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Article

The Effects of Different Patterns of Group Collaborative Learning on Fourth-Grade Students’ Creative Thinking in a Digital Artificial Intelligence Course

by
Xiaoyong Hu
1,2,
Yue Liu
3,
Jie Huang
1 and
Su Mu
1,2,*
1
School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
2
Institute of Artificial Intelligence in Education, South China Normal University, Guangzhou 510631, China
3
School of Education Science, Nanjing Normal University, Nanjing 210097, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12674; https://doi.org/10.3390/su141912674
Submission received: 16 August 2022 / Revised: 27 September 2022 / Accepted: 28 September 2022 / Published: 5 October 2022
(This article belongs to the Special Issue Digital Education for Sustaining Our Society)
Browse Figures
Review Reports Versions Notes

Abstract

:
Digital technology plays a unique role in the cultivation of students’ creative thinking, which helps them solve poorly structured problems with effective and original solutions. This study applied collaborative learning in a digital-technology-supported artificial intelligence (AI) course and aimed to explore the impact of collaborative learning on fourth-grade students’ creative thinking. According to whether a leadership role was assigned by a teacher and a final consensus was built in the group, four patterns of collaborative learning were designed for comparison in order to determine which pattern was more effective for the promotion of students’ creative thinking. In total, 37 fourth-grade students taking part in the study were divided into four groups, and each group adapted one of four patterns of collaborative learning. The Torrance Creative Thinking Test (TTCT-Figure) was used to test the pre- and post-creative thinking of the four groups of students. A paired-sample t-test was used to analyze the pre- and post-tests of students’ creative thinking to verify whether all four patterns of collaborative learning could improve the students’ creative thinking. One-way ANOVA was used to analyze the post-test results of the four groups’ creative thinking to determine the differences in the creative thinking of the four groups of students. The results indicated that the patterns of collaborative learning used by G1, G3, and G4 were effective in improving students’ creative thinking, but the pattern for G2 was not. Moreover, there were significant differences in the cultivation of students’ creative thinking via AI courses among these four patterns of collaborative learning. The G4 students, who had an assigned leadership role and consensus building, showed the greatest improvement in creative thinking. In particular, without an assigned leadership role and consensus building, students’ flexibility of creative thinking would be improved to a greater extent. Teachers can adapt the findings of this study in order to consciously train team leaders in the collaborative learning process and guide them to reach a consensus to achieve the goal of fostering creative thinking in digital-technology-supported courses. To be specific, teachers should let students participate in group collaborative learning in a free way to cultivate their flexibility.

1. Introduction

Digital education, which emphasizes the innovative use of digital tools and technologies in face-to-face, hybrid, and entirely online learning and teaching environments, is making the development of artificial intelligence (AI) technology increasingly important [1]. Nowadays, promoting the developmental process of digital education that relies on AI courses for K–12 is one of the most crucial issues on a global scale [2]. Some countries and organizations have put forward important policies for promoting AI education in order to cultivate AI talents with key competences that will be essential for students’ future careers, including cognitive competence, teamwork competence, and creative thinking [3]. As one of the essential literacies in Partnership for 21st Century learning, creative thinking helps students become competent in facing the challenges posed by future digital life and in satisfying the needs of the sustainability of our society [4]. In Gestalt psychology, which was founded by Max Wertheimer, creative thinking was distinguished as the ability to break out of old patterns and cause an epiphany [5]. Students with creative thinking can envision new possibilities, innovative procedures, and fresh, potentially productive problems instead of following strict rules of reasoning and evidence [6]. Empirical studies on promoting students’ creative thinking have been conducted in order to eliminate the phenomenon in which educational practices lag behind the demands for the cultivation of students’ creative thinking [7,8]. Different programs that effectively promote students’ creative thinking have been proposed in prior studies, including the 5-I training program [9], divergent thinking training [10], and a doodle-book program named Creative Doodle: The Adventures of Dragonfly Grazka [11]. All of these prior programs emphasized that collaborative learning played a major role in the development of students’ creative thinking. These studies on the promotion of learners’ creative thinking were mostly carried out in STEAM and robot coding education [12,13]. Therefore, it is worth further exploring how to promote K–12 students’ creative thinking through collaborative learning in AI courses.
Existing studies involving AI courses have confirmed that they tend to be conducted in the form of collaborative learning, and they aim to develop students’ creative thinking by offering them challenging themes and platforms for problem solving in groups [14]. Some studies have suggested that collaborative learning is an effective approach to the cultivation of students’ creative thinking [15,16]. Collaborative learning allows students to take part in some group activities, such as collective discussion and brainstorming, which could promote the development of their cognition [17,18]. Group collaborative learning enhances students’ interaction, builds up a group relationship among students, and increases their motivation [19]. However, certain aspects of the practice of collaborative learning, such as unbalanced participation and lack of depth of interaction, can prevent the development of creative thinking among students [20]. The aim of cultivating students’ creative thinking by using collaborative learning cannot be achieved simply by grouping students; additional factors are necessary. Therefore, exploring the scientific and effective patterns of group collaborative learning applied in AI courses to promote students’ creative thinking is critical.
As simply grouping students cannot result in the expected effect of collaborative learning, the implementation of collaborative learning relies on a series of collaborative group activities that enhance group members’ participation [21]. Group characteristics, including consensus building and an assigned leadership role, are critical for group collaborative learning [22,23]. Mutual understanding and consensus building have been found to contribute to successful group collaborative learning [24]. Students may interact deeply and construct a better shared understanding while reaching a consensus in group collaborative learning [25]. In particular, having an assigned leadership role in the group allows students to promote as many innovative thoughts as possible in order to reach a better consensus and draw conclusions [26,27]. Therefore, whether an assigned leadership role and consensus building in collaborative learning have an effect on students’ creative thinking in digital-technology-supported AI courses deserves further exploration. To fill this gap, in this study, we divided students into four patterns of group collaborative learning based on two dimensions—assigned leadership role and consensus building—in an AI course. Then, a seven-week teaching practice was used to apply, verify, and compare the effectiveness of these four patterns of group collaborative learning for the promotion of creative thinking. The aim of this study was to explore how primary school students’ creative thinking, including originality, flexibility, fluency, and elaboration, could be cultivated by using different patterns of group collaborative learning in AI courses. The findings of this study can provide suggestions for primary school instructors to better implement the patterns of group collaborative learning in AI courses in order to improve their students’ creative thinking.

2. Literature Review

2.1. Digital AI Courses

Artificial intelligence (AI) has become ubiquitous in our social life. It is necessary to help K–12 students know more about AI to prepare them for their future professions and for the use of AI technologies in the digital age. Thus, AI education for K–12 has attracted a great deal of attention [2]. AI courses in schools are an important strategic move to nurture K–12 students who should be well prepared for the sustainability of society [28]. Kim and colleagues [29] emphasized that students need to develop their literacies and intelligence in order to understand, develop, and apply AI and related technologies through AI courses. Some AI courses have been implemented for students at different stages. For example, an open-source course developed by researchers and collaborators from the Massachusetts Institute of Technology encouraged students to create and think critically [30].
As integrated courses that are closely related to multiple disciplines, AI courses require students to practice and cultivate their key competences, including their skills, cognition, and self-learning competence, thus laying a solid foundation for them to adapt to the future [3]. Williams and colleagues [31] developed an AI course named PopBots for pre-K and kindergarten children to improve their computational thinking. It is worth noting that by analyzing K–12 AI courses in Australia, Yue and colleagues [32] concluded that these courses were devoted to teaching students AI knowledge and the skills to use AI ethically, in addition to other essential competences, including collaborative abilities and creative thinking. It has also been confirmed that students’ creative thinking can be cultivated in digital learning environments. For example, Wheeler and colleagues [33] investigated whether information and communication technology (ICT) had a creative influence on primary school students. Karakus and colleagues [34] also discussed how the application of Storyjumper, a Web2.0 tool, in the teaching process of a life science course could significantly improve the creative thinking of primary school students. In digital AI courses that help students experience the practical advantages of AI, understand the principles of AI, and realize applications of AI, students can use digital technology to develop programs and build systems to accurately express themselves, thereby facilitating the development of relevant competencies [3]. Therefore, whether the use of digital technology in AI courses can promote primary school students’ creative thinking deserves further exploration.

2.2. Collaborative Learning

Collaborative learning is the active engagement and discussion of participants in a coordinated effort for a common purpose or task, where participants work to find some solutions and reconstruct shared knowledge through group learning [35,36,37,38]. Collaborative learning not only allows students to learn with a knowledge network composed of shared and unique knowledge [39], but also stimulates students’ attention so as to invest more cognition in creative tasks in order to promote creative performance and cultivate creative thinking [40]. Recently, plenty of studies have explored the effectiveness of different types of collaborative learning in different contexts of education [41,42,43,44]. For example, Jeong and colleagues [42] showed that students attained better knowledge outcomes and affective outcomes in support of computer-supported collaborative learning in STEM education. Liang and colleagues [43] found that collaborative gaming could enhance students’ learning achievement and improve their abilities, such as problem solving and critical thinking. In addition, Ramirez and Monterola [44] showed that collaborative scripts could significantly improve students’ collective efficacy. Therefore, collaborative learning can contribute to students’ thinking skills and learning performance, especially with the support of digital technology. However, the outstanding learning outcomes of collaborative learning do not simply depend on grouping; the characteristics of students also need to be taken into account [45]. Students’ different knowledge backgrounds, lack of productive collaborative experience, and unawareness of the need to ask for help bring about superficial interaction and an unordered collaborative process in group collaborative learning [20,46].
In order to realize effective collaborative learning, Gijlers and colleagues [24] suggested that students in groups need to maintain mutual understanding and reach a consensus on solutions and domain knowledge. During the process of consensus building, groups of students can illustrate knowledge, interact deeply, and create a shared understanding [25]. Harris and colleagues [26] found that experimental groups that built a consensus had better performance on recall completeness and accuracy than the control group. Cheng and Chu [47] developed a computer-supported collaborative learning system consensus approach that needed students to build a consensus, and the results indicated that the groups that used this approach had significant improvements in their learning outcomes. In the process of building a consensus, students in groups would listen attentively and respectfully, think carefully, and interact deeply, which encouraged everyone to express themselves [48]. In addition to consensus building, a properly assigned leadership role also has a significant effect on group collaborative learning. An assigned leader in a group can guide other group members to promote more novel thoughts, enhance cognitive engagement, reach a better consensus, and draw conclusions [27,49]. It has been shown that a leadership role has a positive impact on members’ participation, leading them to exhibit increased participatory behaviors [22,50,51]. Specifically, Cheng and colleagues [27] found that assigned leadership was helpful for the improvement of students’ creativity, critical thinking, and learning achievement. However, little research has distinguished different patterns of group collaborative learning according to whether there is an assigned leadership role and whether consensus building exists in the group. Therefore, the current study classified four patterns of group collaborative learning. The differences in the processes of group collaborative activities among the different patterns were mainly reflected in whether an assigned leadership role guided group members to carry out collaborative discussion activities and technical operations and whether all group members built the same consensus at the end of the discussion.
Nowadays, collaborative learning is widely conducted in class because it is beneficial for improving students’ motivation, learning engagement, high-order thinking, and learning outcomes [52]. For example, Sisman and colleagues [37] investigated the effect of collaborative learning on the critical thinking of elementary school students in educational robotics. Wu and colleagues [53] proposed a mind-map-based collaborative learning approach and verified its effectiveness in terms of enhancing students’ innovative abilities. In addition, the cultivation of creative thinking through collaborative learning has been confirmed in previous studies. From the point of view of open-ended questions, Hobri and colleagues [54] showed that collaborative learning had a positive impact on students’ creative thinking. Chang and colleagues [41] indicated that learners’ creative thinking could be fostered by open data on programming design in a computer-supported collaborative learning environment. Caldwell and colleagues [55] explored the effects of visual posts on creative thinking in online collaborative learning. Generally, collaborative learning is a significant approach to cultivating students’ creative thinking. It was, therefore, worthwhile for the current study to explore how to effectively cultivate creative thinking with group collaborative learning via AI courses. Therefore, this study applied four patterns of group collaborative learning in an AI course and further explored the effects of these four patterns on students’ creative thinking, as well as their differences.

2.3. Creative Thinking

Creative thinking, as a form of thinking activity, refers to a thinking process in which students solve problems in novel and unique ways based on their experience and generate new ideas and learning products [56]. It has been confirmed that creative thinking allows students to solve complex, ill-defined, or poorly structured problems with high-quality, effective, original solutions in a rapidly changing environment [57]. Gu and colleagues [9] also concluded that creative thinking is a cognitive process that is demonstrated through creative performance. People with creative thinking exhibit four main characteristics: (1) originality: the ability to capture the sense that an idea is new and not well worn; (2) flexibility: the ability to go beyond traditions, habits, and the obvious; (3) fluency: the total number of appropriate solutions relevant to a specific task or problem; (4) elaboration: the ability to perfect the details of appropriate solutions [58,59]. Individuals with high levels of creative thinking would display these four characteristics. Thus, creative thinking in this study consisted of four sub-dimensions: originality, flexibility, fluency, and elaboration.
Creative thinking is an essential ability for K–12 students to meet the requirements of sustaining our society. Touretzky and colleagues [30] called for a deeper understanding of AI in students’ early years at school, as it can foster creative thinking. The development of K–12 students’ creative thinking through AI education has attracted extensive attention in recent years [60]. AI education, including coding education [61] and STEM [62], has been widely used in the process of educational practice. During the process of learning AI, students need to code and create their productions using robots, which has been shown to improve creative thinking [12]. Therefore, to the way of fostering students’ creative thinking in AI education deserves further exploration [14].
In 1926, Graham Wallas proposed that the creative process comprises four stages (Art of Thought): preparation, incubation, illumination, and verification. This idea is foundational in creativity research [63] and is still a conceptual anchor for many researchers [64,65]. For example, Wu and colleagues [18] put forward five strategies of creative thinking training, namely, delay evaluation, collective discussion, ideas and tips, multi-solving, and multiple perspectives. They applied these strategies and found that students’ creative thinking in the experimental group was significantly better than that of the control group. Putri and colleagues [66] concluded that project-based learning makes a great deal of difference in the development of creative thinking in primary school. Wulandari and colleagues [16] proposed a Physics Learning Strategy based on asking activities and group competition, and they allowed students to take part in some learning activities, such as cooperative learning, competition in the group, and so on. The results of their experiment showed that the Physics Learning Strategy could effectively foster students’ creative thinking. In addition, Aiamy and Haghani [17] found that students’ creative thinking would be cultivated in synectics and brainstorming. In general, it seems that collaborative activities that include brainstorming and group discussion, which are usually conducted in collaborative learning, have a significant effect on students’ creative thinking. Therefore, this study aimed to examine the effectiveness of collaborative learning in terms of cultivating primary students’ creative thinking in an AI course.
Various assessment tools, which differ according to their implementation content, research object, research method, and so on, have been used to assess students’ creative thinking. Simone and Hoicka [67] suggested that the Alternative Uses Test by Guilford can effectively assess junior high school students’ divergent thinking. In 2021, the Organization for Economic Cooperation and Development established the PISA 2021 Creative Thinking Framework [68] as a detailed and contemporary testing tool for testing students’ creative thinking. Some researchers have also tested students’ creative thinking with the Williams Creativity Scale, which examines curiosity, imagination, complexity, and risk taking with 50 questions and is sensitive to the testing environment [69,70]. Additionally, Kim [71] stated that the Torrance Test of Creative Thinking (TTCT), including TTCT-Verbal and TTCT-Figure, was a useful tool for creative thinking and was appropriate for people from the kindergarten level to the graduate level and beyond. According to the existing studies, TTCT-Figure is the preferred evaluation tool for primary school students [19,72,73]. Taking the TTCT-Figure test requires a relaxing and cheerful atmosphere that avoids threatening situations and test anxiety [73,74]. Thus, participants can enjoy the activities and view the tests as a series of fun games. TTCT-Figure is, therefore, a fun and relaxing way to measure creative thinking in the form of drawing; it does not require a high level of literacy and can be used evaluate creative thinking from flexibility to elaboration. For the above reasons, TTCT-Figure was selected as the assessment tool for this study.

2.4. Research Questions

Summarizing the literature review above, it can be concluded that collaborative learning, which is widely used in digital AI courses, has a positive impact on students’ creative thinking. In addition, the desired effectiveness of collaborative learning depends, to some extent, on an assigned leadership role and consensus building. However, there are few studies that have adopted four patterns of group collaborative learning according to whether an assigned leadership role and consensus exists in a group in order to investigate the effects of different patterns of collaborative learning on primary school students’ creative thinking in a digital AI course. Thus, the present research aimed to answer the following questions.
(1)
What are the impacts of the four patterns of group cooperative learning on fourth-grade students’ creative thinking in a digital AI course?
(2)
Which pattern(s) of group collaborative learning have a positive effect on fourth-grade students’ creative thinking?

3. Methods

3.1. Participants

This research was conducted in an AI course at a primary school in Guangzhou, China. The participants consisted of 37 students from Class 1, Grade 4. Among them, there were 21 males and 16 females. The students from the same class had similar previous learning experiences in AI courses. Their instructor was a teacher teaching the course on information technology who had sufficient teaching experience with using digital technology in AI courses. According to prior studies, as a quasi-experiment, this study used a one-group pre- and post-test design. A total of 37 participants aged from 10 to 11 years old were randomly divided into four groups, with each group comprising 9 to 10 students [27,75,76,77,78,79]. Each group was assigned to apply one of the four patterns of group collaborative learning. Since the four patterns of group learning had specific requirements, each group member was asked to follow the requirements. A researcher monitored the whole process and corrected the group members’ wrong or inappropriate behaviors according to the requirements in a timely manner. The research lasted for seven weeks, with two 40-min lessons per week. All 37 students and the teacher in this study participated completely voluntarily and provided their informed consent. Meanwhile, they were told that the data that they provided were anonymous and would not be of any commercial use.
The group information for the four groups is shown in Table 1. Group 1 (G1) adopted a pattern with no assigned leadership role and no consensus building; in order to complete the task, the group members took turns presenting their views in a discussion, and there was a lack of cooperation and interaction. Group 2 (G2) adopted a pattern with no assigned leadership role but with consensus building; the group members were required to present their views and conduct consultations to reach a consensus according to the task’s requirements. Group 3 (G3) adopted a pattern with an assigned leadership role but without consensus building; the leader in the group issued instructions to urge the group members to complete the discussion, but there was a lack of interaction and equality. Group 4 (G4) adopted a pattern with an assigned leadership role and consensus building; the leader, who was more familiar with the learning content, put forward questions to ensure that other members participated in interactions, understanding, and the consensus; then, the group members collaborated to complete the operation.

3.2. Procedure

The procedure of the teaching practice is shown in Figure 1. All of the AI course’s classes were taught by the same instructor for 40 min twice a week for 7 weeks. For the first week, a pre-test of creative thinking was conducted. Then, the teacher finished a series of teaching activities as preparation, including randomly splitting students into four groups, interpreting the group collaboration strategies for all of the students, and carrying out training activities for the assigned leadership roles of Group 1 and Group 2. The instructor interpreted the concept, form, and strategy of group collaborative learning for all of the students in advance by playing a video that introduced collaborative learning, which was titled ȜHow much do you know about group collaborative learning?ȝ This procedure aimed to help students better adapt to the subsequent group collaborative learning.
The teaching implementation stage was from the second to the sixth week and comprised the following six procedures, which included both teacher and student activities: design of group collaboration activities, presentation of the teaching objective, knowledge teaching, implementation of group collaboration activities, display and evaluation of groups’ projects, teacher feedback, and a summary. The group learning task lists, which included the learning objectives, learning paths, learning approaches, learning processes, and self-assessment forms, were distributed to students; see Figure 2. All groups were required to complete the task lists. Among them, Group 1 and Group 4, which were required to reach a consensus after collaboration, needed to complete the learning task lists collectively. All of the members of Group 2 and Group 3, which were not required to reach a consensus after collaboration, were required to complete the learning task lists separately. The teacher designed various types of group collaborative learning activities according to the teaching content and objectives, including activities in the question-and-answer mode, discussion-based mode, operation-based mode, game-based mode, and inquiry-learning mode. The teacher created a problem situation, and then all of the groups started the collaborative learning activities, mainly in accordance with the following process: understanding the problem, solving the problem, breaking through the problem’s bottleneck under the guidance of the teacher, and completing the problem task. After each group completed the task, their project would be displayed. The teacher gave feedback on the group projects through the cloud classroom teaching system according to their presentations so as to guide the group members to continue to revise and improve their projects. Finally, the teacher summarized the knowledge of the class in the form of a mind map and introduced the learning content of the next class. In the seventh week, post-tests of creative thinking were completed by each group of students.
Digital technology was used in this study to support the implementation of digital education in an AI course. The cloud classroom teaching system, a kind of digital teaching system for the education and training industry, provided the following functions: management of students’ devices, sharing of learning materials, presentation of teachers’ operations, collection and uploading of students’ work, and so on. The cloud classroom teaching system was used in the whole teaching process of this study. Before class, the teacher used the cloud classroom teaching system to create an online learning course in advance, form groups, plan learning paths, and design learning materials to make preparations for the AI course. In the class, with the support of the cloud classroom teaching system, the teacher could enhance the interaction between the teacher and students, share learning resources, perform evaluations, and give feedback in the process of group collaborative learning to promote in-depth group collaborative learning. After class, the teacher used the system to realize the rapid collection and evaluation of students’ work. Therefore, this study aimed to design and develop effective digital AI courses with the support of a technology-assisted classroom equipped with computers, blocks, and other hands-on learning resources.

3.3. Instruments

TTCT-Figure (Form A), which was developed by Ellis Paul Torrance in 1966 and adapted to different participants, from children to adults [80], was used to comprehensively evaluate the creative thinking levels of the fourth-grade students [74]. TTCT-Figure consists of three activities: picture construction, picture completion, and repeated figures of lines or circles. Each activity was to be completed within 10 min. The reliability of TTCT-Figure was tested by Torrance; it was exhibited that the total reliability coefficient was 0.86, and the reliability coefficients for the sub-dimensions were 0.82 (fluency), 0.78 (flexibility), 0.51 (originality), and 0.78 (elaboration). According to the TTCT Norms—Technical Manual (Torrance, 1966), which presents detailed scoring criteria and procedures, the scores—that is, the Creativity Index (CI)—were calculated by the researchers of this study.
The students’ CIs were collected from the four groups before and after group collaborative learning, including the pre- and post-tests, which were applied to evaluate the changes in students’ creative thinking after using group collaborative learning in the AI course for seven weeks. It was beneficial to confirm whether the patterns of group collaborative learning could promote students’ creative thinking in AI education and to determine which pattern of group collaborative learning had a more significant effect on the promotion of students’ creative thinking.

3.4. Data Analysis

A pre-test and post-test of creative thinking were conducted before and after the research to test the creative thinking of the four groups of students. Excel was used to record the CI results of the pre-test and post-test; IBM SPSS 20.0 was applied to analyze the pre-test and post-test data of this study. Descriptive statistics were used to calculate the means (M) and standard deviations (SD) of the four groups. To assess whether the patterns of group collaborative learning could promote fourth-grade students’ creative thinking, a paired-sample t-test was applied in order to compare students’ pre- and post-test scores for their creative thinking. Then, one-way ANOVA and Bonferroni alpha error adjustment were applied in order to examine the differences among the four patterns of group collaborative learning in the promotion of fourth-grade students’ creative thinking.

4. Results

4.1. Test for Normality

The score for creative thinking was a continuous variable, so it could be tested for normality. As shown in Figure 3 and Figure 4, the results of the pre- and post-tests of creative thinking were from the normally distributed population in this study. Therefore, we used parametric tests, including a paired-sample t-test and one-way ANOVA, to analyze our data and confirm our hypotheses.

4.2. Analysis of the Effects of the Four Patterns of Group Collaborative Learning on Fourth-Grade Students’ Creative Thinking

A paired-sample t-test was conducted for the four groups’ pre- and post-tests of creative thinking. The results (see Table 2) showed that there was no significant difference in the pre- and post-test levels of creative thinking for G2 (p > 0.05). There were significant differences in the pre- and post-test levels of creative thinking for G1 (p < 0.001), G3 (p < 0.05), and G4 (p < 0.001). Hedges’ g and Glass’s delta were used to calculate the effect sizes, as they could detect the effect of the t-test [81]. The values of the effect sizes indicated that G1 and G4 had a large effect (Hedges’ g > 1, Glass’s delta > 1), whereas G3 had a medium effect (Hedges’ g > 0.38, Glass’s delta > 0.38). Therefore, the pattern of collaborative learning used by G4 was the most effective in terms of improving students’ creative thinking in the digital AI course in comparison with the patterns used by G1 and G3, while the pattern used by G2 was ineffective in terms of improving students’ creative thinking.
A paired-sample t-test was conducted on the sub-dimensions of creative thinking—fluency, flexibility, originality, and elaboration—for the four groups. The results (seen in Table 3) showed that students’ fluency and originality in three of the groups (G1, G3, and G4) significantly improved, but they did not for G2. For flexibility, only students in G2 improved. Students’ elaboration was not significantly improved in the four groups. Specifically, G1 and G4 showed large improvements in fluency and originality (Hedges’ g > 1, Glass’s delta > 1), and G3 showed small improvements (Hedges’ g > 0.38, Glass’s delta > 0.38).

4.3. Analysis of the Differences among Different Patterns of Group Collaborative Learning for Fourth-Grade Students’ Creative Thinking

One-way ANOVA was conducted for the pre-test of creative thinking for the four experimental groups. The results (see Table 4) indicated that there were no significant differences among the four experimental groups’ pre-tests of creative thinking (p > 0.517). Therefore, the students in the four groups had the same level of creative thinking before conducting the collaborative learning, indicating that the groups of students in this research were comparable.
One-way ANOVA was also conducted for the post-test of creative thinking for the four experimental groups. The results (see Table 5) showed that there were significant differences among the four experimental groups’ post-tests of creative thinking (p < 0.05). Bonferroni alpha error adjustment was conducted to carry out pairwise comparisons among the four groups. The adjusted significance indicated that there was a significant difference between G3 and G4 (p = 0.043 < 0.05). Thus, different patterns of collaborative learning had differences in their cultivation of students’ creative thinking via the digital AI course. It can be seen in Figure 5 that the scores for creative thinking of the students in G4 were the highest. Thus, RQ2 was answered.
Table 6 shows the results of the one-way ANOVA of the four sub-dimensions of creative thinking among the four experimental groups, indicating that there were significant differences in fluency and originality (p < 0.001), while there were no significant differences in flexibility and elaboration among the four groups (p > 0.05). As shown in Figure 5, the students in G4 performed better than the students in other groups in terms of fluency and originality.
According to the results of the Bonferroni alpha error adjustment of the four groups’ fluency (p = 0.022 < 0.05) and originality (p = 0.012 < 0.05), there was a significant difference between G3 and G4. As seen in Figure 6, the students in G4 performed better than the students in the other groups in terms of fluency and originality.

5. Discussion

This study investigated effective patterns of group collaborative learning that were used in a digital AI course to promote fourth graders’ creative thinking, and it explored the differences among the four patterns in their promotion of students’ creative thinking in a seven-week teaching practice. A total of 37 students were invited to take part in the study for 7 weeks and were assigned to groups with four patterns in a digital AI course. TTCT-Figure (Form A) was used to assess students’ creative thinking in a pre-test and post-test. The results of the data analysis indicated that the patterns of group collaborative learning used in G1, G3, and G4 were beneficial for fourth graders’ creative thinking in the digital AI course, and there were significant differences among the four patterns in the cultivation of creative thinking.

5.1. The Patterns of Group Collaborative Learning Used in G1, G3, and G4 Were Effective in Terms of Promoting Fourth Graders’ Creative Thinking

RQ1 in this study asked whether all of the patterns of group collaborative learning promoted fourth-grade students’ creative thinking in the digital AI course. The significant differences indicated that the patterns of group collaborative learning used in G1, G3, and G4 could effectively foster students’ creative thinking, but that used in G2 could not. This finding was similar to some prior conclusions about the effectiveness and ineffectiveness of consensus building [25] and the assigned leadership role [22,47] in group collaborative learning. The improvement of creative thinking requires a process that allows students to explore in depth and to try some possible solutions according to their knowledge and skills [63]. Students in group collaborative learning are encouraged to work together to find some proper solutions and to reconstruct shared knowledge [36,38]. With the guidance of an assigned leader, group members can interact deeply, express more creative thoughts in an orderly fashion, enhance their cognitive engagement, and draw proper conclusions [49]. In particular, while reaching a consensus, students can illustrate knowledge, interact deeply, and create a shared understanding [25]. Therefore, a significant promotion of creative thinking could be found in G3, which included an assigned leadership role, and in G4, which included an assigned leadership role and the building of a consensus.
Although the students in G2 built a consensus after group collaborative learning, there was no difference in their creative thinking after the learning. It was considered that ideas about which a consensus is reached sometimes just represent the viewpoints and reflections of individual students [82]. However, G2, which was without consensus building and an assigned leadership role, also had a significant difference in the promotion of creative thinking. Blaise and Elsden-Clifton [83] considered that new learning strategies not only bring challenges to teachers, but also make students nervous and uncomfortable. In addition, a group leader with a high status is possibly biased against what group members put forward, which will interfere with the results [84]. The students in G1 were less restricted in their group collaborative learning. In the process of presenting ideas in turn, each student was allowed to stick to their own ideas, learn from members that they agreed with, and constantly refined their ideas without intervention. Therefore, it is important to explore how students can be well prepared for these new learning strategies.
Meanwhile, two dimensions of creative thinking, namely, fluency and originality, were significantly improved in G2, G3, and G4; that is to say, after the intervention, students could put forward plenty of unique ideas, such as proposals of special solutions to the problems. This result was similar to Cubukcu and Cetintahra’s [85] study, which showed that the presence of visual clues contributed to some characteristics (fluency and originality) of creative thinking. However, the study by Cubukcu and Cetintahra [85] did not focus on collaborative learning. Thus, the present study extended the finding to the impact of collaborative learning in primary school. Wechsler [86] indicated that learners who achieved higher scores in fluency and originality on the TTCT had a stronger ability to raise more uncommon ideas. It was shown that fluency and originality were core characteristics of creative thinking and that they reflected innovation to the greatest extent [87]. In the AI course, the students needed to work together to complete some creative and challenging tasks as groups. They were allowed to learn from each other in collaborative learning [88]. Meanwhile, before finding some effective solutions, the students had chances to discuss continually and reflect deeply [89]. Therefore, when constructing individual knowledge, students could learn to think from more and different perspectives. In addition, the improvement in students’ creative thinking can be supported in a group [90]. In groups, while facing difficulties, members can receive help from others through their acquisition and operation of knowledge in AI courses.
It Is worth mentioning that only the students in G1 improved the flexibility of their creative thinking. Internal stimuli from students could impact flexibility in creative thinking, which is difficult to bring about from the outside [91]. Perhaps, the students in G2 did not have too many constraints. Thus, they adapted to the collaborative learning better and completed the tasks with a positive emotional state, which could promote flexibility [82,92]. In addition, students’ elaboration was not significantly improved in the four groups. Wang [93] showed that scores for elaboration in creative thinking were connected with the time spent on learning. Therefore, it is difficult to improve the ability to perfect the details of appropriate solutions in a short time.

5.2. Differences in the Promotion of Fourth-Grade Students’ Creative Thinking among the Four Patterns of Group Collaborative Learning

RQ2 in this study asked if there were any differences among the four patterns of group collaborative learning in the promotion of fourth-grade students’ creative thinking. Another finding of this study answered this question in detail. Compared with the students in the groups using other patterns, the students in G4, which had an assigned leadership role and consensus building, gained the highest level of creative thinking and did the best in two dimensions, namely, fluency and originality. The group leaders who were assigned and trained by the teacher were more responsible and felt more stress when guiding group members to complete the tasks carefully. The high engagement of the assigned leader promoted others to participate actively and interact deeply [50,51], which contributed to the students’ creative thinking [27]. The originality of thoughts depends on the depth of discussion and exploration [94]. With the guidance of an assigned leader, group members can express and learn more creative thoughts in an orderly fashion, illustrate knowledge, create a shared understanding, and draw proper conclusions [25,49]. Additionally, the expectation that leaders will exhibit creative thinking has a positive effect on members’ creative performance [95]. In AI courses, students are always asked to discuss and create something to deal with practical problems. Group members will raise more novel ideas to meet the requirements of the group leader. Therefore, the students in G4 had the highest scores for originality. In addition, a prior study showed that learners would attain the highest level of fluency with less time stress [96]. The leader in G4 prepared before the classes and was familiar with the AI content. In the same amount of time, he was under less pressure and guided others to interact, discuss in an orderly manner, and reach a consensus. As a result, the group members were able to learn and reflect under less pressure.
However, there were no remarkable differences among the four patterns in terms of the other two dimensions, namely, flexibility and elaboration. It is difficult to develop the flexibility and elaboration of creative thinking in a short time. The development of flexibility is related to whether learners are internally stimulated [91]. It is hard for interventions such as group collaborative learning to impact it. The improvement of elaboration depends on the time spent on learning [93]. Therefore, researchers need to conduct experiments for an extended period of time to foster elaboration.
The students in G1, which did not have an assigned leader role or consensus building, obtained a higher level of creative thinking than that of the students in G3, who just had an assigned leadership role and no need to build a consensus. This finding was similar to the research of Howe [81], who found that groups without consensus building may have better results than groups working with a consensus. The possible reason behind this finding was that, sometimes, the ideas upon which a consensus is reached might just represent the reflections of individual students who performed positively in the group collaborative learning. A group leader with a high status could possibly have a bias against the viewpoints that group members put forward, which will interfere with the results [60]. On the other hand, the students were unfamiliar with the patterns of group collaborative learning, so they possibly felt nervous and reserved [82]. Each student in G2 had fewer limits and more creative space.

6. Conclusions and Limitations

6.1. Implications

The study aimed at exploring the effects of four patterns of group collaborative learning on students’ creative thinking, as well as the differences in the effectiveness of the four different patterns. The findings revealed that the patterns of group collaborative learning conducted by G1, G3, and G4 could significantly cultivate students’ creative thinking, especially two dimensions of creative thinking: fluency and originality. However, in comparison with G2 and G3, the patterns of group collaborative learning used in G4 and G1 could better achieve effectiveness in improving fourth-grade students’ levels of fluency and originality of creative thinking in the AI courses. There are several theoretical and practical implications of this study. Theoretically, the current study put forward four patterns of group collaborative learning according to whether there is an assigned leadership role and whether consensus building exists in the group. The findings of this study enrich the literature on the application of group collaborative learning in AI courses to cultivate students’ creative thinking. Moreover, this study strengthens the previous research on the effectiveness and ineffectiveness of assigning a leadership role and building a consensus when applying group collaborative learning. Practically, the findings of this study can provide some suggestions for primary school instructors to better implement group collaborative learning in AI courses to improve their students’ creative thinking. When using a group collaborative learning approach, teachers should strengthen the training of group leaders and consciously guide groups of students to reach a consensus at the end of their discussions, which is more conducive to the cultivation of learners’ creative thinking. Otherwise, when teachers need to pay attention to the improvement of learners’ flexibility, they should let the learners participate in collaborative learning more freely. The findings of this study can also be adapted to other courses that use digital technology to promote students’ creative thinking.

6.2. Limitations and Future Studies

There are several limitations to the present study. Firstly, this study conducted a seven-week teaching program in an AI course in a primary school. Nowadays, K–12 students are able to learn AI knowledge and are competent in using AI technology. Thus, future research can be extended to students in middle or high school to explore the effects of patterns on the improvement of creative thinking. Secondly, other factors, such as gender, learning interests, and learning styles, may influence the cultivation of students’ creative thinking in AI courses, which may be considered in future studies. Third, whether the findings of this study can also be adapted to other courses that use digital technology to promote primary school students’ creative thinking could be explored in the future. Finally, this study employed a small sample of students (n = 37) and a large number of tests. We invite other researchers to replicate this study to validate its findings in the future.

Author Contributions

Conceptualization, X.H. and J.H.; methodology, Y.L. and S.M.; writing—original draft preparation, Y.L. and X.H.; writing—review and editing, X.H. and S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China (No. BCA220206).

Institutional Review Board Statement

This study was approved by the ethics committee of Nanjing Normal University (NNU202209001).

Informed Consent Statement

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

Data Availability Statement

The data collected and analyzed in this research are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Procedure.
Figure 1. Procedure.
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Figure 2. Group learning task lists.
Figure 2. Group learning task lists.
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Figure 3. Normal Q-Q plot of the creative thinking pre-test scores.
Figure 3. Normal Q-Q plot of the creative thinking pre-test scores.
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Figure 4. Normal Q-Q plot of the creative thinking post-test scores.
Figure 4. Normal Q-Q plot of the creative thinking post-test scores.
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Figure 5. Mean of the four groups’ creative thinking.
Figure 5. Mean of the four groups’ creative thinking.
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Figure 6. Means of the four dimensions of creative thinking for the four groups.
Figure 6. Means of the four dimensions of creative thinking for the four groups.
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Table
Table 1. Four patterns of group collaborative learning.
Table 1. Four patterns of group collaborative learning.
Group 1 (G1)Group 2 (G2)Group 3 (G3)Group 4 (G4)
An assigned
leadership role		××○○
Consensus building×○×○
Table
Table 2. A paired-sample t-test on the pre- and post-tests of students’ creative thinking for the four groups.
Table 2. A paired-sample t-test on the pre- and post-tests of students’ creative thinking for the four groups.
GroupPaired Differences tHedges’ gGlass’s Delta
MSDSE95% CI
LowerUpper
G141.77835.61911.87314.39969.1573.519 **–1.431–2.004
G212.00035.33912.494–17.54441.5440.960
G319.50018.8285.9546.03132.9693.275 *–0.467–0.735
G462.20024.8097.84544.45279.9487.928 ***–2.995–3.456
Note: M = mean; SD = standard deviation; SE = standard error of the mean; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table
Table 3. The pre- and post-test results of the sub-dimension of creative thinking in the four groups.
Table 3. The pre- and post-test results of the sub-dimension of creative thinking in the four groups.
DimensionPaired Difference (Pre-Test and Post-Test)tHedges’ gGlass’s Delta
GroupMSD95% CI
LowerUpper
FluencyG1−9.6679.354−16.857−2.476−3.100 *−1.591−2.287
G2−4.5006.928−10.2921.292−1.837
G3−4.5005.855−8.688−0.312−2.431 *−0.476−0.714
G4−14.9005.174−18.601−11.199−9.107 ***−3.214−3.656
G1−3.1112.571−5.088−1.135−3.630 **−0.690−1.002
FlexibilityG20.3753.777−2.7833.5330.281
G3−1.0006.992−6.0024.002−0.452
G4−1.3006.129−5.6853.085−0.671
G1−26.88924.292−45.562−8.216−3.321 *−1.622−2.654
G2−7.87516.462−21.6375.887−1.353
OriginalityG3−13.10012.905−22.332−3.868−3.210 *−0.463−0.782
G4−42.30014.930−52.980−31.620−8.960 ***−2.837−3.872
ElaborationG1−2.1119.829−9.6665.444−0.644
G20.00016.750−14.00414.0040.000
G3−0.90014.441−11.2319.431−0.197
G4−3.70017.544−16.2508.850−0.667
Note: M = mean; SD = standard deviation; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table
Table 4. Results of the one-way ANOVA of the pre-test of creative thinking for the four experimental groups.
Table 4. Results of the one-way ANOVA of the pre-test of creative thinking for the four experimental groups.
VariableGroupMSDFSigPartial η2
Creative
thinking		G1117.89029.4760.7740.5170.066
G2140.63041.283
G3129.30037.532
G4121.10025.449
Note: M = mean; SD = standard deviation.
Table
Table 5. Results of the one-way ANOVA of the post-test of creative thinking for the four experimental groups.
Table 5. Results of the one-way ANOVA of the post-test of creative thinking for the four experimental groups.
VariableGroupMSDFSigPartial η2
Creative
thinking		G1159.67025.5883.0170.0440.215
G2152.63020.942
G3148.80042.182
G4183.30011.982
Note: M = mean; SD = standard deviation.
Table
Table 6. The one-way ANOVA of the four sub-dimensions of creative thinking among the four experimental groups.
Table 6. The one-way ANOVA of the four sub-dimensions of creative thinking among the four experimental groups.
DimensionsGroupMSDtSigPartial η2
FluencyG133.670 5.500 4.832 0.007 0.305
G230.630 9.117
G327.400 9.192
G439.000 2.494
FlexibilityG119.440 4.391 0.614 0.611 0.053
G217.130 6.534
G316.400 5.103
G418.500 5.276
OriginalityG172.440 16.898 4.976 0.006 0.311
G260.130 13.303
G355.200 30.177
G487.300 13.005
ElaborationG134.110 15.415 1.032 0.391 0.086
G244.750 25.955
G349.800 25.359
G438.500 14.887
Note: M = mean; SD = standard deviation.
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Hu, X.; Liu, Y.; Huang, J.; Mu, S. The Effects of Different Patterns of Group Collaborative Learning on Fourth-Grade Students’ Creative Thinking in a Digital Artificial Intelligence Course. Sustainability 2022, 14, 12674. https://doi.org/10.3390/su141912674

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Hu X, Liu Y, Huang J, Mu S. The Effects of Different Patterns of Group Collaborative Learning on Fourth-Grade Students’ Creative Thinking in a Digital Artificial Intelligence Course. Sustainability. 2022; 14(19):12674. https://doi.org/10.3390/su141912674

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Hu, Xiaoyong, Yue Liu, Jie Huang, and Su Mu. 2022. "The Effects of Different Patterns of Group Collaborative Learning on Fourth-Grade Students’ Creative Thinking in a Digital Artificial Intelligence Course" Sustainability 14, no. 19: 12674. https://doi.org/10.3390/su141912674

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