Research on the maker learning experiences of engineering college students majoring in mechanical engineering in China
Research on students’ learning experiences has become a hot topic among Chinese scholars. In recent years, some scholars have conducted studies on the learning experiences of engineering students. Professor Lu Genshu, by analysing the survey data of 2,394 first-year engineering students at Xi’an Jiaotong University, found a close relationship between engineering students’ learning styles and their learning outcomes, indicating that learning styles have a decisive impact on the quality of learning8. Zhao Tingting carried out a study to examine the learning engagement of 396 engineering students at University A and compared the results with the data norms from leading research universities in the United States. The findings showed significant differences in learning strategies, higher-order learning, and reflective and integrative learning between the students at University A and those at top US research institutions. Alarmingly, these gaps seemed to widen after four years of university education9. Guo Hui conducted a questionnaire survey involving more than 500 engineering students from five science and technology universities in central China, all of whom had participated in at least one full research experience. The results indicated considerable variations in research participation and learning engagement among students with different backgrounds, and research participation had a positive but moderate influence on learning engagement10. Su Linqin analysed CCSS survey data from undergraduate students at a “211 Project” engineering university in Beijing and found that learning engagement had a significant positive impact on learning outcomes, with behavioural, cognitive and emotional engagement having a positive impact on learning, while environmental engagement had a negative impact on learning outcomes11. Yu Dan’s evaluation of the learning outcomes of mechanical engineering students at Shanghai Jiao Tong University showed differences in learning outcomes under different education systems, and emphasised that engagement in the learning process affects the achievement of learning outcomes12. Various scholars in China have investigated the learning experiences, research experiences, learning engagement and learning outcomes of engineering students. However, there is limited research on the maker learning experiences of engineering students, focusing on the creation of real products or designs as a core activity.
In addition, scholars from both domestic and international arenas have conducted empirical research to analyze the impact of maker learning activities on the learning outcomes of engineering students. Mohamed Galaleldin and others suggest that integrating maker design experiences and fabrication activities into engineering curricula can enhance the self-efficacy of engineering students and bolster their confidence in engineering design and problem-solving13. The study conducted by Chung Chih-Chao et al. revealed that vocational college students’ engagement in maker learning courses exerts a significant impact on the cultivation of their maker spirit, the utilization of maker spaces, and their academic achievements in maker education14. José Luís Saorin conducted a questionnaire survey involving 44 engineering students from the Universidad de La Laguna in Spain who participated in maker learning activities. The findings indicate that the use of digital design technologies and 3D printing technologies contributes to the development of engineering creativity among these students15. Through interviews with 18 university students engaged in makerspaces and participatory observations within these environments, Wang Xiaoming found that university makerspaces significantly enhance students’ innovative awareness, thinking, and practical capabilities16. Chen Peng conducted a questionnaire survey involving 632 engineering students in China to explore their experiences with maker learning activities. The results revealed that these students exhibited a high level of engagement and satisfactory satisfaction with maker learning. However, the creativity and social aspects of the activities were relatively underdeveloped17. Relevant studies both domestically and internationally have demonstrated that maker learning activities positively and significantly influence the learning outcomes of engineering students.
Concept definitions
The term ‘learning experiences’ for university students refers to the experiences they have during their time at school. Lu Genshu posits that the interaction between students’ engagement in learning and development activities and their perceived learning environment constitutes their overall learning experience18. This study defines the maker learning experiences of engineering students as the interaction between their engagement in maker learning, practice and development activities and their perceived maker learning environment, which forms their overall maker learning experience19. The engineering students referred to in this study are mainly undergraduate and postgraduate engineering students. Maker learning is defined as a new learning model centred on maker projects that address real-world problems; effectively integrate scientific theories, technical means, and practical experiences in maker spaces; optimise various learning resources such as natural, economic, social, and knowledge resources based on the entire life cycle of artificial products; and continuously engage in embodied experiences, collaborative sharing, innovative practices, knowledge construction, application, and innovation through scientific research, technology integration, engineering construction, industrial services, and cultural inheritance and innovation, ultimately developing the core competencies of maker learners17.
Questionnaire design19
Theoretical framework
The purpose of the survey of engineering students’ maker learning experiences is to understand their current situation and to provide a basis for the scientific construction of maker learning models in the context of new engineering education reform and new engineering disciplines. The subjects of this study are full-time undergraduate and graduate students in engineering disciplines who have genuine maker learning experiences. The study primarily examines engineering students’ interactions with their educational environment, their engagement in maker learning and social activities, and their development and learning outcomes during maker learning activities. Engineering students’ learning experiences consist primarily of maker academic engagement, maker learning environment, and maker learning outcomes. This study is grounded in learning engagement theory and uses Alexander Astin’s Input-Environment-Outcome (I-E-O) model as an analytical framework. In addition, it incorporates the Seven Principles for Good Practice in Undergraduate Education and the American Research Council and Hewlett Foundation’s Deep Learning Capability framework into the questionnaire indicators, providing the theoretical framework for the study of engineering students’ maker learning experiences, as shown in Fig. 1.
Theoretical framework of the questionnaire.
Research hypotheses
This study proposes the following three research hypotheses:1. Maker learning experiences differ based on students’ background variables, including gender, family social background, type of university, academic performance, technological practice experience, and maker project learning experience.2.The overall ability development and perceived satisfaction with maker learning among engineering students are positively correlated with their academic engagement in maker activities and their perceived maker learning environment.3.The overall ability development of engineering students is positively correlated with their perceived satisfaction with maker learning.
Indicator system
Based on the definition of maker learning experiences, the maker learning experiences of engineering students are operationalized into three dimensions: maker academic engagement, maker learning environment, and maker learning outcomes. The variables in the questionnaire on engineering students’ maker learning experiences are shown in Table 1, where the “input” variables represent student background characteristics, the “process” variables signify academic engagement in maker activities (maker project learning, maker academic engagement, and maker learning environment), and the “output” variables denote the students’ maker learning outcomes (academic performance, maker learning achievements, overall ability development, and satisfaction with maker learning). This questionnaire focuses on the maker learning activities of engineering students within maker learning environments, emphasizing the extent of students’ engagement in these activities and their self-evaluation of the learning outcomes. According to the purpose of this study, samples of engineering students who have had at least one genuine maker learning experience will be considered valid samples.
Analysis of maker learning experiences among mechanical engineering students
This section presents an empirical analysis of the maker learning experiences of 308 mechanical engineering students from various universities in China, including 40 from first-class universities, 47 from first-class discipline universities, 170 from non-“Double First Class” undergraduate colleges, and 51 from vocational colleges. The analysis focuses on three main aspects: First, it examines the characteristics of maker learning experiences among mechanical engineering students. Second, it conducts a comparative analysis of maker learning experiences across different background variables. Third, it performs a correlation analysis between academic engagement in maker activities, perceived learning environment, and overall ability development and satisfaction with maker learning.
Characteristic analysis of maker learning experiences among university students majoring in mechanical engineering
Analysis of the characteristics of maker project learning
The overall situation regarding the academic engagement of mechanical engineering students in maker projects includes student basic background characteristic variables, maker project learning engagement variables, and maker learning outcome variables. The student basic background characteristic variables include gender, major, year of enrolment, type of university, academic performance ranking, and parents’ education level and occupation. The maker project learning within the student maker learning engagement variables primarily includes motivations for maker learning, frequency of design and production experiences, time invested in maker learning, locations of maker activities, types of maker projects, sources of project topics and funding, completion of academic engagement experiences, participation in technology competitions, and involvement in technology clubs. Descriptive statistical analysis of the maker project learning variables shows that:
1. The main motivations for Maker Learning among students are “personal interest (88.96%), improving innovative thinking and practical skills (68.83%), participating in university technology competitions (64.29%), gaining research/engineering experience (62.66%), gaining credits/certificates/awards (55.52%), improving teamwork and communication skills (52.6%), and improving product design and development skills (50.32%)”, as shown in Fig. 2.
Descriptive statistical analysis results of maker learning motivation among college students majoring in mechanical engineering.
2. The time when students first hosted or participated in the design and production of real projects is predominantly in ‘third semester (25%)’ and ‘second semester (23.38%)’, followed by ‘first semester (14.61%)’ and ‘fourth semester (14.29%)’, with less participation in ‘fifth semester (6.82%)’, ‘sixth semester (2.6%)’ and ‘seventh semester onwards (0.97%)’, as shown in Fig. 3.
Descriptive statistical analysis results of the time when mechanical engineering majors first host or participate in the completion of real work design and production.
The total time spent in maker learning is mainly ‘less than six months (35.39%)’ and ‘six months to less than one year (27.92%)’, followed by ‘one to less than two years (19.16%)’ and ‘two to less than three years (11.04%)’, as shown in Fig. 4. Average weekly participation in maker learning activities is mainly ‘0 to 5 hours (34.74%)’ and ‘6 to 10 hours (24.03%)’, as shown in Fig. 5.
Descriptive statistical analysis of the total time spent by mechanical engineering majors on maker learning.
Descriptive statistical analysis of the average weekly participation time of mechanical engineering majors in maker learning activities.
3. A total of 90.26% of students have complete experiences in creativity, conception, design and production of real projects, with the total number of complete maker learning experiences mainly at ‘once (33.77%)’, ‘twice (28.9%)’ and ‘three times (12.99%)’, followed by ‘four times (10.06%)’ and ‘five times (4.55%)’, as shown in Fig. 6.
Descriptive statistical analysis results of the total number of complete maker learning experiences for college students majoring in mechanical engineering.
4. The main locations for maker learning among students are ‘open labs/workshops (57.14%)’, ‘dormitories (40.91%)’, ‘maker spaces/coworking spaces (35.39%)’, and ‘engineering training/engineering practice centres (34.42%)’, as shown in Fig. 7.
Descriptive statistical analysis results of maker learning places for college students majoring in mechanical engineering.
5. The types of in-class maker projects mainly include ‘innovative experimental projects(46.75%)’, ‘engineering education projects (37.01%)’ and ‘comprehensive design practice projects (27.6%)’, as shown in Fig. 8.
Descriptive statistical analysis results of activity types in maker learning projects for mechanical engineering majors.
6. The types of extracurricular maker projects mainly include “university technology competition projects (72.08%), university innovation and entrepreneurship training projects (53.9%)” and “extracurricular technology innovation projects (23.7%)”, followed by “university research training projects (17.86%)”, as shown in Fig. 9.
Descriptive statistical analysis results of extracurricular maker learning project types for mechanical engineering majors.
7. The types of activities in Maker projects focus on ‘product design’ (57.47%), ‘invention and creation’ (51.95%), followed by ‘entrepreneurship training’ (15.91%), ‘scientific experimentation’ (11.36%) and ‘business practice’ (11.69%), with less emphasis on ‘software development’ (7.79%), ‘social survey’ (6.17%) and ‘social services’ (4.22%), as shown in Fig. 10.
Descriptive statistical analysis results of activity types in maker learning projects for college students majoring in mechanical engineering.
8. The sources of project topics come mainly from “Competition topics (74.03%)” and “Teacher recommendations (55.84%)”, followed by “Self-selection (38.31%)” and “Team consensus (31.49%)”, and less from “Senior recommendations (10.39%)” and “Company commissions (2.6%)”, as shown in Fig. 11.
Descriptive statistical analysis of the sources of topic selection for maker learning programs for mechanical engineering majors.
9. Tasks associated with maker projects mainly include “designing solutions (67.86%)”, “producing physical items (56.82%)” and “drawing plans (55.52%)”, followed by “topic selection (37.66%)”, “project research (36. 36%), ‘testing and improving’ (35.39%), ‘writing reports’ (30.19%), ‘defending the project’ (26.62%), ‘presenting the project’ (26.3%), ‘filing patents’ (24.35%), ‘programming’ (20.45%), ‘project management’ (17.86%) and ‘writing a thesis’ (15.91%), as shown in Fig. 12.
Descriptive statistical analysis results of maker learning project tasks for college students majoring in mechanical engineering.
10. The main sources of funding for maker projects are ‘school funding (69.16%)’ and ‘departmental funding (45.13%)’, followed by ‘teacher funding (27.6%)’ and ‘self-raised funds (20.13%)’, with minimal support from ‘company funding (1.95%)’, as shown in Fig. 13.
Descriptive statistical analysis results of funding sources for maker learning programs for mechanical engineering majors.
11. The time when students first participated in technology clubs is mainly in ‘first semester (40.91%)’, ‘second semester (18.83%)’ and ‘third semester (16.88%)’, while the total time of participation is mainly in ‘less than six months (28.9%)’, ‘six months to less than one year (24.68%)’ and ‘one to less than two years (21.1%)’, as shown in Fig. 14.
Descriptive statistical analysis results of the first participation time of college students majoring in mechanical engineering in technology clubs.
12. A total of 66.88%, 51.62% and 5.84% of students have participated in provincial, national and international competitions respectively. Among the national competitions, the most common are the “National College Student Mechanical Innovation Design Competition” (41.23%), the “National 3D Digital Innovation Design Competition” (31.17%), and the “’Challenge Cup’ National College Student Extracurricular Academic and Technological Works Competition” (20.13%). In addition, 70.78%, 53.57%, and 40.26% of students participated in innovation and entrepreneurship/research training projects at the university, provincial, and national levels, respectively.
Analysis of the characteristics of maker learning outcomes
The maker learning outcomes within the variables of maker learning gains for mechanical engineering majors include maker learning outcomes, number of design and production works, number of papers/reports/patent descriptions written, self-evaluation of design and production works, number of design drawings produced, number of patents applied for/granted, number of technology competition awards won and number of scholarships received. Data from the survey on maker learning outcomes of mechanical engineering students show that:
The most important learning outcomes for makers are ‘physical models’ (66.88%), ‘design drawings’ (51.95%), ‘design descriptions/design reports’ (40.26%), ‘physical prototypes’ (38.64%), ‘research proposals/design proposals’ (36.69%) and ‘technology competition award certificates’ (42.21%), followed by ‘patent certificates’ (28.25%), ‘software/programs’ (23. 7%), ‘development reports’ (14.94%), ‘business plans/start-up reports’ (11.04%), ‘experimental equipment/instruments’ (10.71%), ‘survey reports’ (9.74%), ‘academic papers’ (8.77%), ‘experimental reports’ (8.44%) and ‘research reports’ (7.79%), with very low representation for ‘products’ (1.62%), ‘academic works’ (1.62%) and ‘business licences’ (0.65%).
A total of 73.05% of students have hosted the design and production of works, while 88.64% of mechanical engineering students have participated in the design and production of works. The proportions of students hosting the design and production of one, two, three and four or more works are 40.91%, 18.51%, 7.79% and 5.85% respectively. The proportions of students involved in the design and production of one work are 37.34%, 24.35%, 15.58% and 11.37% respectively.
The proportions of students producing between 1 and 20 drawings, 11–20 drawings, 21–40 drawings, and 41 or more drawings are 39.94%, 18.83%, 11.36%, and 14.93%, respectively.
Only 45.13% and 30.52% of students have applied for utility model and invention patents, respectively, while those who obtained utility model and invention patents represent 31.49% and 14.29%, respectively.
The proportions of students winning awards in provincial, national, and international university technology competitions are 62.99%, 46.10%, and 3.90%, respectively. The proportions of mechanical engineering students receiving national-level and university-level scholarships are 6.17% and 54.87%.
Characteristics of academic participation in maker activities
The overall situation regarding academic participation in Maker activities among mechanical engineering students is shown in Table 2, with an average score of 4.068. Among the different factors, the average score for student-teacher interaction and collaboration is the highest at 4.286, followed by proactive learning at 4.242. Critical creativity (4.053) and application of knowledge (3.903) follow in third and fourth positions, respectively. Communication and collaboration (3.854) received the lowest average score. Table 2 reveals that mechanical engineering students’ academic participation in maker learning is, overall, relatively high. Cluster analysis reveals the presence of three distinct groups: students with high, average, and low levels of academic participation in maker activities, comprising 101, 122, and 85 students, respectively. These figures represent 32.79%, 39.61%, and 27.60% of the total sample surveyed.
Characteristics of the maker learning environment
The aggregate perceptions of the maker learning environment among mechanical engineering students are delineated in Table 3. The mean score for the maker learning environment is 4.705, with the highest mean score being 4.764 for the innovation space factor, followed by 4.712 for effective guidance. The lowest mean score is 4.64 for institutional support. Mechanical engineering students’ overall perception of the maker learning environment is positive, as shown in Table 3. The application of cluster analysis has led to the identification of three distinct groups based on their perceptions of the maker learning environment. The first group, comprising 72 students, exhibits a high perception of the environment. The second group, with 140 students, demonstrates a medium perception. The third group, consisting of 96 students, exhibits a low perception. These three groups collectively represent 23.38%, 45.45%, and 31.17% of the surveyed sample, respectively.
Characteristics of overall skill development
The overall situation regarding the perceived development of comprehensive ability among the mechanical engineering students participating in maker learning is shown in Table 4; Fig. 15. The overall average score for overall ability development is 4.172. Among the different factors, the highest average score is for Autonomous learning and lifelong learning ability with 4.357, followed by Technology and ability to use modern tools with 4.338. The lowest average score is for research and experimental skills at 4.039. Table 4 shows that, overall, the perceived development of generic ability among engineering students involved in Maker Learning is good.
Overall situation of comprehensive ability development.
Analysis of engineering students’ perceived satisfaction with maker learning
The overall situation regarding mechanical engineering students’ perceived satisfaction with maker learning is presented in Table 5; Fig. 16. From Table 5 it can be seen that the average level of satisfaction among engineering students is 4.746. Among the different factors, the highest average is for the learning gains factor with 4.825, followed by teacher support and learning experience with 4.782. The lowest average is for the environmental support factor with 4.575. Overall, this suggests that mechanical engineering students perceive their satisfaction with maker learning positively.
Overall satisfaction of maker learning.
Comparative analysis of maker learning experiences among mechanical engineering majors in higher education institutions
To analyse the differences in maker learning experiences among mechanical engineering students with different background characteristics, this study examines ten factors across four dimensions: academic engagement in maker activities (communication and collaboration, student-teacher interaction, proactive learning, critical creativity, knowledge application), perceived maker learning environment (innovation space, effective guidance, institutional support), perceived overall skill development, and learning satisfaction.
Results from independent samples t-tests indicate that there are no significant differences across the ten factors based on gender, ethnicity, or previous experience in technology innovation clubs or project design in middle/high school. However, there are significant differences in the critical creativity factor for those who have held student leadership positions. In addition, students with different experiences in technology innovation activities during middle/high school show significant differences in proactive learning, critical creativity, and satisfaction with maker learning.
One-way ANOVA results show that family economic status and total time spent in technology clubs do not show significant differences across the ten factors. In contrast, university academic rank shows significant differences in the communication and collaboration and proactive learning factors. The type of university attended leads to significant differences in the knowledge application factor, while the location of the family home shows significant differences in the critical creativity factor. The educational level of the parents influences the factor of proactive learning, and the father’s occupation influences the factors of communication and cooperation and satisfaction. The mother’s occupation shows significant differences in the institutional support factor.
Further analysis using one-way ANOVA shows that the total number of complete experiences with real projects shows significant differences across seven factors in academic engagement, comprehensive skills development and learning satisfaction. Total time spent on maker learning and total time spent in technology clubs also show significant differences across nine factors, including academic engagement, innovation space, effective guidance, comprehensive skills development and learning satisfaction. Weekly participation time in maker activities shows significant differences across all ten factors. Lack of funding for maker projects correlates with significant differences in communication and collaboration, student-teacher interaction, critical creativity, knowledge application, comprehensive skill development and learning satisfaction across six factors. The time of first participation in technology clubs shows significant differences in communication and collaboration, student-teacher interaction, critical creativity, knowledge application, innovation space, effective guidance, comprehensive skill development and learning satisfaction across eight factors. Total number of design drawings and participation in design and production show significant differences across eight factors in academic engagement, innovation space, effective guidance and comprehensive ability development. Hosting design and production activities shows significant differences across four factors: communication and collaboration, proactive learning, knowledge application and comprehensive skills development.
Correlation analysis between maker learning engagement and maker learning gains among mechanical engineering majors in university
The correlation analysis between engagement in maker learning and the gains of maker activities among mechanical engineering students (Table 6) shows a significant positive correlation between academic engagement in maker activities, perceived maker learning environment, perceived overall skill development and learning satisfaction. This suggests that greater investment in maker learning is correlated with better overall skill development and learning satisfaction.
The correlation analysis between maker project learning and gains (Table 7) shows that the number of provincial competition entries and weekly hours spent on maker learning have significant positive correlations with communication and collaboration, student-teacher interaction, proactive learning, critical creativity, knowledge application, innovation space, effective guidance, institutional support, comprehensive skill development and learning satisfaction. Total time spent on maker learning and technology clubs is not significantly correlated with institutional support. Participation in national competitions shows significant positive correlations with communication and collaboration, knowledge application, innovation space, effective guidance, comprehensive skills development and learning satisfaction. The number of design and production activities organised is not significantly correlated with effective guidance, institutional support or learning satisfaction. Similarly, participation in design and production is not significantly correlated with institutional support or learning satisfaction. The total number of design drawings is also not significantly correlated with innovation space, institutional support or learning satisfaction.
link
