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Development of a Metacognitive Regulation-Based Collaborative Programming System and its Effects on Students' Learning Achievements, Computational Thinking Tendency and Group Metacognition

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Title: Development of a Metacognitive Regulation-Based Collaborative Programming System and its Effects on Students' Learning Achievements, Computational Thinking Tendency and Group Metacognition
Language: English
Authors: Wei Li (ORCID 0000-0002-4599-6433), Cheng-Ye Liu (ORCID 0000-0003-0732-0288), Judy C. R. Tseng (ORCID 0000-0002-3579-917X)
Source: British Journal of Educational Technology. 2024 55(1):318-339.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 22
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Foreign Countries, Junior High School Students, Metacognition, Academic Achievement, Thinking Skills, Computation, Programming, Cooperative Learning, Self Efficacy, Individual Differences, Group Behavior
Geographic Terms: Taiwan
DOI: 10.1111/bjet.13358
ISSN: 0007-1013
1467-8535
Abstract: Collaborative programming helps improve students' computational thinking and increases their confidence in solving programming problems. However, the effect of collaborative learning is not ideal because it is difficult for students to mobilize metacognition to regulate learning spontaneously. To guide students to effectively regulate the learning process when they collaborate to solve programming problems, this study develops a collaborative learning approach and a Collaborative programming System (MR-CPS) based on metacognitive regulation to support students' collaborative programming learning. A quasi-experimental study was conducted in a junior high school programming course in Taiwan to assess the effects on students. The impacts of MR-CPS from both individual and collaborative perspectives were investigated. Students' learning achievement and computational thinking tendencies were examined from an individual perspective. From a collaborative perspective, group self-efficacy and group metacognition were investigated. Participants were divided into MR-CPS (n = 115) and No-MR-CPS (n = 107). The MR-CPS group used the collaborative programming approach with metacognitive regulation mechanisms as the experimental group. In contrast, the No-MR-CPS group used the collaborative programming approach without metacognitive regulation mechanisms as the control group. The results show that the MR-CPS group statistically significantly outperformed the No-MR-CPS group in learning achievements. It was also found that the MR-CPS group had statistically significantly better computational thinking tendency, collective efficacy and metacognitive planning and evaluation skills than the No-MR-CPS group. This finding suggests that the MR-CPS has the potential to improve students' learning achievements, computational thinking tendency, group metacognition and collective efficacy. The study results have implications for the design of collaborative programming systems consistent with metacognitive regulation.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1408635
Database: ERIC
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  Value: <anid>AN0174977993;58i01jan.24;2024Jan25.05:06;v2.2.500</anid> <title id="AN0174977993-1">Development of a metacognitive regulation‐based collaborative programming system and its effects on students' learning achievements, computational thinking tendency and group metacognition </title> <p>Collaborative programming helps improve students' computational thinking and increases their confidence in solving programming problems. However, the effect of collaborative learning is not ideal because it is difficult for students to mobilize metacognition to regulate learning spontaneously. To guide students to effectively regulate the learning process when they collaborate to solve programming problems, this study develops a collaborative learning approach and a Collaborative programming System (MR‐CPS) based on metacognitive regulation to support students' collaborative programming learning. A quasi‐experimental study was conducted in a junior high school programming course in Taiwan to assess the effects on students. The impacts of MR‐CPS from both individual and collaborative perspectives were investigated. Students' learning achievement and computational thinking tendencies were examined from an individual perspective. From a collaborative perspective, group self‐efficacy and group metacognition were investigated. Participants were divided into MR‐CPS (n = 115) and No‐MR‐CPS (n = 107). The MR‐CPS group used the collaborative programming approach with metacognitive regulation mechanisms as the experimental group. In contrast, the No‐MR‐CPS group used the collaborative programming approach without metacognitive regulation mechanisms as the control group. The results show that the MR‐CPS group statistically significantly outperformed the No‐MR‐CPS group in learning achievements. It was also found that the MR‐CPS group had statistically significantly better computational thinking tendency, collective efficacy and metacognitive planning and evaluation skills than the No‐MR‐CPS group. This finding suggests that the MR‐CPS has the potential to improve students' learning achievements, computational thinking tendency, group metacognition and collective efficacy. The study results have implications for the design of collaborative programming systems consistent with metacognitive regulation.</p> <p>Keywords: collaborative learning; computational thinking; metacognition; metacognitive regulation; programming education</p> <hd id="AN0174977993-2">Practitioner notes</hd> <p>What is already known about this topic</p> <p></p> <ulist> <item> Collaborative programming has markedly improved students' programming learning performance and computational thinking. However, it is a complex process and is not easy to access.</item> <p></p> <item> Individual knowledge and metacognition skills are essential and challenging factors that could affect collaborative learning.</item> </ulist> <p>What this paper adds</p> <p></p> <ulist> <item> A collaborative programming approach based on metacognitive regulation was proposed to enhance students' academic performance during the learning process.</item> <p></p> <item> With the proposed approach, develop a collaborative programming system based on metacognitive regulation to guide students to focus on metacognitive regulation during the collaborative programming process.</item> <p></p> <item> The study results have implied that the Metacognitive Regulation‐based Collaborative Programming System (MR‐CPS) effectively improves students' learning achievements, computational thinking tendency, group metacognition and collective efficacy.</item> </ulist> <p>Implications for practice and/or policy</p> <p></p> <ulist> <item> The Metacognitive Regulation‐based Collaborative Programming System (MR‐CPS) is a potential approach to implementing collaborative programming activities.</item> <p></p> <item> The relationship between the shift in students' metacognitive‐regulated learning behaviours and their computational thinking development during collaborative programming activities deserves in‐depth study.</item> </ulist> <hd id="AN0174977993-3">INTRODUCTION</hd> <p>Researchers and educators have devoted significant attention to computational thinking (CT) because it is a twenty‐first‐century higher order thinking skill (Binkley et al., [<reflink idref="bib7" id="ref1">7</reflink>]) that is an integral part of modern research and problem solving in the STEM field of work (Wing, [<reflink idref="bib70" id="ref2">70</reflink>]). Programming, as a primary means of developing CT (Arfé et al., [<reflink idref="bib3" id="ref3">3</reflink>]; Román‐González et al., [<reflink idref="bib52" id="ref4">52</reflink>]; Wing, [<reflink idref="bib70" id="ref5">70</reflink>]), is considered an excellent way to create computational work and demonstrate computational thinking skills (Grover & Pea, [<reflink idref="bib22" id="ref6">22</reflink>]). Researchers argue that learning programming allows students to think and act using skills related to computational thinking (eg, problem solving, decomposition, pattern abstraction, identification, testing and evaluation ... etc) (Buitrago Flórez et al., [<reflink idref="bib9" id="ref7">9</reflink>]; Polat & Yilmaz, [<reflink idref="bib50" id="ref8">50</reflink>]; Shute et al., [<reflink idref="bib59" id="ref9">59</reflink>]), thus enhancing students' computational thinking.</p> <p>In addition, researchers believe that collaborative programming is conducive to developing students' computational thinking (Denner et al., [<reflink idref="bib18" id="ref10">18</reflink>]; Wu et al., [<reflink idref="bib71" id="ref11">71</reflink>]). Collaborative programming provides opportunities for students to communicate and share ideas, allows students to see different approaches to problem solving and helps students generate better ideas, thereby reducing deficiencies in programming solutions and improving computational thinking knowledge and skills (Wei et al., [<reflink idref="bib68" id="ref12">68</reflink>]). In collaborative learning, learners discover gaps between each other and gain new insights from their partners' experiences and knowledge through interactions with group members, such as communicating, sharing, arguing and resolving conflicts, leading to knowledge construction (Altintas et al., [<reflink idref="bib2" id="ref13">2</reflink>]; Cai & Gu, [<reflink idref="bib10" id="ref14">10</reflink>]).</p> <p>Although collaborative programming has many benefits for students' development (Shadiev et al., [<reflink idref="bib58" id="ref15">58</reflink>]; Tsompanoudi et al., [<reflink idref="bib65" id="ref16">65</reflink>]; Zheng et al., [<reflink idref="bib73" id="ref17">73</reflink>]), some challenges remain. Due to some students' inadequate metacognitive knowledge and abilities (Cho & Jonassen, [<reflink idref="bib14" id="ref18">14</reflink>]), students have difficulty using effective strategies when faced with complex topics. They cannot monitor and reflect on the learning process (Mayer, [<reflink idref="bib44" id="ref19">44</reflink>]), which results in poor collaborative learning and CT performance, such as patching and guessing (Wu et al., [<reflink idref="bib71" id="ref20">71</reflink>]). Furthermore, students struggle to build a shared understanding with group members when negotiating discussions (Wu et al., [<reflink idref="bib71" id="ref21">71</reflink>]), leading to process loss (Demir & Seferoglu, [<reflink idref="bib17" id="ref22">17</reflink>]). Therefore, there is a need for metacognitive guidance when students are engaged in collaborative learning.</p> <p>Previous research has shown that providing students with effective support for metacognitive regulation mechanisms (eg, self‐planning, self‐monitoring and self‐reflection) in a collaborative learning environment can stimulate more metacognitive behaviours and enhance learning achievements and collective efficacy (Zheng et al., [<reflink idref="bib73" id="ref23">73</reflink>]; Zhong et al., [<reflink idref="bib74" id="ref24">74</reflink>]). As beginners in programming languages, junior high school students may lack the necessary metacognitive skills to regulate collaborative learning effectively (Hadwin et al., [<reflink idref="bib24" id="ref25">24</reflink>]). Therefore, giving students appropriate guidance and encouraging them to pay attention to the learning process is vital to make more accurate cognitive judgements during collaborative programming.</p> <p>Therefore, this study developed a collaborative programming approach and a learning system based on metacognitive regulation. The approach improved the learning effectiveness of collaborative programming by guiding students to set goals, monitor the collaborative learning process and self‐evaluate. A quasi‐experimental study was conducted to assess the system's effectiveness in promoting students' learning achievements, computational thinking tendency, group metacognition awareness and collective efficacy. The following four research questions were examined:</p> <p></p> <ulist> <item> <bold> RQ1 – </bold> Can the MR‐CPS significantly improve students' learning achievements compared to the No‐MR‐CPS?</item> <p></p> <item> <bold> RQ2 – </bold> Can the MR‐CPS significantly improve students' computational thinking tendency compared to the No‐MR‐CPS?</item> <p></p> <item> <bold> RQ3 – </bold> Can the MR‐CPS significantly improve students' group metacognition compared to the No‐MR‐CPS?</item> <p></p> <item> <bold> RQ4 – </bold> Can the MR‐CPS significantly improve students' collective efficacy compared to the No‐MR‐CPS?</item> </ulist> <hd id="AN0174977993-4">LITERATURE REVIEW</hd> <p></p> <hd id="AN0174977993-5">Computational thinking and programming</hd> <p>Computational thinking (CT) is generally considered a cognitive skill using computational methods and tools to solve problems (Lye & Koh, [<reflink idref="bib41" id="ref26">41</reflink>]; Wing, [<reflink idref="bib70" id="ref27">70</reflink>]). CT has become a fundamental literacy and skill everyone must possess today (Shute et al., [<reflink idref="bib59" id="ref28">59</reflink>]). As a result, researchers and educators are increasingly interested in incorporating CT into K‐12 education (Lai & Wong, [<reflink idref="bib38" id="ref29">38</reflink>]), hoping that future citizens can apply computational thinking knowledge and skills in a wide range of contexts (Boom et al., [<reflink idref="bib8" id="ref30">8</reflink>]).</p> <p>Programming education is the most commonly used method for developing CT (Lye & Koh, [<reflink idref="bib41" id="ref31">41</reflink>]) because coding allows for thinking and acting using students' CT skills (eg, problem solving, decomposition, pattern abstraction and recognition, debugging and evaluation) (Buitrago Flórez et al., [<reflink idref="bib9" id="ref32">9</reflink>]; Polat & Yilmaz, [<reflink idref="bib50" id="ref33">50</reflink>]; Shute et al., [<reflink idref="bib59" id="ref34">59</reflink>]). Previous research has also validated the impact of programming on promoting K‐12 students' CT skills (Grover & Pea, [<reflink idref="bib22" id="ref35">22</reflink>]). For example, Sáez‐López et al. ([<reflink idref="bib54" id="ref36">54</reflink>]) taught a programming course to secondary school students, who showed improvements in computational thinking and computational practices.</p> <p>However, most learners still consider programming complex and challenging (Akinola, [<reflink idref="bib1" id="ref37">1</reflink>]; Cheng et al., [<reflink idref="bib13" id="ref38">13</reflink>]; Kucuk & Sisman, [<reflink idref="bib35" id="ref39">35</reflink>]). Programming as a logic and problem‐solving activity requires complex thinking skills that students often find difficult to perform independently (Shadiev et al., [<reflink idref="bib58" id="ref40">58</reflink>]). Therefore, finding an appropriate pedagogical approach to help students learn to program better is challenging for computational thinking education.</p> <hd id="AN0174977993-6">Collaborative programming</hd> <p>Collaborative programming is an approach to teaching programming (Shadiev et al., [<reflink idref="bib58" id="ref41">58</reflink>]). This pedagogical approach stems from cognitive and social constructivist learning theories (Kalaian & Kasim, [<reflink idref="bib30" id="ref42">30</reflink>]). The theory suggests that in collaborative programming, a group of students negotiates and explores programming problems, which can trigger cognitive conflict and knowledge transfer, ultimately leading to knowledge construction. Collaborative programming is particularly useful in developing CT skills and knowledge of computational programming (Denner et al., [<reflink idref="bib18" id="ref43">18</reflink>]; Iskrenovic‐Momcilovic, [<reflink idref="bib27" id="ref44">27</reflink>]). For example, Zhong et al. ([<reflink idref="bib74" id="ref45">74</reflink>]) found that a collaborative programming learning approach helped enrich primary school students' programming knowledge and develop their CT and creative thinking skills. Collaborative programming learning environments also reduce students' negative emotions and enhance their self‐efficacy (Garcia, [<reflink idref="bib21" id="ref46">21</reflink>]; Wei et al., [<reflink idref="bib68" id="ref47">68</reflink>]). In addition, researchers often use technology tools to support collaborative programming (Rum & Ismail, [<reflink idref="bib53" id="ref48">53</reflink>]; Zheng et al., [<reflink idref="bib73" id="ref49">73</reflink>]). Due to the support of technology for learning (Gallagher & Gallagher, [<reflink idref="bib20" id="ref50">20</reflink>]), collaborative programming effectively improves students' cognitive performance and motivation (Shadiev et al., [<reflink idref="bib58" id="ref51">58</reflink>]; Tsompanoudi et al., [<reflink idref="bib65" id="ref52">65</reflink>]).</p> <p>However, collaborative learning is not always successful when students have opportunities to work together (Kreijns et al., [<reflink idref="bib34" id="ref53">34</reflink>]). Similarly, Soller ([<reflink idref="bib61" id="ref54">61</reflink>]) reported that simply dividing students into different groups does not necessarily lead to effective social interaction. Successful collaborative learning depends on the efforts of everyone. In other words, learners need to have a plan for their learning and be able to monitor, coordinate and evaluate their learning activities during collaborative programming (Jeong & Hmelo‐Silver, [<reflink idref="bib29" id="ref55">29</reflink>]). Nevertheless, researchers have found that individuals often fail to adequately control and monitor their cognitive learning activities during collaborative processes (Barron, [<reflink idref="bib5" id="ref56">5</reflink>]; Hadwin & Oshige, [<reflink idref="bib23" id="ref57">23</reflink>]). Therefore, it is necessary to use appropriate regulation strategies to enhance the effectiveness of collaborative learning by guiding learners to plan appropriately, monitor the process and reflect on the effects of collaborative programming.</p> <hd id="AN0174977993-7">Metacognitive regulation</hd> <p>Metacognition refers to "cognition of cognition", which is the knowledge about the regulation of cognitive activities during learning (Flavell, [<reflink idref="bib19" id="ref58">19</reflink>]). Metacognition coordinates learning by planning, monitoring and evaluating cognitive processes (Schraw & Moshman, [<reflink idref="bib56" id="ref59">56</reflink>]), and it is a cognitive‐level regulatory activity (De Backer et al., [<reflink idref="bib16" id="ref60">16</reflink>]; Flavell, [<reflink idref="bib19" id="ref61">19</reflink>]). Self‐regulation is the awareness and behaviour students adopt during learning, involving metacognitive, motivational and behavioural components (Zimmerman, [<reflink idref="bib75" id="ref62">75</reflink>]). Although metacognition is not the same concept as self‐regulation, several researchers have argued that metacognition plays a crucial role in self‐regulation (White, [<reflink idref="bib69" id="ref63">69</reflink>]; Zepeda & Nokes‐Malach, [<reflink idref="bib72" id="ref64">72</reflink>]). To be more precise, metacognition is made up of two key elements. One is metacognitive knowledge, which is awareness of our thinking processes and what we know. The other is metacognitive regulation, which refers to the activities we engage in to facilitate learning (Sandi‐Urena et al., [<reflink idref="bib55" id="ref65">55</reflink>]; Tanner, [<reflink idref="bib63" id="ref66">63</reflink>]).</p> <p>The importance of metacognitive regulation for individual and collaborative learning performance is well supported by relevant research. For example, Mohd Rum and Ismail ([<reflink idref="bib45" id="ref67">45</reflink>]) found that learners with metacognitive scaffolding outperformed learners without metacognitive scaffolding. Moreover, metacognitive scaffolds during collaborative learning can improve students' learning achievement and programming skills (Zheng et al., [<reflink idref="bib73" id="ref68">73</reflink>]). Kwon et al. ([<reflink idref="bib36" id="ref69">36</reflink>]) provided learners with a metacognitive scaffold in collaborative learning that can promote collaborative interaction and improve students' personal and collaborative performance by helping students complete planning, monitoring and reflection. Computational thinking is a process that involves formulating problems and solutions (Wing, [<reflink idref="bib70" id="ref70">70</reflink>]) and metacognition plays a central role in problem solving (Zepeda & Nokes‐Malach, [<reflink idref="bib72" id="ref71">72</reflink>]). To solve a problem, not only do the various stages of problem solving need to be executed, but they also need to be consciously tracked, regulated and controlled. One study found that students' computational thinking is positively related to metacognitive regulation ability (Şen, [<reflink idref="bib57" id="ref72">57</reflink>]). Through metacognitive prompts, students' computational thinking can be significantly improved (Chen et al., [<reflink idref="bib12" id="ref73">12</reflink>]).</p> <p>Although collaborative programming allows students to acquire programming knowledge and develop higher order thinking, research has shown that students need metacognitive regulation during collaborative learning activities to monitor their cognitive situation and make more rational decisions (Koriat et al., [<reflink idref="bib33" id="ref74">33</reflink>]). Past research on metacognitive regulation has focused on individual learning (Järvelä et al., [<reflink idref="bib28" id="ref75">28</reflink>]). The same attention needs to be paid to students' metacognitive regulation skills in a collaborative learning environment (Jeong & Hmelo‐Silver, [<reflink idref="bib29" id="ref76">29</reflink>]) because in relatively open collaborative learning environments, students frequently struggle to apply sufficient metacognitive regulation skills spontaneously and therefore struggle to achieve satisfactory learning outcomes (Manlove et al., [<reflink idref="bib43" id="ref77">43</reflink>]). In other words, students may not use metacognitive skills to regulate their cognitive learning activities during collaborative processes (Barron, [<reflink idref="bib5" id="ref78">5</reflink>]). Moreover, the more the complex programming activities are, the more metacognitive skills are required (Nurulain Mohd Rum & Zolkepli, [<reflink idref="bib46" id="ref79">46</reflink>]).</p> <p>The process of collaborative learning generally involves three levels of metacognitive regulation, namely, metacognitive regulation of individual student cognition, co‐regulation and socially shared regulation (De Backer et al., [<reflink idref="bib16" id="ref80">16</reflink>]; Iiskala et al., [<reflink idref="bib26" id="ref81">26</reflink>]). However, the main subject of metacognition is still the individual, regardless of whether the individual works in a team or independently (Kim et al., [<reflink idref="bib31" id="ref82">31</reflink>]). This study developed a collaborative programming approach and learning system based on individual metacognitive regulation to guide learners to activate metacognition in collaborative programming, thereby improving collaborative learning outcomes.</p> <hd id="AN0174977993-8">THE DEVELOPMENT OF A METACOGNITIVE REGULATION‐BASED COLLABORATIVE PROGRAMMING SYSTEM</hd> <p>Flavell ([<reflink idref="bib19" id="ref83">19</reflink>]) argued that the critical skills of metacognitive regulation are planning, monitoring and evaluation, which occurs before starting the task, during the execution and after the job respectively. Based on Flavell's ([<reflink idref="bib19" id="ref84">19</reflink>]) work, this study proposed a metacognitive regulation‐based collaborative programming approach to enhance the learning effect of collaborative programming and students' computational thinking and metacognition. As shown in Figure 1, the approach consists of seven stages. Among them, 'Set learning goals,' 'self‐monitoring' and 'self‐evaluation' are the three critical steps of metacognitive regulation and supervision that can occur at any stage of learning. Metacognitive regulation strategies are highlighted in blue in Figure 1. Furthermore, metacognitive regulation is an iterative loop process. That is, the evaluation results may influence the goal setting for the next task.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0001.jpg" title="1 Metacognitive regulation‐based collaborative programming approach.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>At the beginning of the learning activity, the teacher presents the learning task and defines the problem by creating a real problem situation. Then, the students clarify the problems to be solved and set their individual learning goals from learning achievement, learning strategy selection and collaborative activities to participate in. After that, they engage in group discussions and design algorithms for the solution, which could be represented by drawing a programming flowchart. After the students have come up with a solution to the problem, they implement the solution's algorithm through collaborative programming and solve problems together after running debugging and modifying their programs continuously. Finally, students are guided to self‐evaluate and reflect on their collaborative process and performance to adjust to the next programming learning activity. Throughout the collaborative process, students are guided to self‐monitor their learning. Throughout the learning process, teachers need to observe students' collaborative status, prompt them to complete each collaborative task and provide guidance on any problems they encounter. In addition, after students submit their programming assignments, teachers need to assess them promptly and give students feedback and learning advice.</p> <p>Based on the metacognitive regulation‐based collaborative programming approach, the study developed a collaborative programming system to guide students in metacognitive regulation during collaborative programming. Figure 2 shows the system architecture. It mainly consists of a collaborative learning module and a metacognitive regulation module. The collaborative learning module supports students' collaborative activities, and the metacognitive regulation module provides a metacognitive scaffold to guide students in regulating and monitoring their cognitive activities during the problem‐solving process. In addition, the collaborative programming environment includes an identity verification module, a task assignment module, a task guidance module, a metacognitive scaffolding editing module, a teacher evaluation module, a learning performance visualization module and databases. Teachers can turn off or on the metacognitive regulation module. In other words, the system has two modes. One is computer‐supported collaborative programming with the metacognitive regulation module. The other is the conventional computer‐supported collaborative programming mode, which does not allow the use of the metacognitive regulation module.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0002.jpg" title="2 System architecture of collaborative programming based on metacognitive regulation.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>First, the instructor logs into the learning system through the identity verification module. Before students can work on the activities, teachers need to create courses, publish learning tasks and group students through the learning task assignment module. In addition, the learning system has preset some metacognitive regulation questions. Teachers can also set other metacognitive regulation questions in the metacognitive regulation editing module according to the requirements of the learning content. After the instructor completes the task assignment and metacognitive scaffolding setup, students can log into the system and begin learning. Students are guided through the task guidance module to select the programming task they need to complete and learn about the requirements, the knowledge points involved and the deadline for submitting the assignment. The interface for collaborative programming tasks is shown in Figure 3.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0003.jpg" title="3 The task interface.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>After students understand the learning task, the system guides them to the metacognitive regulation module to set learning goals from individual and collaborative perspectives. The system guides students to use metacognition from an individual perspective through two questions: "How many points do I want to get?" and "What learning strategies do I plan to use?". The system also guides students to use metacognition from a collaborative perspective through the question, "What tasks do I want to be actively involved in during collaborative programming?". Each question will provide some options for students to choose from, guiding them to set their learning goals. Students must finish the goal setting before collaborating in a learning activity. The learning goal‐setting interface is shown in Figure 4. The system records the student's options and compares them when the student completes the self‐evaluation phase. When evaluating students, teachers will give learning suggestions considering the goals they set and the performance they achieved.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0004.jpg" title="4 The learning goal‐setting interface.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>Next, students move to the collaborative learning module. The collaborative learning module consists of three learning mechanisms: the discussion mechanism, the collaborative flowcharting mechanism and the collaborative programming mechanism. The system allows students to collaborate on flowcharting and programming to complete a task simultaneously. During the problem‐solving process of completing a task, students can discuss the problems encountered at any time and solve the problem together. Different from other collaborative learning platforms/systems, the system provides a dual space where the problem solving and the social interaction spaces are closely integrated. Some researchers suggested constructing a dual‐interaction space, where one is a workspace, and the other is an online chat room (Perit Çakır et al., [<reflink idref="bib49" id="ref85">49</reflink>]). In this way, students can effectively organize the social and cognitive processes involved in problem‐solving activities. The interface of the collaborative programming and discussing interface is shown in Figure 5.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0005.jpg" title="5 Collaborative programming and discussing interface.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>Researchers argued that if students do not know which strategies are available and when to use them, students will not be able to implement them in groups (Lyons et al., [<reflink idref="bib42" id="ref86">42</reflink>]). Therefore, the monitoring mechanism of this system differs from other metacognitive moderation systems in that monitoring is done by giving appropriate question prompts during the progress of collaborative programming, guiding students to monitor their collaborative learning process. For example, during group discussions, the system prompts, "Did you participate actively in the group discussion and present your ideas?" When writing code collaboratively, the system prompts, "Did you participate in writing code collaboratively?" Students need not answer these questions. The prompts only serve as gentle reminders to help students monitor their collaborative learning process and guide them towards their goals. The interface of self‐monitoring is shown in the lower‐left corner of Figure 5.</p> <p>After students complete their collaborative programming tasks and submit their assignments, the instructor evaluates them through the evaluation module. Students can view their group's performance through the learning performance visualization module. Finally, the system guides students to self‐evaluate and reflect on their learning performance. As shown in Figure 6, each student completes four self‐evaluation questions: How many points do I think I can score for this collaborative performance? What strategies or methods did I use in this collaborative programming activity? What learning activities did I engage in during this collaborative programming learning? Based on the above reflections, how can I improve my next task?</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0006.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0006.jpg" title="6 Self‐evaluation interface.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <hd id="AN0174977993-15">METHODOLOGY</hd> <p></p> <hd id="AN0174977993-16">Participants</hd> <p>The participants were 222 students in their third year of junior high school in Hsinchu City, Taiwan, with an average age of 15. Coming from eight different classes, they all had enrolled in a junior high school IT course for 2.5 years and were all familiar with the basic operation of computers. There were no significant differences in the students' pretest (<emph>F</emph> = 0.85, <emph>p</emph> = 0.36 > 0.05), pre‐computational thinking tendency (<emph>F</emph> = 0.28, <emph>p</emph> = 0.36 > 0.05), pre‐group metacognition (<emph>F</emph> = 6.25, <emph>p</emph> = 0.31 > 0.05) and pre‐collective efficacy (<emph>F</emph> = 3.90, <emph>p</emph> = 0.06 > 0.05). These results implied that students were at similar levels. Four classes of 115 students (60 boys, 55 girls) were assigned randomly as the MR‐CPS group, and another four classes of 107 students (52 boys, 55 girls) were the No‐MR‐CPS group. During the experiment, all students were randomly assigned to groups of 3–5 students to participate in collaborative programming activities. All groups were taught by the same instructor with over 5 years of experience teaching programming.</p> <p>Participants volunteered to take part in this study. The study provided participants and their parents with an informed consent form explaining the experiment's details. The data collected were kept confidential, and coding was used to protect the names of the subjects from being made public. The experiment lasted from April to June 2022.</p> <hd id="AN0174977993-17">Instrument</hd> <p>The measurement instruments used in this study included the pretest, posttest and questionnaires on computational thinking tendency, group metacognition and collective efficacy. The purpose of the pretest was to assess learners' knowledge before the experiment, while the purpose of the posttest was to evaluate their learning achievement at the end of the experiment session. The pretest and posttest were designed by two junior schoolteachers with over 10 years of programming teaching experience through discussion. The teaching content was determined by negotiation between the experimenters and the instructor. Both tests consisted of 10 multiple‐choice questions and were scored out of 100. Their main content is the basic programming concepts (data types, arithmetic operators, expressions, input and output functions, sequential, branch and loop structure, etc).</p> <p>The computational thinking tendency questionnaire was adapted from a measure developed by Hwang et al. ([<reflink idref="bib25" id="ref87">25</reflink>]) to examine the impact of the MR‐CPS on students' computational thinking tendencies. It consists of six items (eg, I can usually develop a step‐by‐step procedure for solving complex problems), and Cronbach's α of the adapted questionnaire was 0.84.</p> <p>The group metacognition questionnaire was employed to clarify learners' knowledge of cognition and metacognitive skills during collaborative learning. The group metacognition questionnaire used in this study was adapted from the measure developed by Biasutti and Frate ([<reflink idref="bib6" id="ref88">6</reflink>]). Previous studies have used it to assess group metacognition for K‐12 students (Socratous & Ioannou, [<reflink idref="bib60" id="ref89">60</reflink>]). The questionnaire was constructed of 18 items based on four dimensions: knowledge of cognition, planning skill, monitoring skill and evaluation skill (eg, I know how to use the material.). The total Cronbach's α of the adapted questionnaire was 0.89, and Cronbach's α of the four dimensions were 0.77, 0.80, 0.72 and 0.71 respectively.</p> <p>The collective efficacy questionnaire was adapted from a measure developed by Wang and Lin ([<reflink idref="bib67" id="ref90">67</reflink>]) to investigate the impact of the MR‐CPS on students' collective efficacy. It consists of eight items (eg, I believe that our group can achieve a superior outcome for this learning task), and Cronbach's α was 0.91.</p> <p>All questionnaires were administered using a 5‐point scale, where 1–5 represented, in order, <emph>complete disagreement</emph>, <emph>disagreement</emph>, <emph>neutrality</emph>, <emph>agreement</emph> and <emph>complete agreement</emph>.</p> <hd id="AN0174977993-18">Experimental procedure</hd> <p>As shown in Figure 7, the experimental course lasted 7 weeks, with 45 minutes of class time per week. In the first week, students in both classes completed a pretest with a prequestionnaire. Subsequently, the instructor introduced the students to the learning approach and the collaborative programming system to ensure that both classes understood the collaborative programming system for this study.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01jan24/bjet13358-fig-0007.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet13358-fig-0007.jpg" title="7 Experimental procedure diagram.[Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>Formal programming instruction took place from week 2 to week 6. In each class, the instructor taught the appropriate basic programming knowledge. Then, the MR‐CPS group performed collaborative programming activities under the metacognitive regulation‐based collaborative programming system, while the No‐MR‐CPS group learned with the conventional collaborative programming system. Each group needed to complete Task 1 (design a simple calculator) within 2 weeks and Task 2 (design a complex calculator) within 3 weeks. Students in the MR‐CPS group were required to complete metacognitive conditioning activities in the same amount of learning time. A detailed description of the tasks and schedule is given in Table 1. In the 7th week, all students completed a posttest and postquestionnaires.</p> <p>1 TABLE Tasks and schedule for collaborative programming activities.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Tasks</th><th align="left">Major program concepts</th><th align="left">Description</th><th align="left">Schedule</th></tr></thead><tbody valign="top"><tr><td>Task 1 Design a simple calculator</td><td><list list-type="Bullet"><list-item><p>Data Types</p></list-item><list-item><p>Arithmetic operators</p></list-item><list-item><p>Expressions</p></list-item><list-item><p>Input and output functions</p></list-item><list-item><p>Sequential structure</p></list-item></list></td><td>Students shall collaborate to complete an algorithm flowchart and a simple calculator program, which should allow users to enter two positive numbers and perform addition and subtraction operations in Python.</td><td>Weeks 2–3</td></tr><tr><td>Task 2 Design a complex calculator</td><td><list list-type="Bullet"><list-item><p>Data Types</p></list-item><list-item><p>Arithmetic operators</p></list-item><list-item><p>Expressions</p></list-item><list-item><p>Input and output functions</p></list-item><list-item><p>Sequential structure</p></list-item><list-item><p>Branch and Loop structure</p></list-item></list></td><td>Students shall work together to complete an algorithm flowchart and a program of a complex calculator, which can calculate the sum, the subtraction, the multiplication, the division, the average of two numbers and the square of one number in Python.</td><td>Weeks 4–6</td></tr></tbody></table> </ephtml> </p> <hd id="AN0174977993-20">Data analysis</hd> <p>SPSS 26 statistical software was used to analyse the data in this study. The quantitative data were assessed for conformity to a normal distribution by looking at Q–Q plots of the data distribution and the Shapiro–Wilk test. The results showed that all experimental data were normally distributed. In addition, Levene's test was used to test the homogeneity of variance of the quantitative experimental data. The results showed that the hypothesis of homogeneity of variance was valid for the learning achievements (<emph>F</emph> = 0.099, <emph>p</emph> = 0.753), computational thinking tendency (<emph>F</emph> = 0.243, <emph>p</emph> = 0.624), group metacognition (<emph>F</emph> = 2.408, <emph>p</emph> = 0.122) and group efficacy (<emph>F</emph> = 0.147, <emph>p</emph> = 0.702), demonstrating that it was possible to use analysis of variance.</p> <p>To explore the effect of the proposed learning method on the students, an independent <emph>t</emph>‐test was used to analyse the pretest and the prequestionnaire before the experiment. The results showed no significant difference between the two groups, as shown in Table 2. In other words, they could be considered parallel groups. After the experiment, to exclude the effects of the pretest and prequestionnaire results, one‐way ANCOVA was used to analyse the students' learning achievements, computational thinking tendencies, group metacognition and collective efficacy. The independent variable was the learning approach, while pretest and prequestionnaire ratings were the covariates, and posttest scores and postquestionnaire ratings were the dependent variable.</p> <p>2 TABLE The t ‐test analysis of the pretest and postquestionnaire.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variable</th><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left"><italic>t</italic></th></tr></thead><tbody valign="top"><tr><td>Pre‐LA</td><td>MR‐CPS</td><td>115</td><td>42.783</td><td>21.665</td><td>0.923</td></tr><tr><td>No‐MR‐CPS</td><td>107</td><td>45.327</td><td>19.197</td><td /></tr><tr><td>Pre‐CTt</td><td>MR‐CPS</td><td>115</td><td>3.103</td><td>0.790</td><td>−0.525</td></tr><tr><td>No‐MR‐CPS</td><td>107</td><td>3.050</td><td>0.709</td><td /></tr><tr><td>Pre‐GM</td><td>MR‐CPS</td><td>115</td><td>3.735</td><td>0.532</td><td>−2.501</td></tr><tr><td>No‐MR‐CPS</td><td>107</td><td>3.567</td><td>0.464</td><td /></tr><tr><td>Pre‐CE</td><td>MR‐CPS</td><td>115</td><td>3.662</td><td>0.788</td><td>−1.974</td></tr><tr><td>No‐MR‐CPS</td><td>107</td><td>3.468</td><td>0.661</td><td /></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: CE, collective efficacy; CTt, computational thinking tendency; GM, group metacognition; LA, learning achievements.</p> <hd id="AN0174977993-21">RESULTS</hd> <p></p> <hd id="AN0174977993-22">Learning achievements</hd> <p>The one‐way ANCOVA results for posttest learning achievements are shown in Table 3. The adjusted mean scores for the experimental and control classes were <emph>M</emph> = 54.005 and <emph>M</emph> = 47.752. According to the results, the MR‐CPS group had significantly higher learning achievements than the No‐MR‐CPS group (<emph>F</emph> (<reflink idref="bib1" id="ref91">1</reflink>, 219) = 4.344, <emph>p</emph> = 0.038) with a smaller effect size (<emph>η</emph><sups>2</sups> = 0.019) (Cohen, [<reflink idref="bib15" id="ref92">15</reflink>]). The results imply that the MR‐CPS is better at improving learning achievements in collaborative programming.</p> <p>3 TABLE The one‐way ANCOVA analysis of learning achievements.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left">Adjusted mean</th><th align="left">SE</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>η</italic><sup>2</sup></th></tr></thead><tbody valign="top"><tr><td>MR‐CPS group</td><td>115</td><td>53.478</td><td>24.281</td><td>54.005</td><td>2.081</td><td>4.344<xref ref-type="fn" rid="tfn2" /></td><td>0.038</td><td>0.019</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>48.318</td><td>23.532</td><td>47.752</td><td>2.154</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>2 * <emph>p</emph> < 0.05.</p> <hd id="AN0174977993-23">Computational thinking tendency</hd> <p>To evaluate the effects of the different learning approaches on computational thinking tendencies, the results of the one‐way ANCOVA are shown in Table 4. The MR‐CPS group had significantly higher computational thinking tendencies than the No‐MR‐CPS group (<emph>F</emph> (<reflink idref="bib1" id="ref93">1</reflink>, 219) = 12.404, <emph>p</emph> = 0.001), with a small‐to‐medium effect size (<emph>η</emph><sups>2</sups> = 0.054 < 0.059) (Cohen, [<reflink idref="bib15" id="ref94">15</reflink>]). The adjusted mean scores for the MR‐CPS and No‐MR‐CPS groups were <emph>M</emph> = 3.494 and <emph>M</emph> = 3.175 respectively. Based on these results, we can conclude that learners using the MR‐CPS had better computational thinking tendency.</p> <p>4 TABLE The one‐way ANCOVA analysis of computational thinking tendency.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left">Adjusted mean</th><th align="left">SE</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>η</italic><sup>2</sup></th></tr></thead><tbody valign="top"><tr><td>MR‐CPS group</td><td>115</td><td>3.506</td><td>0.778</td><td>3.494</td><td>0.063</td><td>12.404<xref ref-type="fn" rid="tfn3" /></td><td>0.001</td><td>0.054</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.162</td><td>0.743</td><td>3.175</td><td>0.065</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>3 ** <emph>p</emph> < 0.01.</p> <hd id="AN0174977993-24">Group metacognition</hd> <p>In terms of group metacognition, the results of the one‐way ANOVA are presented in Table 5. The MR‐CPS group had significantly higher group metacognition than the No‐MR‐CPS group (<emph>F</emph> (<reflink idref="bib1" id="ref95">1</reflink>, 219) = 5.421, <emph>p</emph> = 0.021), with a small effect size (<emph>η</emph><sups>2</sups> = 0.024) (Cohen, [<reflink idref="bib15" id="ref96">15</reflink>]). The adjusted mean for the MR‐CPS and No‐MR‐CPS groups was <emph>M</emph> = 3.851 and <emph>M</emph> = 3.697 respectively. That is, the MR‐CPS can better promote students' group metacognition development.</p> <p>5 TABLE The one‐way ANCOVA analysis of group metacognition.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left">Adjusted mean</th><th align="left">SE</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>η</italic><sup>2</sup></th></tr></thead><tbody valign="top"><tr><td>MR‐CPS group</td><td>115</td><td>3.891</td><td>0.581</td><td>3.851</td><td>0.046</td><td>5.421<xref ref-type="fn" rid="tfn4" /></td><td>0.021</td><td>0.024</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.653</td><td>0.504</td><td>3.697</td><td>0.047</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>4 * <emph>p</emph> < 0.05.</p> <p>Table 6 further shows the students' levels in each dimension of group metacognition. There were significant differences between students in the MR‐CPS and No‐MR‐CPS groups for planning and evaluation awareness. The planning awareness of the MR‐CPS group was significantly better than those of the No‐MR‐CPS groups (<emph>F</emph>(<reflink idref="bib1" id="ref97">1</reflink>, 219) = 5.845, <emph>p</emph> = 0.016) with a smaller effect size (<emph>η</emph><sups>2</sups> = 0.026) (Cohen, [<reflink idref="bib15" id="ref98">15</reflink>]). The adjusted mean scores for the MR‐CPS and No‐MR‐CPS groups were <emph>M</emph> = 3.827 and <emph>M</emph> = 3.627 respectively. In addition, the evaluation awareness of the MR‐CPS group was significantly better than those of the No‐MR‐CPS group (<emph>F</emph>(<reflink idref="bib1" id="ref99">1</reflink>, 219) = 6.917, <emph>p</emph> = 0.009), with a smaller effect size (<emph>η</emph><sups>2</sups> = 0.031) (Cohen, [<reflink idref="bib15" id="ref100">15</reflink>]). The adjusted mean scores for the MR‐CPS and No‐MR‐CPS groups were <emph>M</emph> = 3.908 and <emph>M</emph> = 3.699 respectively. However, there were no significant differences between the two groups in terms of knowledge of cognition (<emph>F</emph>(<reflink idref="bib1" id="ref101">1</reflink>, 219) = 1.915, <emph>p</emph> = 0.168) or statistically significant differences in terms of monitoring awareness (<emph>F</emph>(<reflink idref="bib1" id="ref102">1</reflink>,<reflink idref="bib219" id="ref103">219</reflink>) = 3.709, <emph>p</emph> = 0.550). In other words, the MR‐CPS was effective in terms of improving students' planning and evaluation awareness.</p> <p>6 TABLE The one‐way ANCOVA analysis of group metacognition in each dimension.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Dimensions</th><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left">Adjusted mean</th><th align="left"><italic>SE</italic></th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>η</italic><sup>2</sup></th></tr></thead><tbody valign="top"><tr><td>Planning</td><td>MR‐CPS group</td><td>115</td><td>3.853</td><td>0.698</td><td>3.827</td><td>0.057</td><td>5.845<xref ref-type="fn" rid="tfn5" /></td><td>0.016</td><td>0.026</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.598</td><td>0.593</td><td>3.627</td><td>0.060</td><td /><td /><td /></tr><tr><td>Evaluation</td><td>MR‐CPS group</td><td>115</td><td>3.919</td><td>0.669</td><td>3.908</td><td>0.055</td><td>6.917<xref ref-type="fn" rid="tfn6" /></td><td>0.009</td><td>0.031</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.687</td><td>0.576</td><td>3.699</td><td>0.057</td><td /><td /><td /></tr><tr><td>Knowledge of cognition</td><td>MR‐CPS group</td><td>115</td><td>3.864</td><td>0.670</td><td>3.799</td><td>0.053</td><td>1.915</td><td>0.168</td><td /></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.622</td><td>0.645</td><td>3.692</td><td>0.055</td><td /><td /><td /></tr><tr><td>Monitoring</td><td>MR‐CPS group</td><td>115</td><td>3.928</td><td>0.635</td><td>3.899</td><td>0.060</td><td>3.709</td><td>0.550</td><td /></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.706</td><td>0.659</td><td>3.737</td><td>0.058</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>5 * <emph>p</emph> < 0.05</item> <item>6 ** <emph>p</emph> < 0.01.</item> </ulist> <hd id="AN0174977993-25">Collective efficacy</hd> <p>Table 7 shows the one‐way ANCOVA results for collective efficacy. Students in the MR‐CPS group had significantly higher collective efficacy than the No‐MR‐CPS group (<emph>F</emph>(<reflink idref="bib1" id="ref104">1</reflink>, 219) = 12.694, <emph>p</emph> < 0.001) with a small‐to‐medium effect size (<emph>η</emph><sups>2</sups> = 0.055 < 0.059) (Cohen, [<reflink idref="bib15" id="ref105">15</reflink>]). The adjusted mean scores for the MR‐CPS and No‐MR‐CPS groups were <emph>M</emph> = 3.852 and <emph>M</emph> = 3.481 respectively. These results suggest that the MR‐CPS better enhances students' collective efficacy.</p> <p>7 TABLE The one‐way ANCOVA analysis of collective efficacy.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Groups</th><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left"><italic>SD</italic></th><th align="left">Adjusted mean</th><th align="left">SE</th><th align="left"><italic>F</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>η</italic><sup>2</sup></th></tr></thead><tbody valign="top"><tr><td>MR‐CPS group</td><td>115</td><td>3.872</td><td>0.784</td><td>3.852</td><td>0.072</td><td>12.694<xref ref-type="fn" rid="tfn7" /></td><td><0.001</td><td>0.055</td></tr><tr><td>No‐MR‐CPS group</td><td>107</td><td>3.460</td><td>0.779</td><td>3.481</td><td>0.075</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>7 *** <emph>p</emph> < 0.001.</p> <hd id="AN0174977993-26">DISCUSSION AND CONCLUSIONS</hd> <p>This study developed a metacognitive‐regulated collaborative programming approach and a programming learning system based on this approach. The results showed that MR‐CPS could improve students' individual learning achievements and computational thinking tendency and also had a role in promoting students' group metacognition and collective efficacy. Therefore, the MR‐CPS can provide students with metacognitive guidance to help them perform better on collaborative programming tasks and achieve better learning outcomes.</p> <p>Regarding learning achievements, students in the MR‐CPS group performed statistically significantly better than those in the No‐MR‐CPS group. This result is consistent with previous research findings (Theobald, [<reflink idref="bib64" id="ref106">64</reflink>]). It found that metacognition is a primary characteristic of high academic achievers (Wang et al., [<reflink idref="bib66" id="ref107">66</reflink>]). This result is similar to previous research findings that providing learners with self‐planning, self‐monitoring and self‐evaluation mechanisms can significantly enhance their learning achievements (Lai & Hwang, [<reflink idref="bib37" id="ref108">37</reflink>]). Some studies have shown that students struggle to set goals and formulate plans during collaborative learning activities (Hadwin et al., [<reflink idref="bib24" id="ref109">24</reflink>]). The MR‐CPS used metacognitive‐regulated question scaffolding to guide learners in developing a learning plan in terms of learning objectives, learning methods and the collaborative tasks they planned to engage in, which helped students to have a clear plan for both learning objectives and collaborative assignments, and also facilitated the monitoring of the collaborative process and learning reflection (Hadwin et al., [<reflink idref="bib24" id="ref110">24</reflink>]), thus helping to improve their achievement (Çakıroğlu & Öztürk, [<reflink idref="bib11" id="ref111">11</reflink>]). Moreover, monitoring helps students become aware of various strategies that can be used and modified promptly to achieve learning goals (Zimmerman & Schunk, [<reflink idref="bib77" id="ref112">77</reflink>]). Research has shown that monitoring provides the basis for effective allocation of study time and effort, better control of attention and improved performance (Roebers, [<reflink idref="bib51" id="ref113">51</reflink>]). Self‐evaluation after completing a task leads to reflection on the achievement of learning objectives, the learning strategies used and the collaborative tasks in which they participated, which helps students to modify or refine their learning strategies to improve subsequent learning (Zimmerman, [<reflink idref="bib76" id="ref114">76</reflink>]). In terms of computational thinking tendency, students in the MR‐CPS group performed statistically significantly better than those in the No‐MR‐CPS group. The result of this study implied that metacognitive regulation is a strategy that helps develop computational thinking. Computational thinking is constantly associated with problem solving (Kong et al., [<reflink idref="bib32" id="ref115">32</reflink>]). It has been found that self‐reflection can improve students' ability to regulate and thus improve their problem‐solving skills (Panaoura, [<reflink idref="bib48" id="ref116">48</reflink>]). In addition, developing a learning plan, monitoring the learning process, evaluating and reflecting can stimulate students' metacognition (Ouyang et al., [<reflink idref="bib47" id="ref117">47</reflink>]), which is significantly positively related to computational thinking (Şen, [<reflink idref="bib57" id="ref118">57</reflink>]) and therefore contributes to improving students' computational thinking (Liu et al., [<reflink idref="bib40" id="ref119">40</reflink>]; Liu & Liu, [<reflink idref="bib39" id="ref120">39</reflink>]).</p> <p>Regarding group metacognition, students in the MR‐CPS group expressed more confidence in planning and evaluating skills than those in the No‐MR‐CPS group. The possible reason is that the self‐planning and self‐evaluation mechanisms effectively activated the students' planning and evaluation awareness. This result aligns with previous research, which indicated that appropriate metacognitive scaffolding could stimulate students' metacognition (Ouyang et al., [<reflink idref="bib47" id="ref121">47</reflink>]). However, in terms of knowledge of cognition and monitoring, there were no statistically significant differences between the two groups. Sun et al. ([<reflink idref="bib62" id="ref122">62</reflink>]) pointed out that external regulatory factors in regulative learning are susceptible to regulatory mechanisms (eg, seeking help, etc). In contrast, knowledge of cognition is an internal factor for learners. The reason may be that knowledge of cognition is the same between the two classes of students. In addition, because the self‐monitoring mechanism in this system is presented in the form of a pop‐up window, students may easily overlook it during the learning process. Therefore, the MR‐CPS may not affect students' awareness and confidence in monitoring.</p> <p>On the other hand, students who used the MR‐CPS showed better collective efficacy than students who used the No‐MR‐CPS. Since collective efficacy refers to a group's shared beliefs about its perceived ability to perform a task to achieve a specified programming goal (Bandura et al., [<reflink idref="bib4" id="ref123">4</reflink>]), this finding implied that the self‐planning and self‐evaluation mechanisms in the MR‐CPS significantly increased students' confidence and beliefs about the group's ability to complete the task. This finding is the same as previous studies (Zheng et al., [<reflink idref="bib73" id="ref124">73</reflink>]) because the MR‐CPS helped students to control their learning process better and clarify their position in the group. The results of the study also indicated that students gained more relevant knowledge. As a result, students felt that they were more capable of completing learning tasks and were able to achieve higher scores through group work.</p> <p>The experimental group was statistically significantly higher than the control group in terms of academic achievement, computational thinking tendencies, group metacognition and collective efficacy. The results suggest that the metacognitive‐regulated collaborative programming approach has the potential to improve students' learning performance. However, the results also showed that the overall effect size was smaller. Therefore, the implications of the proposed approach for practical use should not be overstated. Experiments with longer intervention times could be conducted to verify further the effectiveness of the proposed metacognitive‐regulated collaborative programming approach.</p> <p>There are some limitations to this study. First, this study focused on the effects of the metacognitive regulation‐based collaborative programming approach on junior high school students. The findings cannot be extended to other school levels. Future research could focus on the effects of the approach on programming learning for elementary or college students. Second, in this study, no qualitative data were collected or analysed. Future studies could adopt a mixed research design. Third, the results of this study showed that students' self‐monitoring awareness was not improved. The system needs to be further enhanced with the monitoring function and analysed with qualitative research data. Finally, different from previous studies that employed regulation only from an individual perspective, this study employed metacognitive regulation from individual and collaborative perspectives. However, the metacognition of the team as a whole, namely, socially shared regulation, is not involved in this study. Future research should consider designing socially shared metacognition that can stimulate group metacognition.</p> <hd id="AN0174977993-27">ACKNOWLEDGEMENTS</hd> <p>This work was supported by the National Science and Technology Council of the Republic of China [MOST 109‐2511‐H‐216‐001‐MY3] and [NSTC 112‐2410‐H‐216‐002] and the Ministry of Education of Humanities and Social Science Project of the People's Republic of China [21YJA880027].</p> <hd id="AN0174977993-28">CONFLICT OF INTEREST STATEMENT</hd> <p>All the authors have approved the manuscript and agree to submit it to <emph>British Journal of Educational Technology</emph>. There are no conflicts of interest to declare.</p> <hd id="AN0174977993-29">DATA AVAILABILITY STATEMENT</hd> <p>The analysed data can be provided upon request via e‐mail to the corresponding author.</p> <hd id="AN0174977993-30">ETHICS STATEMENT</hd> <p>The study has been examined and advised by an academic ethics review committee.</p> <ref id="AN0174977993-31"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref37" type="bt">1</bibl> <bibtext> Akinola, S. O. (2016). Computer programming skill and gender difference: An empirical study. American Journal of Scientific and Industrial Research, 7 (1), 1 – 9. https://doi.org/10.5251/ajsir.2016.7.1.1.9</bibtext> </blist> <blist> <bibl id="bib2" idref="ref13" type="bt">2</bibl> <bibtext> Altintas, T., Gunes, A., & Sayan, H. (2014). A peer‐assisted learning experience in computer programming language learning and developing computer programming skills. 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DbLabel: ERIC
An: EJ1408635
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PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Development of a Metacognitive Regulation-Based Collaborative Programming System and its Effects on Students' Learning Achievements, Computational Thinking Tendency and Group Metacognition
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wei+Li%22">Wei Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4599-6433">0000-0002-4599-6433</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cheng-Ye+Liu%22">Cheng-Ye Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0732-0288">0000-0003-0732-0288</externalLink>)<br /><searchLink fieldCode="AR" term="%22Judy+C%2E+R%2E+Tseng%22">Judy C. R. Tseng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3579-917X">0000-0002-3579-917X</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Technology%22"><i>British Journal of Educational Technology</i></searchLink>. 2024 55(1):318-339.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 22
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Junior+High+School+Students%22">Junior High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Computation%22">Computation</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+Learning%22">Cooperative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Differences%22">Individual Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Group+Behavior%22">Group Behavior</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/bjet.13358
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0007-1013<br />1467-8535
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Collaborative programming helps improve students' computational thinking and increases their confidence in solving programming problems. However, the effect of collaborative learning is not ideal because it is difficult for students to mobilize metacognition to regulate learning spontaneously. To guide students to effectively regulate the learning process when they collaborate to solve programming problems, this study develops a collaborative learning approach and a Collaborative programming System (MR-CPS) based on metacognitive regulation to support students' collaborative programming learning. A quasi-experimental study was conducted in a junior high school programming course in Taiwan to assess the effects on students. The impacts of MR-CPS from both individual and collaborative perspectives were investigated. Students' learning achievement and computational thinking tendencies were examined from an individual perspective. From a collaborative perspective, group self-efficacy and group metacognition were investigated. Participants were divided into MR-CPS (n = 115) and No-MR-CPS (n = 107). The MR-CPS group used the collaborative programming approach with metacognitive regulation mechanisms as the experimental group. In contrast, the No-MR-CPS group used the collaborative programming approach without metacognitive regulation mechanisms as the control group. The results show that the MR-CPS group statistically significantly outperformed the No-MR-CPS group in learning achievements. It was also found that the MR-CPS group had statistically significantly better computational thinking tendency, collective efficacy and metacognitive planning and evaluation skills than the No-MR-CPS group. This finding suggests that the MR-CPS has the potential to improve students' learning achievements, computational thinking tendency, group metacognition and collective efficacy. The study results have implications for the design of collaborative programming systems consistent with metacognitive regulation.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1408635
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1408635
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bjet.13358
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 318
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Junior High School Students
        Type: general
      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Computation
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Cooperative Learning
        Type: general
      – SubjectFull: Self Efficacy
        Type: general
      – SubjectFull: Individual Differences
        Type: general
      – SubjectFull: Group Behavior
        Type: general
      – SubjectFull: Taiwan
        Type: general
    Titles:
      – TitleFull: Development of a Metacognitive Regulation-Based Collaborative Programming System and its Effects on Students' Learning Achievements, Computational Thinking Tendency and Group Metacognition
        Type: main
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          Name:
            NameFull: Wei Li
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          Name:
            NameFull: Cheng-Ye Liu
      – PersonEntity:
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            NameFull: Judy C. R. Tseng
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            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0007-1013
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              Value: 1467-8535
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            – Type: volume
              Value: 55
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            – TitleFull: British Journal of Educational Technology
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