1. Introduction
As a major digital technology used by the AEC industry [1], building information modeling (BIM) has emerged as the main focus of professional learning in AEC-related higher education [2]. BIM learning has become the foundation for the sustainable development of the AEC industry. BIM learning supports the sustainable application of BIM technology across the stages of design, procurement, manufacturing, construction, operation, and maintenance [3]. Although scholars have made great progress in BIM research, with BIM technology now even widely used in Financial Investment Projects [4], nevertheless, there remains a disconnection between theoretical education and engineering practice in BIM learning [5].
College and university discipline competitions are based on classroom teaching where competition is used to stimulate student’s abilities, such as combining theory with practice, independent thinking and teamwork [6]. Discipline competition can cultivate students’ practical innovation ability and promote the all-round development of college students, which has become an important part of practical teaching [7]. Discipline competition is not only an important means of talent training in universities, but also an important basis by which employers select graduates [8]. Thus, college students respond by showing great interests in discipline competitions. At present, there are a large number of BIM related discipline competitions being held in China, as well as in other countries. These include: the Annual Brilliance Competition for Infrastructure, the Excellence in BIM Competition for the Global Engineering and Construction Industry, the Hong Kong International BIM Competition, the Building SMART International Competition, the China National Competitions, the National College BIM Graduation Design Innovation Competition, the China Construction Engineering BIM Competition, the Transportation BIM Engineering Innovation Award, the Innovation Cup Building Information Model Application Design Competition, the You-lu Cup National BIM Technology Competition, the Municipal Cup BIM Application Skills Competition, among others. Moreover, BIM-related competitions are sponsored by enterprises and industry associations across various provinces and cities in China. The organizers of discipline competitions usually partner with enterprises. Students not only enhance their BIM skills as demanded by the requirements of the competition, but also leverage their career development after employability [9].
Nationally, across China, graduation design is a compulsory course of AEC-related education, which is the main link between theoretical education and engineering practice. It is formulated to help students smoothly transition from school education to professional practice after graduation [5]. BIM technology is sits at the core of the future sustainable development of the AEC industry. Therefore, it is crucial to identify the driving factors of BIM learning outcomes and extract those mechanisms that augment BIM learning performance. Consequently, this study investigates the sixth National BIM Graduation Design Innovation Competition of Colleges and Universities (sixth NBGDIC) as a representative vehicle for unpacking the driving factors that accelerate BIM learning. To this end, exploratory factor analysis and ordered logistic regression of returned questionnaire survey results are used.
2. Literature Review
2.1. BIM and Related Technologies
Building information modeling (BIM) and virtual reality (VR) have attracted increasing attention within the AEC industry over recent years. Virtual reality allows simulation of dangerous, expensive or hard-to-reach environments. It can improve the learning process and motivate students to develop the skills they need to succeed and innovate [10]. Alizadehsalehi, S., et al. argues that VR technology in the AEC industry makes it easier for students to understand digital data in BIM [11]. In addition, Wang, L., et al. suggests that BIM can be integrated with other digital technologies or platforms, such as 3D printing, drones, mixed reality and laser scanning [2]. Digital twins (DT) can use both real-time and historical data to simulate the future state of an asset and predict the consequences of maintenance operations [12]. Many researchers try to use BIM as a basis to develop the DT of a building, for either existing buildings or those in the design stage [13]. The combination of extended reality (XR) and BIM provides interactive renderings, spatial coordination, and virtual models, and provides many benefits that can improve the performance of AEC projects, including time, cost, quality, and safety. This integration is useful not only during the design and construction phases, but also during maintenance [13]. In the AEC industry, XR technology that simulates a building project in a multidimensional digital model and presents multiple aspects of the project can be of great help at all stages of a project [14].
2.2. Factors Impacting BIM Learning Performance
A systematic examination of existing literature reveals the current, known factors influencing discipline competition outcomes, and these include the demographic attributes of participants, social environment incentives, level of team cooperation, ability of instructor, among others [15,16]. This study develops this knowledge to in an effort to establish the influencing factors impacting the BIM learning performance of AEC undergraduates.
Obviously, the individual idiosyncratic characteristics of participants are the key determinates of learning performance. Previous studies have found that individual ability (thinking, reasoning and task execution) and achievement demand (motivation, attitude or belief) significantly affect students’ learning performance [16,17]. Individual thinking mode, reasoning, and task execution ability can affect and assist individuals to deal with the problem of multilateral cooperation between multiple tasks [18], thus significantly affecting the final learning outcomes of learners [19]. However, individual ability is not the only driving factor of learning performance [20]. Peiró, J.M., et al. (2020) found that the learners’ perception of the importance of the learning task significantly affects learning motivation, attitude and belief, and thus consequently affects ultimate learning performance [21]. Over the duration of a discipline competition, students will draw on their personal store of knowledge and practical ability, yet incentivization through awards and honors can also leverage motivation, which in turn can be expected to impact final learning performance.
In addition to the learner’s personal characteristics, the instructors’ comprehensive ability will also significantly affect the students’ learning performance, good teachers can improve the students’ learning scores to varying degrees [22]. Some studies have shown that the important factors affecting the learning quality of college students include the professional ability [23,24], daily guidance behavior [25], and the effectiveness of guidance [26] of the instructors. Teams participating in the BIM Graduation Design Innovation Competition need to complete the work with the help of the instructors. The competition process involves theoretical knowledge and practical operation. Therefore, the instructors are important for BIM learning performance.
“Teamwork” refers to a method of cooperating with a group of people to achieve a goal through interdependent actions and mutual accountability. Effective teamwork requires individual self-efficacy, proper communication among members, and acceptance of suggestions and instructions from leaders and others [27]. Studies have found that team size and communication frequency between team members have a significant effect on team work performance [28]. The National BIM Graduation Design Innovation Competition is a blend of project construction cost, engineering management, building material supply, and so on, which needs students from different majors cooperating with each other and apply different software to complete the solution design, digital construction design, 3D digital model reconstruction, writing reports, and so on. Thus, team members need to communicate effectively and work in close collaboration.
Social environment incentive refers to the incentive effect generated by the social system, production relations, and social relations between people in a specific period and a specific region [6]. External objective factors such as the rigid requirements of schools for students to participate in discipline competitions and the influence of the group they are in, as well as motivational factors such as school rewards and class awards and excellence evaluation, may have a great influence on student’ learning performance [29]. For example, as time goes on, individuals will be influenced by the team, and the goals of individuals and groups will gradually converge [30]. At the same time, performance-based rewards have a positive effect on individual performance [31], and rewards can be used as an effective management tool to improve people’s work performance [32].
School is the main platform for education and teaching activities, and the impact of school education and teaching on the students’ learning performance is particularly important [33]. School atmosphere is a comprehensive reflection of the school environment variable, which not only refers to the physical environment in school (such as construction, lighting, and so on), but also to the individual measurement of school experience, including the organizational level, the teaching level, interpersonal relationship, culture, values, and other aspects [34,35]. The school atmosphere exerts a subtle influence on students, which is directly related to the students’ academic achievements and future development [36]. A good learning atmosphere not only includes reasonable curriculum planning, positive teaching atmosphere, perfect hardware facilities, but also includes the emphasis given by the school to students’ extracurricular activities and taking an active part in competitions.
Research shows that college students have a higher understanding of the industry and better academic performance, and they have a higher probability of finding an ideal job [37]. Therefore, if students have a clearer understanding of their ideal employment after graduation, they will be encouraged to spend more time working towards it. BIM implementation strategies are increasingly driving the use of this technology in the AEC industry worldwide, and Chinese Government agencies have deployed a number of strategies aimed at promoting the development and adoption of BIM [38]. Since 2011, The Ministry of Housing and Urban-Rural Development of China has updated the BIM technology promotion policy annually. In December 2016, the Unified Standard for the Application of Building Information Model approved by the Ministry of Housing and Urban-Rural Development was the first national standard for the application of BIM in China, which had a critical impact on the development of BIM technology in China. From 2017, a series of policies and standard documents issued by the State Council and the Ministry of Transport has also promoted the sustainable development of BIM technology. These policy documents targeted specific projects on the adoption of BIM, and provide technical guidance for the application of BIM across all national engineering endeavors. In 2018, policies and guidelines on BIM implementation were again updated. The construction industry has thus transitioned from traditional construction practices to BIM-based practice. The more students know regarding the application of the BIM within industry, the more they can be expected to be motivated to master BIM technology and in so doing, cultivate the furtherance of BIM uptake across the AEC industry.
3. Materials and Methods
3.1. Questionnaire Design
Based on the previous research and the tracking investigation of the sixth NBGDIC, the questionnaire design of this study mainly includes two parts: Part I: basic demographic information of the participants, including gender, age, team name, school, major, grade, years of using BIM-related software, the degree of correlation between this competition and their graduation design, and so on; Part II: BIM learning performance driving factor scale. A five-point Likert scale was used to measure the degree of conformity between each observation variable and the contestant [39,40,41,42,43]. Points 1 to 5 indicated “completely inconsistent”, “basically inconsistent”, “general”, “relatively consistent” and “completely consistent”, respectively. The measurement scale of the questionnaire is mainly derived from relevant literature and combined with interviews with competitors. Seven predictors, including individual ability, achievement demand, instructor quality, teamwork, social environment incentive, school (college) atmosphere, and understanding of BIM industry application, were constructed, and 44 corresponding measurement items were designed.
3.2. Date Collection
The purpose of the sixth NBGDIC is to promote the BIM application skills for AEC related students. The sixth NBGDIC provides a new way for the application and exploration of BIM technology in colleges and universities, provides ideas for opening BIM-related courses and subject research, and improves the employment rate and employment quality of students. The participating teams of the sixth NBGDIC will upload their work through the Internet according to the registration items. The uploading time is from March 2020 to April 2020. The competition contains seven modules: (A) BIM modeling application, (B) BIM cost management, (C) BIM-based digital construction organization design, (D) BIM-based 5D construction management, (E) structural design based on BIM+ assembly, (F) innovation module, including intelligent construction and management innovation, (G) architectural decoration virtual design. This competition sets up four awards: first prize, second prize, third prize and excellence award. This research questionnaires, as the feedback of the participating students’ experience, were issued to the competition system by the sponsor Glodon Company Limited on 8 May 2020. After a month of follow-up investigation, 2790 questionnaires were finally collected, and questionnaires with missing data were eliminated. Finally, 2777 valid questionnaires were collected with an effective recovery rate of 99.53%. The results of the competitors were published on the official website (
3.3. Data Analysis Method
The SPSS24.0 software was used to analyze the survey data. The Cronbach’s alpha coefficient method was used to test the internal consistency of questionnaires data, principal component factor analysis was used to test the structural validity of questionnaires data, and the t-test or ANOVA was used to check the inter-group differences of data. Exploratory factor analysis was conducted on the 44 driving factors of BIM learning performance by maximum variance method. Finally, the BIM competition results are taken as BIM learning performance and the influence of the aforementioned factors based on BIM learning performance is explored by ordered logistics regression [44].
4. Results
4.1. Data Description
In this study, 2777 valid questionnaires were collected, there were 1582 male contestants and 1195 female contestants. The age group ranged from 19 to 24 years old (n = 2713, accounting for 96.7%). Juniors (n = 1007, 36.3%) and seniors (n = 1340, 48.3%) accounted for more than 70% of the total competitors. The participants came from 330 colleges and universities, among them, 134 were research-oriented colleges/universities, accounting for 4.8%, 2041 were application-oriented colleges/universities, accounting for 73.5%, and 588 were applied research-oriented colleges/universities, accounting for 21.2% (the distribution of the sample colleges/universities are shown in Figure 1). According to different majors of competitors accounting for more than 1%, the competitors were divided into seven categories, including construction management (n = 973, accounting for 35.0%), engineering cost (n = 851, accounting for 30.6%), civil engineering (n = 529, accounting for 19.0%), water supply and drainage science and engineering (n = 71, accounting for 2.6%), building environment and energy application engineering (n = 63, 2.3%), building electrical and intelligent engineering (n = 51, 1.8%), and others (n = 239, 8.6%); the majority of participants have learned and used BIM-related software for no more than 5 years, and those who have used BIM for 2 years are the most (n = 937, accounting for 33.7%). More than 50% of the participants thought that the sixth NBGDIC was related to their graduation designs (n = 1414, accounting for 50.9%); the details of the participants are shown in Table 1.
4.2. Reliability and Validity Test
Generally, the Cronbach’s alpha is greater than 0.8, which indicates an excellent internal consistency of data, which fulfills the requirements for further analysis. The results of the data analysis in this study show that the Cronbach’s alpha coefficient is 0.962 and the number of items is 44, indicating that the data have good reliability. Meanwhile, the KMO value of the study data is 0.965, which is greater than the critical value of 0.5, and the significance probability of the Bartlett sphericity test is 0, indicating that the data are suitable for factor analysis.
4.3. Difference Analysis between Groups
Results show that the maximum average score of the contestants is 3.07 ± 1.291, among which 39.4% are above the average score, and 60.6% are below the average score, in which males are (3.1 ± 1.3) and females are (3.0 ± 1.3). The applied research colleges/universities (3.2 ± 1.3) received the highest score, students majoring in building electrical and intelligent engineering received the highest score of (3.3 ± 1.2), and freshmen received the lowest score of (2.5 ± 1.4). Participants who have studied and used BIM-related software for more than 3 years have the highest score (3.3 ± 1.3), and no significant difference exists among the relevant degree of this competition and their graduation design. The difference analysis results of the BIM learning performance between groups are shown in Table 2.
4.4. Exploratory Factor Analysis
In this study, the dimension reduction was conducted on 44 BIM learning driving factor items. In the exploratory factor analysis process, the method of maximum variance was used for factor rotation, which made each factor load change to the two extremes of larger or smaller, and the items with the maximum rotation load of less than 0.4 were deleted. Results show that there were seven common factors with eigenvalues greater than 1, and the total variance explanatory variance was 73.022%. The common factors of the seven dimensions are 1 = the comprehensive ability of instructor, 2 = school atmosphere, 3 = team collaboration, 4 = participants’ personal ability, 5 = understanding of BIM industry application, 6 = social environment incentive, and 7 = achievement demand. The specific results of each factor load are shown in Table 3.
4.5. Ordered Logistic Regression
In this study, ordered logistic regression was used to explore the influence of the above factors on BIM learning performance based on the sixth NBGDIC. The results show that among the seven common factors, four common factors significantly affected the BIM learning performance, which were the comprehensive ability of instructor (p = 0.002 < 0.1), team collaboration (p = 0 < 0.1), participants’ personal ability (p = 0.001 < 0.1), and achievement needs (p = 0.066 < 0.01); Moreover, the gender, type of school, type of major and time of learning and using BIM-related software all have a significant effect on the BIM learning performance; however, the college/university atmosphere, participants’ understanding of BIM industry application, social environment incentive, and the correlation between this competition and their graduation design have no significant effect. The detailed results of the ordered logistic regression are shown in Table 4.
5. Discussion
5.1. Effects of Demographic Variables
The time of the participants’ learning and use of BIM-related software has a significantly positive effect on their BIM learning performance (β = 0.071, p = 0.049). BIM provides a collaboration platform to support collaboration between different business fields [39], in which competitors need to spend energy and time to learn the operation of BIM software. However, accumulating scientific knowledge can improve innovation performance. Therefore, continuous accumulation of BIM-related software learning and operation is the guarantee to improve BIM learning performance. The BIM learning performance of male students was significantly higher than that of female students (β = 0.159, p = 0.029). BIM learning involves professional theories and software operation practices related to construction. Existing studies show that male students are more in demand in the construction industry, and male students are more suitable to engage in construction-related jobs from the perspective of construction enterprises [45]. The BIM learning performance of students from different types of schools is significantly different between groups. The BIM learning performance of students from research-oriented colleges/universities (3.1 ± 1.3) and application colleges/universities (3.0 ± 1.4) is lower than that of students from applied research colleges/universities (3.2 ± 1.3). According to the results of the ordered logistic regression, taking the BIM learning performance of students in applied research universities as the reference, the BIM learning performance coefficients of students in both research and application universities are significantly negative. Therefore, the BIM learning performance of students in applied research colleges/universities is relatively better. BIM learning requires not only a professional theoretical basis, but also practical ability, which is consistent with the educational orientation of “practical application + theoretical research” in applied research universities [46]. There are also significant differences in the BIM learning performance of students across different majors. The scores, from high to low, are building electrical and intelligence (3.3 ± 1.2), building environment and energy application engineering (3.2 ± 1.5), civil engineering (3.2 ± 1.3), engineering cost (3.1 ± 1.3), water supply and drainage science and engineering (3.1 ± 1.2), engineering management (3.0 ± 1.3), and others (2.8 ± 1.3). The results of the ordered logistic regression show that the majors of higher education students significantly affect the BIM learning performance. With the continuous promotion and practical application of BIM technology, BIM technology has been widely known by practitioners in the construction industry, and BIM technology education has also been implanted into the talent training programs of AEC-related majors [47]. In addition, the diversification and complexity of building functions also require the cooperation of multiple professionals to complete the implementation of BIM technology [48]. Therefore, from the analysis of BIM learning performance, students majoring in new technology and construction have better BIM learning performance, but the comprehensive application of BIM technology should be completed in collaboration with multiple majors to realize the effective collaborative management of BIM.
5.2. Effects of Common Factors
The above results show that in the sixth NBGDIC, four common factors have significantly positive effects on the BIM learning performance, and the effect degree from large to small is as follows: team collaboration, individual ability, the comprehensive ability of instructor and achievement demand. First, team collaboration is significantly and positively correlated with BIM learning performance (β = 0.191, p = 0.000), indicating that the degree of cooperation between team members will significantly improve BIM learning performance. David W. Johnson (1993), an American education scholar, found that “cooperative learning is the most important and most successful in more than ten years” [49]. Cooperative learning has been widely used in teaching reform in many countries and regions in the world. Cooperative learning can improve students’ learning performance to the greatest extent [50]. As the frequency and quality of team members’ communication improves, so does team work performance [51]. The NBGDIC is a multi-disciplinary cross-integration competition, which requires team members to have a high degree of cohesion and cooperation. Each member should perform a good job in their own professional field and everyone should communicate effectively and work together to complete the competition. Therefore, the degree of team collaboration becomes the most significant factor affecting BIM learning performance. The individual ability of participants significantly affects BIM learning performance (β = 0.122, p = 0.001), which becomes the second most influential factor after team collaboration. Studies have shown that students’ self-learning ability and professional foundation significantly affects professional learning performance [52]. Instructor’s comprehensive ability also have significant positive effect on BIM learning performance (β = 0.107, p = 0.002). According to the measurement indicators of instructors’ comprehensive ability, BIM learning performance can be significantly improved by encouragement, affirmation, effective suggestions and in-depth guidance from instructors. Existing studies have also shown that the harmonious relationship between instructors and students may play an important role in promoting the students’ development and affects the students’ systematic mastery of certain knowledge [53]. The results of this study show that participants’ achievement requirement have a significant positive effect on their BIM learning performance (β = 0.063, p = 0.066). Existing studies show that the learners’ perception of the importance of learning tasks and their interest in learning tasks may significantly affect their learning motivation, attitude, and belief, thus affecting their learning performance [21], which is consistent with our research results.
6. Conclusions and Recommendations
This is the first study to take the participants of the Sixth National BIM Graduation Design Innovation Competition of Colleges and Universities as the object of investigation, and to undertake a research design, data collection, and data analysis on the influencing factors of BIM learning performance within such a context. Over the month following the event a questionnaire survey was sent out to participants and a total of 2777 valid responses were returned for analysis. Seven common factors affecting BIM learning performance were extracted using exploratory factor analysis, while an ordered logistic regression was used to ascertain the influence of the derived driving factors on BIM learning outcomes. Findings provide theoretical support for an in-depth understanding of BIM learning drivers, while also offering insights into ways and means by which BIM education in Chinese colleges and universities can be further enhanced. These are:
(1). Strengthen the fundamental professional knowledge and sustainable learning ability of college students majoring in AEC related disciplines;
(2). Provide real-time attention to the developmental trends of BIM in industry and translate these into the BIM learning environment, which in turn will improve students’ incentives, willingness and interest in mastering BIM;
(3). Encourage students of multiple majors in AEC related disciplines to cooperate and learn BIM, and create reasonable divisions of labor while sharing skills according to professional backgrounds;
(4). Improve the comprehensive ability of BIM instructors in all aspects of BIM, including in competencies such as team liaison, the technical features of BIM, and BIM as a management tool, combined with a systematic understanding of the developmental trends of BIM in the AEC industry;
(5). Undergraduate AEC related majors should implant BIM theory and application into current ‘talent training’ programs across the full duration of the four-year learning process in colleges and universities.
While significant research advancements have been offered in this paper, there are limitations. These may be taken up in subsequent research work:
(1). This is the first study on BIM learning performance undertaken with regard to the annual NBGDIC competitions. However, the impact students’ various majors have on BIM learning outcomes, while anticipated to exert some influence, was not investigated.
(2). In conducting the survey, it was discovered that extant research has paid scant attention to the application of BIM technology in colleges and universities.
The anticipated next related research step will be to examine how to fully combine BIM education with university education, promote and publicize BIM education in colleges and universities, promote BIM as a legitimate, independent professional course in colleges and universities, and conduct in-depth research on the development of cooperative ties regarding BIM between universities and industry.
Conceptualization, Y.A. and Y.L.; methodology, Y.A.; software, Y.L.; validation, L.T. (Liyao Tan), M.Z. and L.T. (Ling Tan); formal analysis, Q.F.; investigation, J.Z., L.T. (Liyao Tan), M.Z., Q.F. and Y.W.; resources, Y.W.; data curation, Y.L.; writing—original draft preparation, Y.L., I.M.; writing—review and editing, Y.A.; visualization, L.Z.; supervision, Y.A.; project administration, Y.A.; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.
This study is jointly supported by the National Natural Science Foundation of China (72171028), School-enterprise Cooperative Education Program of Ministry of Education (201901098002), Sichuan Rural Community Governance Research Center (SQZL2021A01 and SQZL2021B03), Sichuan Disaster Economics Research Center (ZHJJ2021-YB004), Meteorological Disaster Prediction and Emergency Management Research Center (ZHYJ21-YB06), Sichuan Rural Development Research Center (CR2101), Regional Public Management Information Research Center (QGXH21-02), Open Foundation of the Research Center for Human Geography of Tibetan Plateau and Its Eastern Slope (Chengdu University of Technology) (RWDL2021-ZD001), Energy and Environmental Policy Research Center, Chengdu University of Technology (CEE2021-ZD02), and Chengdu Philosophy and Social Science Research Base—Chengdu Park Urban Demonstration Area Construction Research Center project (GYCS2021-YB001).
The study was approved by Department of Construction Management, Chengdu University of Technology.
Informed consent was obtained from all subjects involved in the study.
The data are available from the corresponding author upon reason-able request.
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. The distribution of the sample colleges and universities.
Demographic information of the participants (N = 2777).
| Characteristics | Samples | ||
|---|---|---|---|
| Frequency | Proportion (%) | ||
| Gender | Male | 1582 | 57.0 |
| Female | 1195 | 43.0 | |
| Age | Under 19 | 14 | 0.5 |
| 19–21 | 1155 | 41.6 | |
| 22–24 | 1558 | 56.1 | |
| 25 and above | 50 | 1.8 | |
| School type | Research | 134 | 4.8 |
| Application | 2041 | 73.5 | |
| Applied research | 588 | 21.2 | |
| Major types | Engineering management | 973 | 35.0 |
| Engineering cost | 851 | 30.6 | |
| Civil engineering | 529 | 19.0 | |
| Water supply and drainage science and engineering | 71 | 2.6 | |
| Building environment and energy application Engineering | 63 | 2.3 | |
| Building electrical and intelligent | 51 | 1.8 | |
| other | 239 | 8.6 | |
| Grade | Freshman | 41 | 1.5 |
| Sophomore | 389 | 14 | |
| Junior | 1007 | 36.3 | |
| Senior | 1340 | 48.3 | |
| Time of learning and using BIM software | 0–0.5 years (excluding 0.5 years) | 734 | 26.4 |
| 0.5–1 years (excluding 1years) | 937 | 33.7 | |
| 1–2 years (excluding 2 years) | 763 | 27.5 | |
| 2–3 years (excluding 3 years) | 273 | 9.8 | |
| 3 years or above | 70 | 2.5 | |
| The related degree between this competition and their graduation design | Totally irrelevant | 46 | 1.7 |
| Basically irrelevant | 151 | 5.4 | |
| General | 534 | 19.2 | |
| More relevant | 1414 | 50.9 | |
| Complete related | 632 | 22.8 | |
Analysis of the differences between groups of different categories (
| Characteristics | Category | Achievement Maximum |
|---|---|---|
| Gender | Male | 3.1 ± 1.3 |
| Female | 3.0 ± 1.3 | |
| t | 2.682 | |
| P | 0.007 | |
| Age | Under 19 | 1.5 ± 0.4 |
| 19–21 | 1.3 ± 0.0 | |
| 22–24 | 1.3 ± 0.0 | |
| 25 and above | 1.2 ± 0.2 | |
| F | 0.555 | |
| P | 0.645 | |
| School type | Research | 3.1 ± 1.3 |
| Application | 3.0 ± 1.4 | |
| Applied research | 3.2 ± 1.3 | |
| F | 2.637 | |
| P | 0.072 | |
| Major types | Engineering management | 3.0 ± 1.3 |
| Engineering cost | 3.1 ± 1.3 | |
| Civil engineering | 3.2 ± 1.3 | |
| Water supply and drainage science and engineering | 3.1 ± 1.2 | |
| Building environment and energy application engineering | 3.2 ± 1.5 | |
| Building electrical and intelligent | 3.3 ± 1.2 | |
| other | 2.8 ± 1.3 | |
| F | 2.793 | |
| P | 0.01 | |
| Grade | Freshman year | 2.5 ± 1.4 |
| Sophomore year | 3.1 ± 1.3 | |
| Junior year | 3.1 ± 1.3 | |
| Senior year | 3.0 ± 1.3 | |
| F | 4.284 | |
| P | 0.005 | |
| Time of learning and using BIM software | 0–0.5 years (excluding 0.5 years) | 3.0 ± 1.3 |
| 0.5–1 years (excluding 1years) | 3.1 ± 1.3 | |
| 1–2 years (excluding 2 years) | 3.1 ± 1.3 | |
| 2–3 years (excluding 3 years) | 3.2 ± 1.3 | |
| 3 years or above | 3.3 ± 1.3 | |
| F | 2.869 | |
| P | 0.022 | |
| The related degree between this competition and their graduation design | Totally irrelevant | 3.1 ± 1.4 |
| Basically irrelevant | 2.9 ± 1.2 | |
| General | 3.0 ± 1.3 | |
| More relevant | 3.1 ± 1.3 | |
| Complete related | 3.1 ± 1.3 | |
| F | 1.587 | |
| P | 0.175 |
Exploratory factor analysis (rotated component Matrix A).
| Items | Component | ||||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
| My instructor always gives me a lot of encouragement | 0.856 | ||||||
| My instructor always gives me useful advice | 0.855 | ||||||
| My instructor always affirms my progress | 0.853 | ||||||
| My instructor will give me in-depth guidance on my entries | 0.812 | ||||||
| I get on well with my instructor | 0.809 | ||||||
| My instructor attaches great importance to this competition | 0.792 | ||||||
| My instructor is very professional | 0.788 | ||||||
| My instructor is able to assign tasks according to team members’ characteristics | 0.759 | ||||||
| Even if my operation is not proficient, my instructor will not blame me | 0.748 | ||||||
| My instructor is very familiar with the rules and procedures of this competition | 0.741 | ||||||
| My instructor is very influential in the BIM industry | 0.599 | ||||||
| If the competition needs, our school can arrange corresponding personnel to coordinate | 0.796 | ||||||
| Our school conducted targeted lectures and training for participants | 0.768 | ||||||
| Our school values the participation of students in the competition | 0.756 | ||||||
| The BIM teaching methods in our school are diverse | 0.753 | ||||||
| Our school provides special equipment room and hardware supporting for the participants | 0.749 | ||||||
| Our school continuously promotes BIM development to students | 0.747 | ||||||
| School teachers have corresponding projects to support BIM technology practice | 0.738 | ||||||
| Our school offers a series of BIM related courses | 0.728 | ||||||
| School teachers are willing to recommend the winning entries to enterprises | 0.726 | ||||||
| School teachers are willing to recommend award-winning students to enterprises | 0.723 | ||||||
| Our school/college are willing to provide financial support for participating students | 0.687 | ||||||
| Our team has a reasonable division of labor and sincere cooperation | 0.805 | ||||||
| Our team has given me a lot of confidence | 0.794 | ||||||
| Our team had a smooth communication channel | 0.788 | ||||||
| Our team members are willing to share their own experience and technology to each other | 0.78 | ||||||
| Our team members have reasonable professional backgrounds | 0.74 | ||||||
| I have a strong ability to learn | 0.752 | ||||||
| I often learn BIM knowledge by myself on the Internet | 0.729 | ||||||
| I have solid professional knowledge and theory | 0.723 | ||||||
| I am familiar with the rules and procedures of this competition | 0.685 | ||||||
| I often participate in BIM related competitions | 0.619 | ||||||
| I am familiar with the application of BIM in architectural design | 0.798 | ||||||
| I am familiar with the application of BIM in architectural planning | 0.772 | ||||||
| I am familiar with the application of BIM in architectural construction | 0.77 | ||||||
| I am familiar with the application of BIM in architectural maintenance | 0.768 | ||||||
| The purpose of participating in the this competition is to get learning points and other incentives | 0.841 | ||||||
| The purpose of participating in this competition is to obtain material rewards | 0.836 | ||||||
| It is the instructor’s requirement to participate in this competition | 0.667 | ||||||
| Influenced by the team, I decided to participate in this competition | 0.655 | ||||||
| The purpose of participating in this competition is to improve my personal ability | 0.761 | ||||||
| The purpose of participating in this competition is to enrich my practical experience | 0.749 | ||||||
| It is very important to participate in this competition | 0.685 | ||||||
| It is my interest to participate in this competition | 0.62 | ||||||
Note: 1 to 7 represent the comprehensive ability of instructor, school atmosphere, team collaboration, participants’ personal ability, understanding of BIM industry application, social environment incentive, and achievement demand, respectively.
Ordered logistic regression results.
| Estimate | The Standard |
Wald | Degrees of |
Significant | 95% A Confidence Interval | |||
|---|---|---|---|---|---|---|---|---|
| Minimum | Maximum | |||||||
| The threshold value | (The maximum = 1) | −1.277 | 0.215 | 35.198 | 1 | 0 | −1.698 | −0.855 |
| (The maximum = 2) | −0.538 | 0.214 | 6.353 | 1 | 0.012 | −0.957 | −0.12 | |
| (The maximum = 3) | 0.781 | 0.214 | 13.332 | 1 | 0 | 0.362 | 1.2 | |
| (The maximum = 4) | 2.106 | 0.218 | 93.736 | 1 | 0 | 1.68 | 2.532 | |
| The comprehensive ability of instructor | 0.107 | 0.034 | 9.641 | 1 | 0.002 | 0.039 | 0.175 | |
| College/university atmosphere | −0.01 | 0.035 | 0.078 | 1 | 0.78 | −0.078 | 0.059 | |
| Team collaboration | 0.191 | 0.034 | 30.868 | 1 | 0 | 0.123 | 0.258 | |
| Individual ability | 0.122 | 0.036 | 11.216 | 1 | 0.001 | 0.05 | 0.193 | |
| Understanding BIM application | −0.004 | 0.035 | 0.013 | 1 | 0.909 | −0.072 | 0.064 | |
| Social environment incentive | −0.035 | 0.034 | 1.033 | 1 | 0.309 | −0.102 | 0.032 | |
| Achievement needs | 0.063 | 0.034 | 3.384 | 1 | 0.066 | −0.004 | 0.131 | |
| Time of learning BIM related software | 0.071 | 0.036 | 3.874 | 1 | 0.049 | 0 | 0.141 | |
| The related degree between this competition and their graduation design | −0.008 | 0.041 | 0.04 | 1 | 0.841 | −0.089 | 0.072 | |
| (gender: male = 1) | 0.159 | 0.073 | 4.761 | 1 | 0.029 | 0.016 | 0.301 | |
| (gender: female = 2) | 0A | 0 | ||||||
| (Type of school: application) | −0.273 | 0.086 | 10.1 | 1 | 0.001 | −0.441 | −0.105 | |
| (Type of school: research ) | −0.38 | 0.172 | 4.872 | 1 | 0.027 | −0.718 | −0.043 | |
| (Type of school: applied research) | 0A | 0 | ||||||
| (Major type: engineering management) | 0.254 | 0.132 | 3.714 | 1 | 0.054 | −0.004 | 0.513 | |
| (Major type: engineering cost) | 0.359 | 0.137 | 6.908 | 1 | 0.009 | 0.091 | 0.627 | |
| (Major type: civil engineering) | 0.501 | 0.141 | 12.54 | 1 | 0 | 0.224 | 0.778 | |
| (Major type: water supply and drainage science and engineering) | 0.472 | 0.245 | 3.719 | 1 | 0.054 | −0.008 | 0.953 | |
| (Major type: building environment and energy application engineering) | 0.569 | 0.255 | 4.962 | 1 | 0.026 | 0.068 | 1.07 | |
| (Major type: building electrical and intelligent) | 0.717 | 0.279 | 6.608 | 1 | 0.01 | 0.17 | 1.264 | |
| (Major type: other) | 0A | 0 | ||||||
Correlation function: fractional logarithm. A set this parameter to zero because it is redundant.
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