test the suitability of instructor BIM capability scale data for factor analysis. The KMO test is used to evaluate the sampling adequacy of factor analysis. KMO values range from 0 to 1, with higher values indicating greater suitability for factor analysis. The data obtained had a KMO of 0.938, indicating that these data are very suitable for factor analysis. The Bartlett sphericity test is used to evaluate whether the correlation matrix is significant. The significance level of Bartlett’s test is usually set to 0.05 in accordance with predetermined criteria. The obtained significance value was 0.000, significantly lower than 0.05. These results indicated a significant correlation between variables, and the dataset supported the use of factor analysis.

In this study, the main factors were measured in the form of scales, so testing the data quality of the measurement results was an important prerequisite for ensuring the significance of subsequent analysis. To ensure the internal consistency within the attributes grouped under factors and reliability of the data, the most commonly used Cronbach’s alpha reliability analysis was performed using SPSS 27.0 software [84]. The internal consistency was explained by the reliability coefficient, which is based on the average correlation between the attributes and the number of total attributes in the factor. The value of Cronbach’s alpha varies from 0 to 1. A higher value of Cronbach’s alpha indicates greater internal consistency and vice versa. The whole Cronbach’s alpha coefficient for the questionnaire data was 0.941, which was greater than the required minimum value of 0.90 [80]. Moreover, the indicators within every dimension exhibited a value exceeding 0.6 [85], indicating strong internal consistency within their respective dimensions.

Table 2. Reliability test.

Dimension	Code	Cronbach α	Indicators of Instructor BIM Capability
Technical capability	JS1	0.890	BIM theoretical knowledge
	JS2		Software application capability
	JS3		Capability of solving practical problems
	JS4		Innovation capability
	JS5		Professional knowledge in civil engineering
Organization capability	ZL1	0.871	Teamwork capability
	ZL2		Communication capability
	ZL3		Coordination capability
Personnel capability	RY1	0.868	Encouragement and support
	RY2		Vision and objectives
	RY3		Psychological counseling capability
Process capability	LC1	0.847	Time management capability
	LC2		Leadership capability
	LC3		Project management capability
Support capability	JZ1	0.963	Information collection and integration capability
	JZ2		Training and education
	JZ3		Acquisition of competition venues
	JZ4		Hardware configuration
	JZ5		Network environment
	JZ6		Adaptability
	JZ7		Obtaining software usage authorization
	JZ8		Financial support
	JZ9		Technical assistance

summarizes the Cronbach alpha coefficients.

3.3.1. Confirmatory Factor Analysis (CFA)

To promote the theoretical understanding of the multidimensionality of instructor BIM capability, strict confirmatory factor analysis was conducted using AMOS 28 to construct the CFA model, as shown in Figure 4. The BIM capability measurement model for instructors is an endogenous latent variable, while technical capability, organizational capability, personnel capability, process capability, support capability are exogenous latent variables. Different goodness-of-fitness (GOF) measures different aspects of a model’s capability to represent the data. As shown in

Table 3. GOF Measures.

Number	GOF Measure	Recommended Level of GOF Measures	Actual Measurement Results
1	Chi-square/degree of freedom (χ2/df)	1 to 3	1.156
2	GFI	0 (no fit) to 1 (perfect fit)	0.918
3	AGFI	0 (no fit) to 1 (perfect fit)	0.897
4	CFI	0 (no fit) to 1 (perfect fit)	0.992
5	RMSEA	<0.05 (very good) to 0.1 (threshold)	0.026
6	IFI	0 (no fit) to 1 (perfect fit)	0.992
7	TLI	0 (no fit) to 1 (perfect fit)	0.990

Note: GFI = Goodness of fit index; AGFI (Adjusted Goodness of Fit Index) IFI = incremental fit index; TLI = Tucker–Lewis index; CFI = comparative fit index; RMSEA = RMS error of approximation.

, χ²/df = 1.156 < 3, GFI = 0.918 > 0.9, AGFI = 0.897 > 0.8, CFI = 0.992 > 0.9, IFI = 0.992 > 0.9, TLI = 0.990 > 0.9, and RMSEA = 0.026 < 0.05 [86,87]. The GOF test results offered good support for the fit of the model.

To ensure the accuracy of the testing structure model, it was crucial to establish an effective measurement model and conduct a comprehensive evaluation of its validity and reliability. Under the premise that the CFA model of the instructors’ BIM capability scale had good fit, the Average Variance Extracted (AVE) and Composite Reliability (CR) of every dimension of the scale needed to be further examined. The standardized factor loadings of every indicator measurement on its corresponding dimension were calculated through the established CFA model. Subsequently, the AVE and CR values of every dimension were calculated accordingly. As shown in

Table 4. Convergent validity analysis.

Path Relationship			Estimate	AVE	CR
JS1	<---	Technical capability	0.813	0.621	0.891
JS2	<---	Technical capability	0.775
JS3	<---	Technical capability	0.820
JS4	<---	Technical capability	0.721
JS5	<---	Technical capability	0.806
ZL1	<---	Organization capability	0.826	0.694	0.872
ZL2	<---	Organization capability	0.782
ZL3	<---	Organization capability	0.888
RY1	<---	Personnel capability	0.832	0.692	0.871
RY2	<---	Personnel capability	0.870
RY3	<---	Personnel capability	0.792
LC1	<---	Process capability	0.736	0.651	0.848
LC2	<---	Process capability	0.847
LC3	<---	Process capability	0.833
JZ1	<---	Support capability	0.846	0.745	0.963
JZ2	<---	Support capability	0.859
JZ3	<---	Support capability	0.871
JZ4	<---	Support capability	0.874
JZ5	<---	Support capability	0.863
JZ6	<---	Support capability	0.870
JZ7	<---	Support capability	0.869
JZ8	<---	Support capability	0.874
JZ9	<---	Support capability	0.843

, the AVE values for all five dimensions exceeded the recommended threshold of 0.5, indicating satisfactory convergent validity of the indicators and constructs. Moreover, the Composite Reliability (CR) values for all dimensions were higher than 0.7, indicating excellent internal consistency and reliability [80].

Discriminant validity refers to the degree to which a given structure differs from other structures [88]. An adequate discriminant validity means the square root of the AVE of each construct should be higher than the inter-construct correlation, and a measurement item’s loading on its respective construct should exceed the cross loadings [89]. From the analysis results in

Table 5. Discriminative validity analysis.

	Technical Capability	Organization Capability	Personnel Capability	Process Capability	Support Capability
Technical capability	0.621
Organization capability	0.401	0.694
Personnel capability	0.494	0.592	0.692
Process capability	0.307	0.260	0.487	0.651
Support capability	0.603	0.439	0.532	0.429	0.745
The square root of AVE value	0.788	0.832	0.833	0.807	0.863

, it was concluded that the standardized correlation coefficients between each dimension were less than the square root of the AVE values corresponding to the dimension, indicating good discriminant validity between each dimension.

3.3.2. Structural Equation Modeling (SEM)

SEM is a multivariate statistical technique that includes two types of models: measurement models (confirmatory factor analysis) and structural models (regression or path analysis) [90]. It allows researchers to explore complex relationships between variables by analyzing covariance matrices. AMOS 28 software was used for variable setting and analysis. After conducting CFA on the BIM capability measurement model of the instructors mentioned above, the next step was to conduct path analysis and ultimately determine the weight coefficients of each potential variable and observation variable in the model. Figure 5 shows the SEM model of instructor BIM capability.

Similarly, Amos 28 was used to test the model fit and path relationship hypothesis of the SEM model. The test results are shown in

Table 6. SEM model fitness test.

Number	GOF Measure	Recommended Level of GOF Measure	Actual Measurement Results
1	Chi-square/degree of freedom (χ2/df)	1 to 3	1.235
2	GFI	0 (no fit) to 1 (perfect fit)	0.911
3	AGFI	0 (no fit) to 1 (perfect fit)	0.891
4	CFI	0 (no fit) to 1 (perfect fit)	0.987
5	RMSEA	<0.05 (very good) to 0.1 (threshold)	0.031
6	IFI	0 (no fit) to 1 (perfect fit)	0.987
7	TLI	0 (no fit) to 1 (perfect fit)	0.986

and

Table 7. SEM model path coefficients and significance tests.

Code	Path	Standardized Path Coefficient	P Label
H1	Instructor BIM capability→ Technical capability	0.69	*** (p < 0.01)
H2	Instructor BIM capability→ Organization capability	0.63	*** (p < 0.01)
H3	Instructor BIM capability→ Personnel capability	0.77	*** (p < 0.01)
H4	Instructor BIM capability→ Process capability	0.54	*** (p < 0.01)
H5	Instructor BIM capability→ Support capability	0.75	*** (p < 0.01)

. It can be observed that the model has good adaptability and significant path relationships.

Path coefficient is a quantitative way to represent the relationship between latent variables and the relationship between latent variables and measured variables. The larger the value of the path coefficient, the deeper the impact. Based on standardized path coefficients, the path coefficients for the five dimensions of instructor BIM capability (technical capability, organizational capability, personnel capability, process capability, and support capability) were 0.69, 0.63, 0.77, 0.54 and 0.75, respectively. These coefficients represent the strength and direction of the relationship between variables. Table 7 shows the modified model path coefficients and significance tests.

Finally, the standardized path coefficients obtained from the structural equation model were used to determine the weight coefficients of potential and observed variables in the model, as shown in Table 7. The weighting coefficients for every indicator level are shown in

Table 8. Dimensions and indicator weights of instructor BIM capability.

Dimension	Dimension Weight	Indicator Code	Indicator Weight
Technical capability		JS1	0.206
		JS2	0.198
	0.204	JS3	0.208
		JS4	0.183
		JS5	0.206
Organization capability		ZL1	0.336
	0.186	ZL2	0.316
		ZL3	0.348
Personnel capability		RY1	0.338
	0.228	RY2	0.345
		RY3	0.317
Process capability		LC1	0.306
	0.160	LC2	0.347
		LC3	0.347
Support capability		JZ1	0.110
		JZ2	0.111
		JZ3	0.112
		JZ4	0.112
	0.222	JZ5	0.111
		JZ6	0.112
		JZ7	0.112
		JZ8	0.112
		JZ9	0.108

.

4. Discussion

This article constructed a BIM capability evaluation framework for competition instructors from the perspective of participating students. This framework included five dimensions, namely technical capability, organization capability, personnel capability, process capability, and support capability, with a total of 23 capability indicators, further proving the multidimensionality of the BIM capability of instructors and the importance of instructors in BIM competitions. Based on the weighted ranking of the five dimensions, personnel capability was assigned with the highest weighting, followed by support capability, while process capability was the least important. This is significantly different from the emphasis placed on technical and process capability in BIM capability research in the traditional AEC industry [52,91]. Therefore, the existing BIM capability framework is not suitable for screening excellent instructors. This further emphasizes the necessity of this study.

Another important finding of this study was that participating students from different levels of universities and different educational backgrounds have diverse requirements for the BIM capability of their instructors. Personnel capability was the dimension with the highest weight coefficient, including encouragement and support, vision and objectives, and psychological counseling. BIM capability models constructed in different contexts have emphasized the importance of personnel capability. Although different research subjects are targeted, this highlights the necessity of personnel capability in project management [40,49]. Based on the importance and support of Chinese education for similar innovative practice activities such as BIM competitions, most participating students recognized the importance of instructors’ personnel capability, especially their vision and objectives.

Different types of students participate in BIM competitions for different purposes. Students from the Double First-Class Initiative had multiple purposes for participating in the competition. Firstly, they hoped to master BIM technology through participation and enhance their competitiveness.; Secondly, obtaining postgraduate exemption was also an important goal; due to the importance of enhancing students’ innovative practical abilities in Chinese universities, award-winning students would receive grade point bonuses, which would help them obtain scholarships. This was also an important goal for students from junior colleges and ordinary undergraduate colleges. With the increasing demand for BIM talent in the AEC industry, obtaining more employment opportunities was also an important motivation for them. Setting goals and significance for winning awards could enhance their motivation and drive.

The support capability of instructors was also highly valued by participating students, including information collection and integration capability, training and education, acquisition of competition venues, hardware configuration, network environment, adaptability, software usage authorization, financial support, and technical assistance. In other contexts, this ability is considered a resource and hardware. Dib et al. [58] classified BIM capability as planning and management, process, team structure, hardware, process definition, and information management. Mahamadu et al. [57] classified BIM capability attributes for pre-qualification and selection activities, namely competence and resources, culture and attitude, and cost. Obviously, in BIM competitions, there is a higher demand for the support capability of instructors.

Different types of students attached varying degrees of importance to these capability indicators. Students from the Double First-Class Initiative had good competition conditions due to the sufficient funding of their respective universities. Therefore, they believed that the adaptability and technical assistance of instructors were the most important. The adaptability of the instructors helped to solve unexpected problems during the competition process, such as personnel changes, competition rules changes, etc., which helped them to efficiently complete the competition. Although Double First-Class Initiative students attached great importance to BIM, some of its technologies were still lacking. Inviting technical assistance could provide them with cutting-edge BIM knowledge and technical guidance, which kept them competitive in the competition.

Participating students from vocational colleges placed greater emphasize on instructors to provide support for basic competition facilities, such as funding support, venue, network environment, and software usage authorization. This was because ordinary universities have limited educational funding and limited support for innovative practical activities such as BIM competitions. Compared to students from regular undergraduate colleges, participating students believed that the training and education provided by instructors to the students was the most important aspect of competition preparation and support capability. Compared to vocational colleges, undergraduate colleges have more funds but do not have the same level of BIM education as the Double First-Class Initiative. Therefore, participating students from undergraduate colleges attached more importance to instructors in their BIM technology training and education. This was considered an effective way to improve their level of BIM.

Surprisingly, the technical capability of the instructors was the factor with the lowest weighting coefficient, which was different from the existing BIM capability frameworks’ emphasis on technical and process capability [38]. The main reason for this was China’s emphasis on and popularization of BIM technology. Most universities had launched BIM related courses, and most participating students had a certain basic knowledge of BIM technology. In addition, as BIM technology was operated on computer software, students generally had strong operational and learning abilities. However, students from different levels of schools also had different opinions on technical capability. The technical capability of instructors included BIM theoretical knowledge, software application capability, problem-solving capability, innovation capability, and professional knowledge of civil engineering. Participating students from the Double First-Class Initiative placed greater emphasis on the innovative capability of their instructors, expecting that instructors would cultivate their innovative thinking and practical abilities to achieve innovative results in BIM competitions. Students from undergraduate institutions believed that instructors’ capability to solve practical problems was more helpful to them. However, vocational students, due to their relatively weak professional knowledge, preferred to receive guidance from instructors with civil engineering knowledge during the competition process.

In addition, graduate students had different views on the BIM capability of instructors compared to undergraduate students. Graduate students owned good self-discipline and relatively good practical capability. They participated in innovative practical activities similar to BIM competitions to be exposed to the forefront of civil engineering and professional technology. Therefore, the capability of instructors to invite technical assistance was more recognized. Through contact with technical assistance, graduate students could be exposed to the cutting-edge technologies of BIM, which not only opened up new ideas for their research but also enhanced their competitiveness in the digital technology field.

In summary, the BIM capability assessment model is not based on established standards, but rather on mutual inspiration. Therefore, there is no universally accepted and widely used model [46]. Moreover, students from different educational backgrounds also have varying demands for the BIM capability of their instructors. Evaluating the BIM capabilities of BIM competition instructors can help improve the teaching effectiveness of BIM courses in universities and innovate the course content and teaching methods. In addition, this also helps to collect timely feedback from students, peers, and industry experts to evaluate and improve BIM education projects. Adjusting teaching methods and content based on feedback is necessary to ensure that educational programs keep pace with industry development. BIM education is an important means to meet the demand for BIM talents in the AEC industry. This research result will help cultivate more outstanding BIM talents for the AEC industry and alleviate the shortage of BIM talents in the AEC industry. BIM competitions are an important carrier for digital education practices in AEC related disciplines in universities. As the organizer and leader of digital education practice in universities, this article constructed a BIM capability evaluation model for instructors from the perspective of participating students, effectively promoting the digital practice and sustainable development of AEC related disciplines in universities.

Due to the lack of a BIM capabilities research framework for education personnel in the field of BIM education, this article only compared the theoretical results with the BIM capabilities research results of stakeholders in the AEC industry (such as owners, contractors, etc.) [42,43]. We found that there were differences between the BIM capabilities framework of instructors and stakeholders in the AEC industry, such as the psychological counseling capability of instructors, which is a typical difference. This difference arises from the different purposes of constructing BIM capabilities indicators between the two. For example, the goal of building a BIM capability framework for facility owners is to ensure the performance, efficiency, and profitability of assets in commercial activities. The construction of the BIM capabilities framework for instructors is aimed at improving students’ professional skills and competitiveness in educational activities. In other words, the ultimate goal of instructors is to cultivate talents through BIM competitions, and psychological counseling is an activity in competition guidance and also a responsibility of Chinese teachers. The capability framework established in the AEC industry is mostly built for commercial activities, and owners have no responsibility to consider the psychological feelings of participants. In addition, in terms of selecting capability indicators, the principles and decisions for facility owners’ BIM capability indicator selection are based on long-term investment returns, risk management, and asset performance. The indicator selection decisions of instructor BIM capabilities is more based on educational practice effectiveness, student participation, and competition results.

However, whether in the AEC industry or BIM competitions, the evaluation of BIM capability frameworks emphasizes the importance of the capability to collaborate effectively. In the AEC industry, this helps ensure close communication and collaboration among all parties involved in the project process, achieving a win-win situation; In BIM competitions, it helps to improve the overall performance of the participating teams. At the same time, both attach great importance to the indicator of “innovation capability”. Evaluating BIM capabilities can motivate all parties to continuously learn and master new BIM technologies and promote technological innovation and progress, which is crucial for the long-term development of the AEC industry and BIM education.

5. Conclusions and Recommendations

BIM is an important carrier for the digitalization and sustainable development of disciplines related to the AEC industry. With the popularization of BIM, the demand for BIM talents in the AEC industry is increasing [19]. BIM education is considered an important way to cultivate BIM talents [22]. It is an important digital platform for combining theory with practice in BIM education. As the direct organizer and leader of the competition, instructor BIM capability has a significant impact on competition results and learning outcomes. However, previous studies have not focused on the BIM capability of instructors. From the perspective of participating students, this article constructed a BIM capability evaluation framework of competition instructors using survey questionnaires and SEM methods, filling this research gap.

The framework proposed in this article includes five main dimensions, namely technical capability, organization capability, personnel capability, process capability, and support capability, with a total of 23 secondary capability indicators. This confirmed the multidimensionality of the BIM capability of instructors. Through path analysis, the weights of every capability dimension were determined. The results showed that personnel capability was the most important BIM capability for instructors, followed by support capability, and process capability was the least important, which was significantly different from the emphasis on BIM technology and process competence in the AEC industry. The weights of secondary capability indicators in different dimensions were variable. It should be noted that “Vision and objectives” was the most important in the dimension of personnel capability. Domestic universities attach great importance to innovation practice activities similar to BIM competitions, and award-winning students could receive GPA bonus points or exemption from postgraduate entrance exams. Students who master higher levels of BIM knowledge and practical abilities will not only have more career choices, but also be more competitive in the job market. Due to fierce market competition, possessing BIM skills can also contribute to future career development. With the increasing demand for BIM talents in the AEC industry, obtaining more employment opportunities was also an important motivation for them. Setting goals and defining the benefits for winning awards to participating students would greatly enhance their motivation. This reflects the characteristics of Chinese higher education that emphasizes innovation and practice.

This article also enriches the theoretical knowledge of BIM education and provides the concepts of BIM capability indicators for instructors. The proposed BIM capability evaluation framework for instructors provides a reference for universities to select excellent instructors, and also guides the direction for instructors to improve their BIM capability. In addition, this study laid a foundation for research related to relevant capability of instructors in other disciplines and BIM education. It also provides strong support for future research and practical work related to BIM education.

6. Limitations and Future Research

However, some limitations of this research need to be acknowledged. One issue was that the data collected by this research institute came from China. The BIM capability evaluation framework for instructors might vary with different compositions of participants with distinct experiences and backgrounds. Therefore, conducting more extensive data collection in different countries and regions could enhance the applicability of the BIM capability evaluation framework for instructors. Another limitation was that the results of this study were based on the current competitive education scenarios. With the improvement of BIM education in universities and the development of BIM technology, the framework for evaluating the BIM capability of instructors might change. The use of snowball sampling may have potential limitations and impacts on research results, and it is recommended that future studies use more extensive sampling methods to validate our findings.

Future research can first expand the coverage of the study, explore the adaptability of the framework in different countries, and conduct a more comprehensive study of the BIM capabilities of instructors from the perspectives of multiple stakeholders, including instructors, event organizers, and BIM training institutions. Furthermore, it is also possible to explore how to improve the quality of BIM education in different types and levels of universities through measures such as optimizing personnel and resource allocation. This includes comparing the differences in supporting BIM education among different higher education institutions, establishing and implementing mechanisms to enhance the BIM capabilities of instructors, and developing corresponding improvement measures. It also includes exploring the costs and challenges involved in implementing these research results in universities to ensure that they can be effectively translated into educational practice. Through continuous in-depth research and accumulation, we hope to provide a solid theoretical and practical foundation for the development of BIM education, thereby promoting the progress of the entire industry.