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Article

Linking Making and Creating: The Role of Emotional and Cognitive Engagement in Maker Education

by
Yafei Shi
1,
Qi Cheng
1,
Yantao Wei
2,* and
Yunzhen Liang
1
1
Faculty of Education, Henan Normal University, Xinxiang 453007, China
2
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11018; https://doi.org/10.3390/su151411018
Submission received: 22 May 2023 / Revised: 2 July 2023 / Accepted: 11 July 2023 / Published: 14 July 2023
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Abstract

:
Learning through making enhances the learning experience and effectively develops learners’ creativity. In this regard, maker education has been extensively incorporated into school education as a vital learning environment. However, less is known about how making advances students’ creativity in maker education. Therefore, this study aimed to explore relationships among making, tinkering, and creating. A total of 372 middle school students enrolled in maker courses through the 2021–2022 school year were surveyed. They completed a questionnaire concerning learning engagement, individual creativity, collective creativity, and learning motivation. Structural equation modeling (SEM) was employed to test the hypotheses proposed in this study. The results revealed significant relationships among learning engagement, individual creativity, and collective creativity. Specifically, behavioral engagement positively predicted emotional and cognitive engagement. Moreover, both emotional and cognitive engagement positively related to collective creativity, while only the effect of cognitive engagement on individual creativity was observed. The direct effects of behavioral engagement on individual and collective creativity were not significant. Furthermore, emotional and cognitive engagement mediated the association between behavioral engagement and collective creativity, while only cognitive engagement played a mediating role between behavioral engagement and individual creativity. In addition, the moderating analysis showed that learning motivation moderated the association between behavioral engagement and emotional engagement in school-maker education. Some implications for integrating maker education into school education and cultivating students’ creativity were discussed.

1. Introduction

Creativity is regarded as an essential competence for careers in today’s society [1]. Exiting research has shown that maker education helps students to develop a variety of skills and competencies, including critical thinking, creativity, communication, and collaboration [2]. Maker education refers to a type of learning activity that encourages students to engage in science, technology, engineering, and mathematics and thereafter to develop their problem solving and creativity [3,4]. The European-funded MAKE-IT project has proved the significant effect of maker education on social, economic, and environmental sustainability. Grounded on this, maker education has garnered a lot of attention worldwide [3,4]. Given the value of maker education, a lot of researchers and educators have started thinking about how to integrate it into school education [5]. For instance, Chou found that integrating maker education into elementary school could significantly improve students’ problem-solving skills [6].
However, directly integrating the maker model into school education without cultivation of thinking results in many weaknesses in school-maker education. Although the main purpose of maker education is to foster students’ creativity and practical thinking [7], making is becoming an activity that students follow, which involves imitating teachers and runs counter to innovation-based exploration [8]. Making, to some extent, can bring students a sense of accomplishment, but the aim of maker courses is not just to finish products and operate the equipment [9]. Some researchers have found and pointed out these problems. For instance, a study conducted by Valente and Blikstein observed that students in makerspace activities paid more attention to product construction rather than engaging in knowledge construction [10]. Godhe et al. further suggested that maker education needs to understand the learning context rather than simply import devices, applications, and practice into schools and classrooms [11], though the devices and tools in the makerspace helped students participate in the activities [12]. However, making is more preoccupied with the use of tools and equipment but less concerned about the construction of basic knowledge [13].
A question about the role of making in maker education is whether youth can learn through making [14]. To improve the quality of learning, many researchers have redesigned the maker education model to improve students’ learning performance. For instance, Hung, Gao, and Lin came up with a maker education model based on social design, which focused on the design-thinking process and brought students a sense of accomplishment [9]. This indicated that making maybe influence learning through thinking. Therefore, the present study aims at exploring whether and how students’ creativity could be boosted by practical activities in maker education.
Some educators are concerned about how to deal with the relationship between making and conceptual understandings [15]. For instance, Dewey thought that knowledge and experience were inseparable [16]. However, it is unclear how to apply Dewey’s idea of learning by doing to contemporary school-maker education [11]. Generally, the way of knowing includes making, tinkering, and engineering in school-maker education [17]. Honey and Kanter claimed that making could be considered “project-based learning” or “hands-on learning” [18]. In maker activities, however, students make something, but the learning experience is limited [8]. Therefore, educators should guide children to understand knowledge by constructing tools for themselves rather than just accepting ready-made things [19].
A tool-centric approach to integrating making into education will certainly fail, as it neglects the critical elements of mindset [14]. Much of the literature regards tinkering as a mindset and activity that builds on learners’ prior interests and knowledge [20]. Tinkering is referred to a process in which a person generates ideas, which is conducive to improving problem-solving ability and creativity [21]. In addition, thinking and creativity are closely aligned with tinkering, which is essential to future success [8]. When people are tinkering, they try, modify, and create new possibilities [8]. Fostering the maker mindset through education is a fundamental human project—to support the growth and development of one person not just physically but mentally and emotionally [18]. Therefore, students’ learning depends on experience and how they pursue interests and ideas through tinkering activities [20]. However, most of the studies about makers just focus on how to improve students’ making [13]. There is limited evidence about the role of the internal mindset process in influencing students’ creativity and performance in maker education. Furthermore, tinkering emphasizes playful and strategic production [8,17], which is part of emotional and cognitive engagement. However, very few studies focus on cognitive and emotional engagement in maker education.
Engineering is regarded as creative inventing [17]. Creativity is identified as a behavior to develop new things in the form of a physical, psychological, or emotional construct [22]. Therefore, it is crucial to investigate the relationships among behavior, psychology, and emotion in maker education. In the present study, maker education can be described as three processes: making, tinkering, and creating. Specifically, making focuses on practical behavior with tools and materials [17]. To be specific, practice activities should be designed by instructors in class to improve behavioral engagement. Tinkering refers to internal psychological factors, including two aspects: emotional and cognitive engagement. Creating refers to the ability to extract principles based on a theoretical foundation, emotional participation, and practical behavior. Many previous studies in maker education centered on the first process. This study stresses the exploration of the mechanism between making, tinkering, and creating. It sheds light on the effect of making and thinking on students’ creating ability and has some implications for instructors and researchers when cultivating students’ creativity in school-maker education.

2. Theoretical Framework and Model Development

2.1. Maker Education

Maker education initiated the development of the maker movement [5]. Students who participate in the process are called makers in a classroom or makerspace [4]. The maker movement originated and expanded in the United States [23]. Specifically, in 2005, the publication of the magazine MAKE promoted the development of the maker movement [5,13]. In 2006, the Maker Fair exposition was held in the San Francisco Bay Area, which indicated that the maker movement had begun [13,23]. During the movement, people of different ages were willing to join and engage in making and sharing the product with others [3], which encouraged the confidence and enthusiasm of the maker.
With the development of the maker movement, the value of maker education motivated a lot of researchers and educators to think about how to integrate it into school education [5]. For instance, Taylor suggested that it was essential and effective to adopt the style of maker learning into K-12 classrooms after evaluating the value of integrating the maker movement into K-12 education [24]. However, several problems and challenges still exist in this process [25]. First, makers in outside schools collaborate and create through a group that the members are experienced, but inside school-maker education, teachers are the authority and center of a class [11]. Therefore, teachers in maker education need to be knowledgeable and have comprehensive abilities [4]. Second, to stimulate students to be involved in making activities, many universities enlarge the makerspace by adding equipment [4]. While it is necessary to increase the number of tools in school makerspace, more attention should be paid to the process and creation rather than to tools [3]. Third, the design of school-maker education activities focuses on making but ignores tinkering. The activities in school-maker education should be designed by teachers to meet the real need of learners. In other words, students’ needs should be considered in maker education when instructors design activities [7].
Different researchers think differently about learning through making. Some researchers indicate that making-centered learning activities can be used to promote learning directly. For instance, Wardrip and Brahms suggested that learning in maker education could be tracked and evaluated by learning itself [12]. In contrast, other researchers claimed that hands-on and based-project learning in the makerspace could develop the mindset of students [4], which is conducive to the cultivation of students’ creativity. For instance, Huang et al. emphasized that learning is be achieved by not just making but knowledge constructs and logical analysis [7]. However, few studies have explored the mechanism underlying making, tinkering, and creating in maker education.

2.2. Individual Creativity and Collective Creativity

The progress of civilization depends on creativity [26]. Although there is no accurate definition of creativity, innovation and value are common themes expressed by researchers [26]. For instance, Young suggested that creativity refers to the process of making something novel and valuable [27]. Hennessey and Amabile considered that creativity is innovation in regard to products, ideas, and problem solving. Much of the research about creativity has focused on individual creativity and collective creativity [26]. For instance, Lucchiari, Sala, and Vanutelli conducted experimental research to evaluate the level of individuals’ creativity at an Italian primary school, though it was challenging to define [28]. Parjanen and Hyypiä investigated whether individuals’ collective creativity would be improved after participating in games and confirmed that there was an interactive relationship between individual creativity and collective creativity [29]. Tadmor, Satterstrom, Jang, and Polzer demonstrated that multicultural experience could effectively enhance the level of collective creativity [30]. In the present study, individual creativity refers to the innovative idea generated in the process of experience and practice [31]. It can be influenced by peer support and social interaction throughout the work process [31]. In addition, individual creativity can also be impacted by collective creativity [32]. Collective creativity can be defined as the process leading to creative ideas and products that are the results of a series of interactions and collaboration in a group [32]. Collective creativity is not just the total of individual creativities but rather enhancing them when facing a common challenge [32]. Most research has focused on collective creativity in the context of STEAM activities [33]. However, research has not sufficiently explored how collective creativity is developed in maker education. Therefore, the present study explored the antecedents of individuals’ and collective creativity and the influencing mechanisms between them in the maker education context.

2.3. The Mediating Role of Emotional and Cognitive Engagement

Learning engagement is generally regarded as a multidimensional construct, including behavioral, emotional, and cognitive components [34]. There is a growing interest in learning engagement because of its contribution to students’ academic performance [35,36]. In general, behavioral engagement is considered to be an observed behavior or performance [36,37,38], which is an external process. On the contrary, emotional engagement and cognitive engagement are regarded as psychological engagement, which is an internal process [36,39]. Different dimensions of learning engagement have different functions in the learning process. Behavioral engagement stresses the confirmation of classroom and school rules and actively participating in classroom and school activities, which is beneficial for the improvement in academic achievements [34,40]. Emotional engagement refers to the affective responses from the school community, which is conducive to academic outcomes [34,41]. Cognitive engagement is regarded as deep-learning strategies and self-regulated learning, which directly impact the process of learning, understanding, and mastering knowledge and improve one’s problem-solving ability [34]. Self-regulated learning is identified as the highest form of cognitive engagement [42], which is beneficial for thoughtful problem-solving [34].
The relationships among the three types of engagement are internal and complex [41]. Evidence has shown that the three dimensions of school engagement can be mutually reinforced [37]. For instance, Li and Lerner analyzed three-year longitudinal data from 1029 high school students and found that both emotional and cognitive engagement could be predicted significantly by behavioral engagement [43]. That is, the higher the level of students’ behavioral engagement, the higher their emotional and cognitive levels were [37]. However, few studies have explored the internal mechanism regarding how school engagement improves students’ performance in maker education. Therefore, the present study intended to clarify the mechanism regarding behavioral engagement, emotional engagement, and cognitive engagement in the maker education learning process. Specifically, as is shown in Figure 1, this study classified making as behavioral engagement, tinkering as emotional and cognitive engagement, and creating as individual and collective creativity.
Evidence has shown that learning engagement provides a significant contribution to creativity [44]. For instance, some researchers have suggested that emotional and cognitive engagement have dual actions on creation [39]. Guan, Wang, Chen, Jin, and Hwang found that the processes, including observation, doing, and reflection, contributed to students’ creativity performance, which indicated that learning engagement, including behavioral, emotional, and cognitive engagement, positively impacts creativity [45]. Furthermore, Bowden proposed that the concept of engagement produces positive impacts at both individual and group levels [39]. To date, however, creativity within individual and collective levels remains largely unexplored in maker education. Specifically, it is unclear how the internal mechanism of learning engagement will affect individual and collective creativity. This research offers valuable insights into how to promote both individual and collective creativity through learning engagement in maker education.

2.4. The Moderating Role of Learning Motivation

Motivation is identified as an internal state that stimulates and maintains behavior, which can be divided into two types: intrinsic and extrinsic motivation [46]. Many theories attempt to explain motivation from diverse perspectives. For instance, self-determination theory (SDT) contends that motivation originates from different types of needs for self-determination (e.g., the need for autonomy) [47]. Social cognitive theory (SCT) explains that motivation refers to stimulating and maintaining goal-oriented activities [48]. Expectancy value theory holds that motivation is determined by the expectation of the possibility of success of the task and the value entrusted to the task [49]. Learning motivation is part of motivation [50]. Learning motivation is affected by a series of factors, such as internal psychosocial factors, external social interaction, and the social environment [51]. In addition, it can be used to explain the reasons why students engage in academic tasks [52]. Therefore, the research and theory of motivation can be applied to all kinds of instructional contexts to advance students’ learning [52].
Many researchers have attempted to improve the performance of students by integrating motivation into their classes because motivation can predict achievement directly or indirectly. Existing research suggests that motivation is curvilinearly related to emotional and cognitive engagement. For instance, motivation is conducive to achievement by increasing the quality of cognitive engagement [53]. In addition, Guan et al. found that behavioral engagement can promote motivation and emotional engagement based on VR comparative experiments [45]. Therefore, motivation plays an important role in learning and teaching. It is necessary for instructors to apply the motivation theory to education. The present study aims to explore how to enhance students’ learning engagement by motivating them.
In addition, motivation is an essential component to motivating and engaging students in the learning process [54]. In terms of emotional engagement, students with high motivation and interest are willing to try to learn and understand learning material [55]. Furthermore, students’ motivation could influence their academic achievement by affecting their emotions [56]. In terms of cognitive engagement, motivation could lead to students’ achievement by improving the quality of cognitive engagement [53]. Furthermore, students’ engagement is also regulated by their motivation. For instance, Skinner and Belmont proposed that motivation could increase students’ active participation in those who have high behavioral engagement, whereas students with lower behavioral engagement would be negatively affected by motivation [57]. Therefore, this study posited that motivation would moderate the association between behavioral engagement and emotional engagement and the relationship between behavioral engagement and cognitive engagement.

2.5. The Current Study

The previous study has shown that there are significant relationships between learning engagement, creativity, and motivation [45,53]. This study aims to investigate the relationships among them in school-maker education. The research model in this study is shown in Figure 2. The following hypotheses are posited:
Hypothesis 1. 
Behavioral engagement has a direct effect on emotional engagement (H1a), cognitive engagement (H1b), individual creativity (H1c), and collective creativity (H1d).
Hypothesis 2. 
Emotional engagement has a direct effect on individual creativity (H2a) and collective creativity (H2b).
Hypothesis 3. 
Cognitive engagement has a direct effect on individual creativity (H3a) and collective creativity (H3b).
Hypothesis 4. 
Emotional (H4a) and cognitive engagement (H4b) have mediating effects on the association between behavioral engagement and individual creativity.
Hypothesis 5. 
Emotional (H5a) and cognitive engagement (H5b) have mediating effects on the association between behavioral engagement and collective creativity.
Hypothesis 6. 
Learning motivation has a moderating effect on the relationship between behavioral engagement and emotional engagement (H6).
Hypothesis 7. 
Learning motivation has a moderating effect on the relationship between behavioral engagement and cognitive engagement (H7).

3. Method

3.1. Participants and Sampling

The participants for this cohort were recruited from a middle school in Zhengzhou, China, which was equipped with all kinds of teaching instruments and learning tools, such as desks, robots, and computers. A real screen was captured and shown in Figure 3.
This school was specifically chosen as it is among the few schools that implemented maker education normatively. Three hundred seventy-two students volunteered to take part in the survey. The participants were asked to complete the questionnaire about maker education. Thirty-two observations were excluded because of perfunctory responses, where the students failed to complete the survey seriously. The final sample was 340, and among them, 169 were male and 171 were female. All of the participants were aged between 12 and 17 years old (mean age = 13.521 years, s.d. = 0.556). As is shown in Table 1, to identify the demographic statistics of participants, the following parameters were used: gender, age, interest, and computer learning time. The students chosen in the current study have extensive previous experience with maker curriculum.

3.2. Instruments

The questionnaire used in this study was designed to measure the following constructs: behavioral engagement, emotional engagement, cognitive engagement, learning motivation, individual creativity, and collective creativity. The original survey scales were modified to fit into a specific context by adding the background information of maker education. Using a 5-point Likert scale, participants were asked to complete the survey according to their real feelings about maker education.
To capture students’ enthusiasm for learning, we used seven items from the learning motivation questionnaire. The learning motivation scale used in this study was adapted from Learning Motivation by Hwang [58]. Respondents were asked to rate all items on a five-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The Cronbach’s alpha (α) of this scale was 0.955.
The final scale of the learning engagement includes three subscales: behavioral engagement, emotional engagement, and cognitive engagement. Behavioral engagement and emotional engagement scales were, respectively, adapted from Behavioral Engagement and Emotional Engagement scales developed by Skinner, Furrer, Marchand, and Kindermann [59]. The behavioral engagement was assessed using 5 items to examine the classroom behaviors of students during learning activities (α = 0.934). The emotional engagement was measured using 6 items that tapped emotions indicating students’ motivated participation in maker education (α = 0.959). The cognitive engagement scale was adapted from the Deep Strategy Use (DSU) by Greene [60]. Cognitive engagement was assessed using 6 items that aimed to examine the students’ attention while participating in maker education (α = 0.959).
Individual creativity and collective creativity scales were adapted from individual creative behavior and collective creative behavior scales, respectively [61]. Four items of the individual creativity scale and five items of the collective creativity scale were selected to measure individuals’ and groups’ creativity in their maker classrooms. The Cronbach’s alpha values were 0.916 for individual creativity and 0.958 for collective creativity, respectively.

3.3. Data Collection and Analysis

In this study, 372 students volunteered to take part in the survey and complete the questionnaire. Data were collected by the WJX (https://www.wjx.cn/, accessed on 16 September 2022), a professional questionnaire collection tool. The survey was conducted between September and October 2022 in China. A total of 372 questionnaires answered by the students were collected through WJX. Then, the data were imported into Excel, and 32 invalid data were deleted. Then, data management and analysis were performed using SmartPLS 3.0. After confirmatory factor analyses (CFA), structural equation modeling (SEM) was used to evaluate the hypothesized model and to verify the mediating and moderating effects of the related factors.

4. Results

4.1. Assessment of Measurement Model

The measurement model in this study was assessed by item reliability, composite reliability (CR), convergence validity, and discriminant validity. Table 2 displays the results obtained from the preliminary measurement model. Especially, the outer loading value for each item was well above the recommended cut-off value of 0.7 and ranged from 0.787 to 0.943 [62], which indicated that the items of each construct had better internal consistency. The p-values of all items were statistically significant. The composite reliability (CR) value for all constructs was well above the recommended criteria of 0.6 and ranged from 0.941 to 0.968 [63], which indicated that internal consistency was acceptable. All the AVE values exceeded 0.7, meeting the recommended cut-off value of 0.5 [64]. The results indicated that the measurement model had great internal consistency, and the convergent validity of the measurement model was satisfied.
The discriminate validity of these constructs was measured with Fornell–Larcker Criterion and HTMT methods. As is shown in Table 3, all square roots of the AVE values are higher than all the correlation coefficients between each latent construct, which indicates that the discriminate validity in this study is satisfied. As is shown in Table 4, most values of HTMT are less than 0.85, which again indicates the acceptable discriminate validity of the measurement model.

4.2. Assessment of Structural Model

The structural model was assessed using effect size (f2), determination coefficient (R2), path coefficients, VIF, and predictive relevance (Q2) in this study. The recommended cut-off values are effect size (f2) > 0.02 [65], determination coefficient (R2) > 0.19 [66], VIF > 5 [66], and predictive relevance (Q2) > 0. As is shown in Table 5, all the determination coefficient (R2) values were greater than the recommended cut-off value of 0.25 [66] and ranged from 0.545 to 0.714, which indicated that the variance in the endogenous variable could be explained well by the exogenous variables. In addition, all the predictive relevance (Q2) values were greater than 0 [62] and ranged from 0.444 to 0.559, which indicated that the model in this study had great predictive relevance.
As is shown in Table 6, the results of SEM showed that behavioral engagement was positive and significant with emotional engagement (β = 0.423; f2 = 1.955; p < 0.001) and cognitive engagement (β = 0.738; f2 = 1.196; p < 0.001), which indicated that hypotheses H1a and H1b were supported. In addition, emotional engagement had statistically significant effects on collective creativity (β = 0.332; f2 = 0.094; p < 0.001). Thus, hypothesis H2b was supported, but the effect of emotional engagement on individual creativity was not significant (β = 0.032; f2 = 0.001; p = 0.62), which indicated that hypothesis H2a was not supported. Cognitive engagement had a positive effect on individual creativity (β = 0.734; f2 = 0.728; p < 0.001) and collective creativity (β = 0.519; f2 = 0.306; p < 0.001). Thus, hypotheses H3a and H3b were supported. However, behavioral engagement did not have statistically significant effects on individual creativity (β = 0.111; f2 = 0.013; p = 0.10) and collective creativity (β = 0.017; f2 = 0.000; p = 0.86). Therefore, hypotheses H1c and H1d were not supported.

4.3. The Mediation Role of Emotional Engagement and Cognitive Engagement

According to Table 6, behavioral engagement could not directly influence individual creativity and collective creativity, while their effects could be extended through the role of emotional and cognitive engagement. Therefore, the mediating effects of emotional and cognitive engagement were further verified. Especially, behavioral engagement could affect both individual creativity and collective creativity indirectly through cognitive engagement, as posited in H4b and H5b. In addition, behavioral engagement could also influence collective creativity via emotional engagement, which indicated hypothesis H5a was supported. However, the effect of behavioral engagement on individual creativity through emotional engagement was not observed significantly. Thus, hypothesis H4a was not supported. Figure 4 shows the structural model of this study.
As is shown in Table 7, the direct effects of behavioral engagement on individual creativity and collective creativity were 0.02 (p > 0.05, [−0.016, 0.254]) and 0.11 (p > 0.05, [−0.155, 0.192]), respectively. The indirect effects of behavioral engagement on collective creativity with the mediating roles of emotional engagement and cognitive engagement were 0.27 (p < 0.001, [0.108, 0.455]) and 0.38 (p < 0.001, [0.284, 0.473]), respectively. The indirect effects of behavioral engagement on individual creativity with the mediating roles of emotional engagement and cognitive engagement were 0.03 (p > 0.05, [−0.079, 0.128]) and 0.54 (p < 0.001, [0.452, 0.635]), respectively.

4.4. The Moderation Role of Learning Motivation

As is shown in Figure 5 and Figure 6, this study found that learning motivation moderated the association between behavioral engagement and emotional engagement, which indicated that hypothesis H6 was supported. Specifically, learning motivation could weaken the positive predictive effect of behavioral engagement on emotional engagement. Interestingly, the moderating effect of learning motivation on the association between behavioral engagement and cognitive engagement was not observed significantly. Thus, hypothesis H7 was not supported.
A simple slope analysis is depicted in Figure 7 to interpret the moderating effect of learning motivation. It also showed that learning motivation inhibited the positive association between behavioral engagement and emotional engagement. High-motivation students who are in a low level of behavioral engagement may be more emotionally engaged in maker education. Low-motivation students who are in a low level of behavioral engagement may be less emotionally engaged in maker education. Therefore, instructors are supposed to design classroom activities properly.

5. Discussion

The cultivation of creativity has received ongoing attention in maker education. Learning through making is apt to focus on practical activities. Based on previous research, this study is further devoted to investigating the internal effect and mechanism of behavioral engagement on creativity. The results revealed that behavioral engagement can indirectly affect individual and collective creativity via emotional and cognitive engagement but cannot directly affect individual and collective creativity. It simultaneously incorporated the role of emotional and cognitive engagement as a mediator and learning motivation as a moderator. The findings imply that it is essential for instructors to consider the factors from both emotional and cognitive engagement in future maker education.

5.1. Behavioral Engagement and Individual and Collective Creativity

The results revealed that the direct effects of behavioral engagement on both individual creativity and collective creativity were not significant, which suggested that making without tinkering fails to foster students’ creativity in school-maker education. This result supports, to some extent, the idea of Dreu, Nijstad, and Baas [67], demonstrating that the relationship between behavioral engagement and creativity is established only when certain conditions are met. In addition, Litts suggested that learning requires interest. That is, a person who is interested in making will possess a maker mindset, which is conducive to meaningful learning [68]. In other words, behavioral engagement could indirectly influence the learning of students. Vossoughi and Bevan indicated that making improved students’ learning and development by integrating social emotion into practical activities [15]. Therefore, just paying attention to making cannot help students master knowledge. The reason for this may be that students only imitate the teacher’s behavior and steps but do not reflect on the underlying knowledge behind these behaviors and do not engage in in-depth thinking. Making is not equivalent to simple assembling. Stubborn-making behavior does not cultivate students’ creativity.

5.2. The Mediating Role of Emotional and Cognitive Engagement

Though behavioral engagement had no direct effect on students’ creativity, it yielded significant indirect effects via emotional and cognitive engagement. Emotional and cognitive engagement play an intermediary role between behavioral engagement and individual and collective creativity. Students’ individual and collective creativity could be cultivated by improving the level of emotional and cognitive engagement. For instance, instructors could engage students’ interests in maker classes. Specifically, behavioral engagement has direct and positive effects on both emotional and cognitive engagement. This finding is in line with Li and Lerner, who suggested that emotional engagement and cognitive engagement could be predicted markedly by behavioral engagement [43]. In addition, collective creativity could be positively predicted by emotional engagement and cognitive engagement, which is partially in line with Newton, who reported that the emotion of the group could influence the creativity of group members [69]. In the same vein, Dreu et al. found that high positive affectivity people are generally more creative than others with low positive affectivity [67]. In addition, the results also found that cognitive engagement could directly affect individual creativity, which is partially in line with Kaufman et al., who found that scientific creativity may be due at least in part to cognitive ability [70]. Therefore, practical activities should integrate inquiry activities into maker classes rather than repetitive operations in maker education. These results are partially in line with those of Sun, who found that student engagement positively predicts creativity [71]. In addition, creativity could be facilitated by tinkering, which is consistent in line with those of Newton, who indicated that thought could influence creativity [69]. It is noteworthy that behavioral engagement could indirectly influence creativity through emotional engagement and cognitive engagement, which showed that practical activities are supposed to stimulate individuals’ emotional and cognitive engagement. It was said that the dominant form would be hands-on practice in maker education. Rather, individuals should ponder frequently and find activities inherently interesting and enjoyable to develop their creativity. As is expected, cognitive engagement could positively predict individual creativity and collective creativity. That is, improving the level of students’ cognitive engagement is a crucial prerequisite for cultivating their creativity. Compared with emotional engagement, cognitive engagement has a somewhat different profile: cognitive engagement could influence both individual and collective creativity. Interestingly, emotional engagement has no significant impact on individual creativity but does on collective creativity. The reason for this may be that high-level emotional engagement can effectively boost collaborative learning and interaction. That is, students may be willing to communicate with others when they feel interested and happy. At the same time, compared with emotional engagement, cognitive engagement has a stronger positive influence on collective creativity. To improve students’ creativity, instructors should facilitate their cognitive engagement in class.

5.3. The Moderating Role of Learning Motivation

This study revealed that learning motivation moderated the association between behavioral engagement and emotional engagement. Although numerous pieces of research have proved that motivation has a positive impact on emotional engagement [72], this research found that learning motivation negatively moderated the relationship between behavioral engagement and emotional engagement. Specifically, motivation weakens the positive influence on the association between behavioral engagement and emotional engagement. One possible reason for this may be that students with a high level of learning motivation pay attention to the learning process but lack emotional engagement. This suggests that instructors should not exaggerate the incentive effect of learning motivation. Interestingly, learning motivation cannot moderate the effect of behavioral engagement on cognitive engagement. It is possible that students with high behavioral engagement may already have a higher cognitive engagement, so the moderating effect of learning motivation is unclear.

5.4. Implications

Recent years have witnessed a surge in maker education, which advocates the idea of learning through making and encouraging students to discover and think. Subsequently, many educators and researchers have attempted to implement making activities into formal education [68]. However, how to reform school education through the maker model leaves even the experienced researcher who is new to the field of maker education with the challenge of cultivating students’ creativity. Therefore, this study yields some implications for instructors and researchers in the field of school-maker education to cultivate students’ creativity.
First, as the study findings demonstrate, behavioral engagement alone did not directly influence students’ creativity. The design of practical activities in maker class should be more flexible, which is more conducive to the development of students’ divergent thinking. Specifically, teachers should guide students to explore, not just imitate. Self-exploration could be more effective than teaching by teachers in cultivating students’ creativity. Second, school-maker education should focus on engaging learners’ interests. Sun and Rueda found that situational interest significantly correlated with emotional engagement [73]. Therefore, students’ emotional engagement level would be improved when they are interested. Cognitive engagement seems to play a more important role than emotional engagement because it could facilitate both individual and collective creativity. To this end, school-maker education will be required to help students to think. Third, Students with strong individual motivation should pay attention to improving their emotional experience and promoting the development of collective creativity.
In sum, the results have revealed that learning engagement variables, such as emotional engagement and cognitive engagement, greatly impact students’ creativity and group creativity. Therefore, school-maker education should be concerned with how to deal with the relationships among making, tinkering, and creating. Maker class activities could be designed based on some teaching model, such as problem-solving teaching. Students’ creativity will be developed if they think deeply in school-maker education.

6. Limitations, Future Research, and Conclusions

Several limitations need to be noted regarding the present study. First, a limitation of this study is that the sample size was not sufficiently large. That knowledge can be used to highlight directions for future research. Further research should increase the sample size to ensure the applicability of the conclusion. Second, the participants in this study were from a middle school and had participated in maker class. Therefore, a second broad recommendation is that more maker students should be surveyed. Third, the data were collected by questionnaire. It is recommended that further research should increase the diversity of samples and the forms of surveys. Maker education is a special experience that allows students to develop their comprehensive abilities. Although the current study is based on several limitations, the findings suggest that the present study was designed to determine the effect of learning engagement on students’ creativity in maker education. The second aim of this study was to investigate the moderating effects of learning motivation on the association between behavioral engagement and cognitive and emotional engagement. One of the more significant findings to emerge from this study is that behavioral engagement could indirectly influence students’ creativity through cognitive and emotional engagement. The present findings contribute to our understanding of “learning through making”. Given the results, these conclusions could be used to highlight directions for instructors and participators.

Author Contributions

Conceptualization, Y.S. and Q.C.; methodology, Y.S.; software, Q.C.; validation, Y.W., Y.L. and Q.C.; formal analysis, Y.W.; investigation, Q.C.; resources, Y.S.; data curation, Y.W.; writing—original draft preparation, Y.S.; writing—review and editing, Q.C. and Y.L.; visualization, Y.L.; supervision, Y.W.; project administration, Y.S.; funding acquisition, Y.W., Y.S. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 62277029 and 72004055; the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China, grant numbers 22YJC880061 and 20YJC880100; the National Collaborative Innovation Experimental Base Construction Project for Teacher Development of Central China Normal University, grant number CCNUTEIII-2021-19; and the Special Project of Wuhan Knowledge Innovation, grant number 2022010801010274.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of The Henan Normal University (HNU IRB).

Informed Consent Statement

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

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The relationship of making, thinking, and creating in this research.
Figure 1. The relationship of making, thinking, and creating in this research.
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Figure 2. Research model in this study.
Figure 2. Research model in this study.
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Figure 3. Maker space and students in maker class.
Figure 3. Maker space and students in maker class.
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Figure 4. Structural model of this study. Note: *** p < 0.001.
Figure 4. Structural model of this study. Note: *** p < 0.001.
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Figure 5. The influence of behavioral engagement on emotional engagement: a moderating model of learning motivation. Note: * p < 0.05.
Figure 5. The influence of behavioral engagement on emotional engagement: a moderating model of learning motivation. Note: * p < 0.05.
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Figure 6. The influence of behavioral engagement on cognitive engagement: a moderating model of learning motivation.
Figure 6. The influence of behavioral engagement on cognitive engagement: a moderating model of learning motivation.
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Figure 7. Interaction between behavioral engagement and motivation on emotional engagement.
Figure 7. Interaction between behavioral engagement and motivation on emotional engagement.
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Table
Table 1. The descriptive statistics of the participants.
Table 1. The descriptive statistics of the participants.
ItemsScaleCount(s)Percentage
gendermale16949.71%
female17150.29%
age1230.88%
1316147.35%
1417451.18%
1510.29%
1600.00%
1710.29%
interestROBOT4011.76%
CREATIVE COMPUTING4011.76%
ARTIFICIAL INTELLIGENCE113.24%
3D PRINT3710.88%
SCHOOL VIDEO STATION4112.06%
OTHERS17150.29%
computer learning time<two months5616.47%
<a half year3510.29%
<a year12336.18%
<two years4513.24%
>two years8123.82%
Table
Table 2. Reliability and convergence validity 1.
Table 2. Reliability and convergence validity 1.
Est.MS.E.Tp ValuesCRAVECronbach’s αrho_A
BET1 ← BET0.8950.8940.01948.1560.0000.9510.7950.9340.936
BET2 ← BET0.9200.9190.01182.9770.000
BET3 ← BET0.7870.7870.02235.5890.000
BET4 ← BET0.9260.9250.01276.1040.000
BET5 ← BET0.9210.9210.01181.3250.000
CET1 ← CET0.8830.8820.01849.2450.0000.9670.8290.9590.959
CET2 ← CET0.8850.8850.01655.7030.000
CET3 ← CET0.9300.9300.01088.6330.000
CET4 ← CET0.9290.9290.01280.7290.000
CET5 ← CET0.9360.9350.01091.3950.000
CET6 ← CET0.8990.8980.01753.4670.000
COE1 ← COE0.9360.9360.01096.1560.0000.9680.8570.9580.959
COE2 ← COE0.9430.9420.007127.8030.000
COE3 ← COE0.9290.9290.01184.4110.000
COE4 ← COE0.9330.9330.01095.2520.000
COE5 ← COE0.8860.8860.01849.7130.000
EET1 ← EET0.9110.9110.01949.1260.0000.9680.8590.9590.959
EET2 ← EET0.9520.9520.008124.2930.000
EET3 ← EET0.9400.9400.01093.5660.000
EET4 ← EET0.9350.9350.01275.9720.000
EET5 ← EET0.8950.8940.01751.6400.000
INE1 ← INE0.8720.8710.02043.8180.0000.9410.7990.9160.917
INE2 ← INE0.9100.9090.01368.6050.000
INE3 ← INE0.9130.9130.01368.4150.000
INE4 ← INE0.8800.8790.02240.6960.000
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity; Est. = estimate; M = sample mean; S.E.= standard deviation; T = T statistics; CR = composite reliability; AVE = average variance extracted.
Table
Table 3. Discriminate validity of this study (Fornell–Larcker Criterion) 1.
Table 3. Discriminate validity of this study (Fornell–Larcker Criterion) 1.
ConstructsBETCETCOEEETINE
BET0.891----------------
CET0.7380.910------------
COE0.6690.7800.925--------
EET0.8130.7520.7350.927----
INE0.6790.8400.7640.6740.894
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity.
Table
Table 4. Discriminate validity of this study (HTMT) 1.
Table 4. Discriminate validity of this study (HTMT) 1.
ConstructsBETCETCOEEETINE
BET--------------------
CET0.779----------------
COE0.7070.813------------
EET0.8570.7830.767--------
INE0.7350.8950.8150.719----
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity.
Table
Table 5. Assessment of structural model 1.
Table 5. Assessment of structural model 1.
ConstructsBET223CETCOEEETINE
----BETCETCOEEETINER2Q2
BET----1.1960.0001.9550.013--------
CET--------0.306----0.7280.5450.444
COE--------------------0.6600.559
EET--------0.094----0.0010.6620.562
INE--------------------0.7140.563
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity.
Table
Table 6. Lateral collinearity assessment and hypothesis testing 1.
Table 6. Lateral collinearity assessment and hypothesis testing 1.
Path(O)(STDEV)VIFTp Values
BET → CET0.740.031.0026.970.00
BET → COE0.020.093.320.180.86
BET → EET0.810.041.0022.720.00
BET → INE0.110.073.321.600.11
CET → COE0.520.062.588.050.00
CET → INE0.730.062.5812.880.00
EET → COE0.330.113.473.120.00
EET → INE0.030.073.470.500.62
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity.
Table
Table 7. Mediating effect analysis 1.
Table 7. Mediating effect analysis 1.
PathSignificant Test of HypothesisCI
Std BetaSTDEVT Statisticsp Values2.50%97.50%
Total Effect
BET → COE0.670.0417.160.000.5890.743
BET → INE0.680.0419.510.000.6090.744
Indirect Effect
BET → EET → COE0.270.092.930.000.1080.455
BET → CET → COE0.380.057.820.000.2840.473
BET → EET → INE0.030.050.500.62−0.0790.128
BET → CET → INE0.540.0511.790.000.4520.635
Direct Effect
BET → COE0.020.090.180.86−0.1550.192
BET → INE0.110.071.600.11−0.0160.254
1 BET = behavioral engagement; CET = cognitive engagement; COE = collective creativity; EET = emotional engagement; INE = individual creativity.
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Shi, Y.; Cheng, Q.; Wei, Y.; Liang, Y. Linking Making and Creating: The Role of Emotional and Cognitive Engagement in Maker Education. Sustainability 2023, 15, 11018. https://doi.org/10.3390/su151411018

AMA Style

Shi Y, Cheng Q, Wei Y, Liang Y. Linking Making and Creating: The Role of Emotional and Cognitive Engagement in Maker Education. Sustainability. 2023; 15(14):11018. https://doi.org/10.3390/su151411018

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Shi, Yafei, Qi Cheng, Yantao Wei, and Yunzhen Liang. 2023. "Linking Making and Creating: The Role of Emotional and Cognitive Engagement in Maker Education" Sustainability 15, no. 14: 11018. https://doi.org/10.3390/su151411018

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