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The article 'Scaffolding Collaborative Drawing-to-Learn to Support Metacognitive Regulation and Model Construction' delves into the significance of drawing in STEM education and the potential of collaborative drawing to enhance learning outcomes. It introduces the drawing-to-learn pedagogical approach, emphasizing the role of metacognition in individual and collaborative drawing. The study presents a novel scaffolding method designed to facilitate students' self- and co-regulation during collaborative drawing activities, with a focus on constructing scientific models. The research demonstrates that this approach significantly improved students' ability to create accurate scientific models of carbon cycling, highlighting the importance of metacognitive co-regulation in collaborative learning. The findings contribute to the understanding of how to effectively use technology to support collaborative learning in STEM education.
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Abstract
The “drawing-to-learn” pedagogical approach has gained considerable attention and evidence supporting its efficacy for facilitating learning. However, little theoretical or empirical research has addressed issues regarding collaborative drawing-to-learn. In this study, we developed collaborative drawing-to-learn activities and three types of scaffolding to facilitate students’ carbon cycling modeling, with the aims of investigating the effects of the activities and scaffolding and identifying significant factors contributing to learning outcomes. Participants were 51 high school students. Mixed methods were employed. Student-drawn models of carbon cycling were collected during the pretests, class sessions, and posttests. Students’ prior knowledge of carbon cycling was measured in the pretests, and their self-ratings of self-regulation and co-regulation demonstrated during collaborative drawing were collected. Student action and discussion data were also collected and analyzed for result triangulation. The results indicated that the designed activities and scaffolding were effective in terms of facilitating the students’ modeling performances, and students reported and demonstrated satisfactory self- and co-regulation. Moreover, correlation and multiple regression results indicated that prior content knowledge and metacognitive co-regulation are significant direct factors affecting students’ collaborative drawing products, whereas metacognitive self-regulation may be mediated by co-regulation to have effects. The study contributes by extending theoretical perspectives on the mechanism of how collaborative drawing may facilitate learning, and by providing examples and empirical benefits of effective collaborative drawing as a pedagogical approach.
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Introduction
Construction of scientific models plays a central role in explaining complex phenomena and illustrating the functionality of various machines in various scientific and engineering practices. Scientific models usually consist of visual representations and symbol systems, making drawing a crucial aspect and an integral component of the language within STEM fields that students need to grasp (National Research Council [NRC], 2012; NGSS Lead States, 2013).
As a pedagogical approach that can support student construction of scientific models, the drawing-to-learn approach emphasizes utilizing drawings as fundamental tools for sense-making, communication, and problem-solving (Cromley et al., 2020; Gilbert & Treagust, 2009; Kozma & Russell, 2005; Wu & Rau, 2019). Empirical research on the drawing-to-learn approach has provided evidence of its efficacy in enhancing students’ understanding of science concepts and fostering the development of mental models and representational skills (Chang et al., 2020; Cromley et al., 2020; Fiorella & Zhang, 2018).
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However, relatively few studies have focused on supporting student collaborative drawing construction. The significance and necessity of involving students in socially co-constructed drawing practices are rooted in sociocultural and situated learning perspectives that construction of human knowledge is both social and communicative, and that communities and collaboration provide pivotal mechanisms for student learning (Mercer, 2013; Mercer et al., 2019; Otero et al., 2011; Wenger, 2011). The Cognitive Model of Drawing Construction (CMDC) has explicated the mechanism of individual drawing construction, delineating the significant roles of individuals’ prior knowledge and self-regulation in achieving successful individual drawing construction (Van Meter & Firetto, 2013). How the CMDC may apply to collaborative drawing construction remains to be explored.
Meanwhile, online co-construction drawing technology rapidly developed throughout and following the COVID-19 pandemic. It can facilitate the collaboration of teachers and students in generating visual representations, and is well-suited for student-centered co-construction learning activities. Theoretical perspectives suggest that online co-construction technology offers an excellent environment for metacognitive co-regulative learning (Hadwin et al., 2018; Raes et al., 2016). For instance, group members can aid one another in reflection, monitoring, and decision-making through interactions, turn-taking, and idea exchange (Garrison & Akyol, 2015; Hadwin et al., 2018; Raes et al., 2016).
However, not all students are naturally inclined to engage in self- and co-regulation in any online co-construction learning environment. Therefore, well-designed scaffolding and activities are necessary to facilitate students’ self- and co-regulation (Burin et al., 2020). To address this issue, we developed learning scaffolding to guide students’ collaborative learning with co-construction drawing technology, with the aim of facilitating their self-regulation and co-regulation during their collaborative drawing.
Although not specifically focusing on collaborative drawing construction, past research has shown that metacognitive regulation, especially co-regulation, can lead to successful collaborative learning and knowledge construction (Ucan & Webb, 2015; Zabolotna et al., 2023). Building upon past research, we hypothesized and tested that the scaffolded collaborative drawing learning activities that we developed by employing the scaffolded collaborative drawing-to-learn approach could support students in constructing scientific models. One possible mechanism for successful construction involves students’ self- and co-regulation enabled by the scaffolding, which may in turn facilitate their construction of scientific models.
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Synthesizing from the theoretical perspectives relating to both individual and collaborative drawing (e.g., Hadwin et al., 2018; Raes et al., 2016; Van Meter & Firetto, 2013), we specifically focused on three factors, students’ prior knowledge, self-regulation, and co-regulation, to examine their roles in the collaborative drawing products, as initial steps to gain insights into the mechanism of how the scaffolded collaborative drawing approach may foster learning. The research questions investigated in this study include:
1.
To what extent did the designed learning scaffolding elicit students’ self- and co-regulation during their collaborative drawing?
2.
To what extent did the scaffolded collaborative drawing learning activities enhance the learning outcome, namely, students’ construction of scientific models?
3.
What factors, including students’ prior scientific knowledge, self-regulation, and co-regulation, significantly contribute to the learning outcome of collaborative drawing?
While existing drawing-to-learn theories and empirical studies predominantly concentrated on individual drawing construction, this research strived to broaden the understanding by building upon the foundations of the CMDC and by investigating factors of collaborative drawing construction contributing to the learning outcome. Furthermore, an existing research gap indicates the growing demand for innovative and effective computer-based drawing methods that leverage technological affordances to enhance the drawing-to-learn instructional approach (Cromley et al., 2020). The current study proposes the scaffolded collaborative drawing-to-learn approach, and shows how collaborative drawing activities and accompanying scaffolding may effectively facilitate collaborative learning with technology and enhance the scientific model construction learning outcome.
Background
The Drawing-to-Learn Pedagogical Approach
Engaging students in using visual representations to create their drawings for idea expression, knowledge construction, or model development is often referred to as the “drawing-to-learn” pedagogical approach (Ainsworth & Scheiter, 2021; Cromley et al., 2020). There are multiple theoretical perspectives supporting the use of the drawing-to-learn approach. For example, the cognitive model of drawing construction (CMDC) delineates the mechanism by which the drawing-to-learn approach facilitates learning, primarily through two key aspects. First, the drawing approach engages individuals in cognitive tasks such as the selection, translation, and organization of content within the drawing. Second, it promotes metacognitive awareness, prompting individuals to recognize conceptual limitations throughout the drawing process and enabling the implementation of self-regulatory strategies to achieve the drawing goals (Van Meter & Firetto, 2013).
In a meta-analysis encompassing 55 studies involving paper-and-pencil-based drawing approaches and 14 studies involving computer-based drawing approaches, Cromley et al. (2020) discovered that the traditional paper-and-pencil drawing-to-learn approach yielded significantly superior effects on both factual and inferential learning outcomes compared to the non-drawing condition. In contrast, the use of computer-based drawing technology did not exhibit significant improvements over the non-drawing condition. The study emphasized the necessity for researchers to explore new instructional approaches incorporating computer-based drawing that harness the affordances of technology to yield substantial learning benefits.
Specific to collaborative construction of external representations, Otero et al. (2011) reviewed past research and perspectives and concluded that co-construction of external representations mediates the process of knowledge convergence in collaborative learning scenarios. Notably, various forms of visual representations serve to establish shared points of focus and serve as a basis for discussion. Despite its theoretical underpinnings, there exists a paucity of empirical research that has specifically investigated the impact of co-constructing external representations, including drawings, on student learning.
Metacognitive Regulation During Collaborative Learning
Although few studies have specifically focused on metacognitive regulation for collaborative drawing, research on metacognitive regulation during collaborative learning may be applied. For example, Volet et al. (2009) proposed a theoretical framework for metacognitive regulation in collaborative learning which includes individual regulation and co-regulation. Individual regulation refers to “self-regulation,” denoting an individual’s capacity to reflect, employ management and monitoring skills, and utilize strategies to adapt their learning during the learning process (Garrison & Akyol, 2015). In comparison, the manifestation of metacognition among group members such as prompting and assisting each other in reflecting, monitoring, and decision-making through processes such as interaction, turn-taking, and idea exchange is termed “co-regulation” (Garrison & Akyol, 2015; Hadwin et al., 2018; Raes et al., 2016).
Thus, in a co-construction learning environment, the demonstration of metacognition should encompass not only self-regulation but also co-regulation (Ucan & Webb, 2015). Furthermore, it is suggested and evidenced that co-regulation may enable further self-regulation or even more productive socially shared regulation (Hadwin et al., 2018; Urban & Urban, 2023). The main characteristic of socially shared regulation, which involves the highest degree of collaborative regulation, includes negotiation and agreement from all group members to regulate their collective activity (Hadwin et al., 2018; Urban & Urban, 2023).
Empirical studies provide evidence that co-regulation plays a crucial role in students’ collaborative learning success (Ucan & Webb, 2015; Zabolotna et al., 2023), including evidence that co-regulation can guide and support knowledge construction (Zabolotna et al., 2023). In this study, we hypothesized and tested students’ co-regulation as a significant factor relating to the learning outcome of the collaborative drawing activity. Furthermore, the CMDC suggests that students’ prior content knowledge and self-regulation are important factors for individual drawing construction. We explored whether these factors may play significant roles in collaborative drawing construction products.
Scaffolding Collaborative Drawing-to-Learn
An effective approach to scaffolding involves the use of prompts or prompting questions (Chang, 2023; Chen & Law, 2016). For instance, in Wu and Rau’s (2018) study, drawing prompts were integrated into a domain-specific drawing application called Chem Tutor to guide students in revising their drawings of “atoms.” It revealed a notable impact of the drawing prompts on instructional efficiency compared to no prompts. Additionally, the study compared the effects of providing prompts throughout the instruction process with providing them before and after instruction, and identified a significant advantage in offering prompts throughout the instructional period.
Other studies provided evidence that prompting is effective in terms of facilitating students’ regulatory strategies for learning, illustrating a mechanism that prompting as scaffolding may facilitate students’ regulation, which in turn may lead to learning gains (Quackenbush & Bol, 2020; Sulla et al., 2023). These results provide empirical support for the sociocultural theory (Mercer et al., 2019), emphasizing the pivotal role of well-designed scaffolding for students’ collaboration and learning processes in enhancing learning outcomes (Burin et al., 2020).
In this study, students were engaged in collaboratively constructing drawings of carbon cycling models using the co-construction drawing technology, Miro. The carbon cycling topic was chosen due to its crucial role in understanding potential strategies for mitigating global warming (Lin et al., 2022; Zangori et al., 2017). Scaffolding was designed based on a synthesis of literature documenting students’ challenges in three key aspects. The first concerns students’ lack of content knowledge for modeling carbon cycling, such as knowledge of the forms of the carbon element in the Earth’s four spheres and understanding of photosynthesis and cellular respiration as mechanisms of carbon cycling (Markauskaite et al., 2020; Zangori et al., 2017). The second relates to the utilization of representation skills to depict the components, processes, and relationships of the carbon cycle for creating a model (Bergan-Roller et al., 2018). For instance, students often struggle with visualizing carbon cycling on a global scale (Zangori et al., 2017). The third aspect refers to challenges in student collaboration, including the need for support for effective group management and ensuring equal levels of agency and participation (Hussein, 2021).
Consequently, three types of scaffolding, in the form of prompting questions, were developed: content, representation, and collaboration scaffolding. This scaffolding leveraged technology affordances to prompt students to reflect on plans, challenges, and solutions regarding their learning with the collaborative drawing technology, with the aim of fostering interaction among learners, and reflection on their knowledge and resources, hence possibly promoting co-regulation and self-regulation (Hadwin & Oshige, 2011; Hadwin et al., 2018). This scaffolding was implemented using the jigsaw approach (Brown & Campione, 1996; Pozzi, 2010), that is, dividing students in a group into three roles: collaboration, content, and representation assistants. This approach has been shown to improve student interaction, positive interdependence, interpersonal and communication skills, and knowledge building (Jeppu et al., 2023; Looi et al., 2008). Detailed descriptions of the scaffolding and procedures are provided in the “Methods” section.
Methods
This study was situated in one cycle of design-based research that emphasized designing interventions based on learning theory and investigating the implementation process and outcome to improve practical feasibility and contribute to learning theory (Brown, 1992; Phillips et al., 2012). Specifically, this study employed mixed methods to evaluate the effectiveness of the designed learning activities in light of how well the design works in local contexts through investigating student changes and group interactions. Analysis of changes in students’ constructed models and data from the learning process can provide direct evidence of how students used the designed features to achieve the learning outcome (Barab & Squire, 2004; Brown, 1992; Phillips et al., 2012).
The design of the research and involved learning activities is visualized in Fig. 1. The learning activities are characterized as scaffolded collaborative drawing-to-learn activities, designed based on theoretical and empirical research delineated in the previous sections. Moreover, the learning activities took advantage of computer-based, multi-user online drawing technology to support student collaboration and modeling. The measures used to address the research questions are outlined in the blue boxes in the figure. Specifically, students’ prior knowledge was assessed to investigate its role in students’ collaborative drawing learning outcomes, a relation indicated in theoretical research and tested in this study. Students’ metacognitive self- and co-regulation and constructed model quality were assessed because they are the aspects that the learning activities were set up to foster, based on our synthesis of the theoretical and empirical research. The details of the participants, learning activities, measures, procedures, and analyses are described as follows.
Fig. 1
The design of the research and learning activities in this study
Before the study, an explanation of the study and a consent form were provided to all students. A total of 26 12th-grade students (15 female) and 25 seventh-grade students (12 female) at two public high schools in Taiwan consented to participate with their parents’ approval and agreement. The 12th and seventh grades represent the highest and lowest grade levels at high schools in Taiwan, respectively. The students were told that they could withdraw at any time during their participation, and that their withdrawal would not affect their school grades. They were also told that all their actions using the drawing tool and their discussions would be video- or audio-recorded. All procedures, including forms and instruments, were approved by the Research Ethics Committee at National Taiwan Normal University (approval no. 202205HS014).
The students had used computers on a daily basis, ranging from less than 30 min to 1 to 2 h. They had all learned basic concepts of carbon cycling since the fifth grade, based on the curriculum standards in Taiwan requiring teaching carbon cycling concepts as early as the fifth grade (Ministry of Education, 2018). However, the regular curriculum is mainly lecture-based with little emphasis on modeling carbon cycling. The students’ pretest scores from the prior scientific knowledge test of carbon cycling in this study (detailed in the “The Prior Scientific Knowledge Test” section) provide evidence that they had some prior knowledge of carbon cycling, to varying degrees, before the study (12th-grade students: mean = 7.04, SD = 2.20; seventh-grade students: mean = 4.44, SD = 2.02. The possible maximum score for this test was 10). The students were taught by their biology teachers in this study, who collaborated on this research project to develop the unit. Both biology teachers had more than 10 years of high school biology teaching experience.
The “Carbon Emission Crisis” Unit and Its Drawing Activities and Scaffolding
A unit named “Carbon Emission Crisis” was developed in this study to engage students in scaffolded collaborative drawing with technology to construct scientific models of carbon cycling. It was designed for high school students, regardless of a specific grade level. The unit lasted four class periods (45 min each), and students worked in groups of three or four. The first activity was an orientation activity. A newspaper article was given to the students introducing the dilemmas that the petrochemical industry currently faces in terms of reducing carbon emissions while maintaining the global economy and the idea of transitioning from a petroleum-based economy to a carbon circular economy. The article ended with a comment that “carbon cycling will become the key to the survival of the petrochemical industry.” The students were guided to discuss why carbon cycling is key to the survival of the petrochemical industry.
Following this orientation reading and discussion, the collaborative drawing task in activity 2 asked the students to draw group models of carbon cycling using a co-construction platform, Miro. Each student used an iPad to log into Miro and collaboratively construct a group model with their group members. Scaffolding was provided using the jigsaw approach (Brown & Campione, 1996) which allocated students in each group one of three roles: collaboration, content, and representation assistants. Students performing each role formed an expert group in which the instructor gave them the worksheets with prompting questions (i.e., the scaffolding), and explained the prompting questions and their roles. The students then returned to their original jigsaw groups, taking turns to lead their group discussions to complete the worksheets with the prompting questions (i.e., the scaffolding, detailed in Table 4 in the Appendix), which were provided simultaneously. Students could decide on their own plan and order to complete the worksheets and to work on their drawings.
Scaffolding was provided throughout the learning process based on empirical findings supporting the provision of scaffolding during instruction (Wu & Rau, 2018). The collaboration scaffolding aimed to foster interactions among learners such as the exchange of reflected thoughts, with the aim of promoting co-regulation (Hadwin & Oshige, 2011; Hadwin et al., 2018). The content and representation scaffolding focus on fostering individuals’ as well as groups’ reflections on their knowledge, skills, strategies, and resources that would be needed in the process of drawing construction, with the aim of promoting both self- and co-regulation.
The students were allowed to search online for any information needed, but could not directly search for a carbon cycling model. This was ensured by our classroom observations and also examinations of the process videos. In activity 3, the students were engaged in looking at and commenting on other groups’ drawings. They reviewed the comments they received about their model from other groups and worked on refining their models. Finally, they were required to propose a “carbon reduction” or “carbon recycling” plan based on the final version of the group’s carbon cycling model and to write down their ideas for the plan.
Data Collection Procedure and Instruments
In addition to the four class periods for learning the unit, the students took pretests and posttests lasting 20 to 30 min. They completed the metacognitive questionnaire immediately after finishing their drawings in activity 2. Data from group discussions and group models were also collected and analyzed. A total of 12 groups were formed, including six 12th-grade and six seventh-grade groups. Group models were student-generated using Miro. Group discussions were audio recorded, transcribed, and analyzed. Additionally, individual tablet screens were recorded as process videos to capture each student’s actions and voices. These videos were examined to help differentiate different group members’ actions and participation in group discussion.
The Drawing Test: Modeling Carbon Cycling
This test was adapted from Zangori et al. (2017) and asked students to draw a carbon cycling model on paper for the pre- and posttests. An introduction and directions were given: “The carbon cycle refers to the continuous circulation of the carbon element (C) in nature among the Earth’s atmosphere, geosphere, hydrosphere, and biosphere. Human activities also have an impact on the natural carbon cycle. Where does carbon come from? And where does it go? Please draw your ideas below. Use lines, text, symbols, various shapes, or forms to represent all your ideas here. Don’t worry about the aesthetics of the drawing, but make sure to represent everything you have in mind.” The wording (in Mandarin Chinese) was pilot-tested with two high school students and evaluated by the biology teachers for readability and validity.
The Prior Scientific Knowledge Test
The prior scientific knowledge test comprised 10 multiple-choice items and was administered before the unit as part of the pretests to measure students’ prior scientific knowledge of carbon cycling. An example item is: “Which chemical reaction in the following can utilize or absorb carbon dioxide from the atmosphere? (A) Bacterial decomposition of organic matter (B) Photosynthesis in plants (C) Respiration in animals (D) Combustion of fossil fuels.” The content and construct validity of the test was established through item analysis conducted in meetings where science education researchers and science teachers reviewed and revised the items to ensure that they measured essential content knowledge related to carbon cycling. The KR-20 for the prior scientific knowledge test was 0.70, indicating adequate reliability.
Metacognitive Regulation of Online Collaborative Drawing (MROCD) Questionnaire
This instrument probed students’ self-rated metacognitive self- and co-regulation during the scaffolded online collaborative drawing activity. Students responded to this questionnaire during learning, right after they completed their group model using Miro. Adapted from Garrison and Akyol (2015) and Binali et al. (2021), the instrument consists of 20 items, rated on a 5-point Likert-scale from strongly disagree (1) to strongly agree (5). It comprises two dimensions: self-regulation (SR) probing the extent to which students employ self-regulation such as monitoring, reflecting, and managing when engaging in the scaffolded online collaborative drawing activity, and co-regulation (CR) probing the extent to which students employ co-regulation such as monitoring, prompting, and reflecting with others through interaction processes when engaging in the scaffolded online collaborative drawing activity. Each dimension consists of 10 items (Table 7 in the Appendix). The reliability of the two dimensions as established in the present study through Cronbach’s reliability coefficients (α) signified a satisfactory level of internal consistency: SR α = 0.81 and CR α = 0.85.
Data Analysis
Scientific Merits of Student-Generated Carbon Cycling Models
Detailed coding rubrics for scoring students’ models were generated, following the coding schemes by Zangori et al. (2017), to indicate the quality of the models in light of their scientific merits. In general, each student-drawn model was evaluated on three aspects of carbon cycling: components, sequences, and mechanisms (Table 5 in the Appendix). Coding rubrics were used to code all of the student-generated models in the drawing pretests and posttests and in the in-class collaborative drawing activity. The pretest and posttest models were drawn on paper by individual students, whereas those drawn during class were made in groups using Miro. Two raters independently coded all of the models following the rubrics. The obtained inter-coder reliability was 0.76 (Cohen’s kappa). Inconsistent codes were discussed and resolved.
Individual students’ pretest and posttest drawing scores on each of the three aspects of modeling carbon cycling were compared, and were then summed to indicate their overall modeling performance. Paired sample t-tests and effect sizes (d) were employed to examine significant changes from the pretests to posttests to indicate the overall effects of the entire unit on developing students’ ability to generate scientific models. The effect size (d) was calculated as the difference between the means divided by the pooled standard deviation (Rosnow & Rosenthal, 1996). Effect sizes are categorized as small (d = 0.2), medium (d = 0.5), or large (d = 0.8) (Cohen, 1988). The student groups’ models were also analyzed and reported to indicate the quality of the models made during class.
Students’ Prior Scientific Knowledge
Each item in the prior scientific knowledge test data was assigned a score of 1 for a correct answer and 0 for an incorrect answer. Subsequently, the total score for each student was computed by summing the scores of all 10 items, reflecting the overall prior scientific knowledge of carbon cycling.
Students’ Self- and Co-regulation Via Questionnaire
The degree of students’ metacognitive self- and co-regulation was indicated using the average mean scores and the distribution of the level of agreement for each statement in the MROCD questionnaire. A higher score indicates more robust agreement with the respective statement. The distribution of students’ responses based on the different levels of agreement was examined to reveal students’ self-reported self-regulation and co-regulation during the scaffolded collaborative drawing activity.
Students’ Self- and Co-regulation Via Group Discussion
Qualitative analysis of group discussion was employed to triangulate the self- and co-regulation results reported by the students. Conversation analysis (Mazur, 2004) was employed to first segment transcripts of group conversations into turns, where a “turn” refers to a unit of talk produced by one student in a conversation group. On average, each group demonstrated 323 turns, ranging between 76 and 530 turns. A coding scheme was generated based on the data to identify students’ metacognitive regulation (Table 6 in the Appendix).
For each turn, the occurrence of each type of metacognitive regulation was coded as 1, and absence as 0. The coding considered multiple data sources, including transcripts of the audio recordings with inspecting process videos to make sense of the conversations. Two coders with majors in educational studies were trained to use the coding scheme to code the group data with the largest number of turns. The inter-coder reliability was 0.72 (Cohen’s kappa). Inconsistent codes were discussed and resolved in panel meetings with two educational researchers and the coders. The coders then divided the data for coding. All the coding was inspected and discussed in multiple meetings with the researchers and coders to finalize the codes. Frequencies and percentages of groups demonstrating each identified metacognitive regulation are reported.
Multiple Regression Analysis
To explore the relationships among students’ prior knowledge of carbon cycling, metacognitive self- and co-regulation self-reported during collaborative drawing, and the students’ developed scientific models of carbon cycling, we initially utilized Pearson correlations to examine the relationships between any two of the four variables. Subsequently, we conducted multiple regression analysis (Hair et al., 2019), using students’ scores on the drawing posttest as the outcome variable and the other three variables as predictors. This allowed us to investigate how, when considered together, the three variables contributed to the learning outcome.
Findings
Self-Regulation and Co-regulation During the Scaffolded Collaborative Drawing Activity
Students reported above-average agreement on demonstrating metacognitive self-regulation (M = 3.43, SD = 0.57) and co-regulation (M = 3.64, SD = 0.59) during the scaffolded online collaborative drawing activity, with co-regulation receiving a higher rating than self-regulation. The distribution of student ratings for each statement is detailed in Table 7 in the Appendix. Inspecting the distribution of student ratings for each statement, the results indicate that the majority of students reported that they actively paid attention to others’ ideas and engaged in thoughtful consideration and reflection on the comments and feedback provided by their peers. Overall, the students reported that they demonstrated both self- and co-regulation, and that co-regulation was demonstrated more often than self-regulation.
The results of analyzing self- and co-regulation demonstrated during group discussions provide triangulation and support for the self-reported results. Table 8 in the Appendix provides an overview of the frequencies, percentages, and example excerpts of metacognitive regulation demonstrated by groups during collaborative drawing-to-learn. Only two types of self-regulation but eight types of co-regulation were identified. Figure 2 summarizes the numbers of groups demonstrating each aspect of the metacognitive regulation. Overall, some students in seven or eight groups demonstrated a certain aspect of self-regulation, and high numbers of the groups demonstrated co-regulation, specifically in the aspects of task-planning, co-evaluating, prompting, suggesting, and commenting.
Fig. 2
The number of groups demonstrating metacognitive regulation during group discussion (blue: self-regulation; other colors: co-regulation) (Color online)
Evidence can be found in the group discussions that there was an immediate effect of the designed scaffolding. For example, one student (ID#70509) asked her group members: “How do we complete the drawing together?” as she was prompted by the scaffolding, adding: “Hey, isn’t the third question asking about our plan?”. Another example from another group was while group members were dividing their tasks: “I am the representation assistant” (Student#70522), “and I am the content assistant” (Student#70521). Moreover, all groups except one reached the highest degree of co-regulation, that is, socially shared regulation, meaning that all group members participated, regulated, and contributed by adding new information to develop shared awareness or understanding of the drawing tasks and concepts. The excerpts in Table 8 provide concrete examples of the co-regulation and socially shared regulation demonstrated by students during the collaborative drawing to construct a scientific model.
Student Development of Scientific Models
The students’ performances on the drawing pretests and posttests are summarized in Table 1. The t-test results indicated that score differences between the drawing pretests and posttests reached statistical significance for both the seventh-grade and 12th-grade students in all aspects of their modeling performance. The effects of the unit were medium to nearly large for the seventh-grade students, and nearly large or large for the 12th-grade students.
Table 1
Mean scores and standard deviations (in parentheses) of the student-generated models
7th-grade (n = 25)
12th-grade (n = 26)
Pretest
Posttest
t value and effect size (d)
Pretest
Posttest
t value and effect size (d)
Overall performance
2.56(2.53)
5.48(4.56)
t = 4.89***; d = 0.79
3.80(3.25)
7.16(3.90)
t = 4.62***; d = 0.94
Component
1.08(1.19)
2.12(1.45)
t = 4.44***; d = 0.78
1.80(1.53)
2.92(1.41)
t = 3.26**; d = 0.76
Sequence
1.08(1.22)
2.12(1.81)
t = 3.32**; d = 0.67
1.32(1.18)
2.28(1.40)
t = 3.29**; d = 0.74
Mechanism
0.40(0.91)
1.24(1.59)
t = 3.06**; d = 0.65
0.68(1.18)
1.96(1.57)
t = 4.23***; d = 0.92
***p < 0.001; **p < 0.01; *p < 0.05
The mean overall scores obtained for the seventh-grade and 12th-grade students’ group models were 7.56 and 9.71, respectively. Although the scoring unit was by groups for the group models, comparing the mean scores among the pretests, group models during class, and the posttests may reveal the quality of the group versus individual models. As shown in Fig. 3, the seventh-grade and 12th-grade students demonstrated a similar pattern: in general, the students generated their best models of carbon cycling during the scaffolded collaborative drawing activity with technology. Although individuals’ post-learning modeling performance seemed to decrease from the collaborative work, the paired sample t-test results indicated that the students’ modeling performance improved after the intervention compared to their pretest performances.
Fig. 3
The mean scores of student-drawn carbon cycle models at three timepoints (Color online)
Figures 4 and 5 show an example of what one student (ID#30515) learned from the collaborative drawing with technology activity to construct a scientific model of carbon cycling. In the initial drawing pretest (Fig. 4a), she exhibited limited understanding of carbon cycling, representing only one component (dead animal) with unclear or irrelevant lines and arrows, and lacking any indication of a mechanism. In comparison, the group model created with three classmates (Fig. 5) utilizing drawing technology showcases its scientific adequacy and rich understanding. This model incorporates multiple carbon outputs (factory, car, cow), a carbon input (tree), and two carbon sinks (soil and ocean). It features adequate lines and arrows forming cycling chains, along with accurately portrayed mechanisms (photosynthesis, combustion, and dissolution) in appropriate positions within the model. The subsequent posttest drawing reveals that the student internalized the key concepts learned during collaborative drawing, since scaffolding and peer support were absent in the posttest. In this posttest drawing (Fig. 4b), she incorporated nearly all the essential components, sequences, and mechanisms outlined in the group model. The changes observed from the pretest to the posttest provide evidence of the student’s development of a scientific model of carbon cycling within the unit.
Fig. 4
The pretest model (a) and posttest model (b) drawn by the same student (ID#30515) who participated in collaborative drawing to create the group model shown in Fig. 5 (English translation added by authors)
A carbon cycle model drawn by a group (Group#30506) of four students using the drawing technology during the scaffolded collaborative drawing activity (English translation added by authors)
Factors Contributing to Students’ Modeling Performance
Results of Pearson correlations between any two of the quantitative variables are summarized in Table 2. Overall, there was no significant relationship between students’ prior scientific knowledge of carbon cycling and their self-reported self-regulation or co-regulation during online collaborative drawing. However, a moderate positive correlation (r = 0.61, p < 0.001) was found between self-regulation and co-regulation. Furthermore, students’ self-regulation during online collaborative drawing was not significantly associated with their individual modeling performance in the posttests (r = 0.07, p = 0.639). However, both prior knowledge and co-regulation during drawing were significantly related to posttest modeling performance, showing moderate to low positive correlations, respectively [prior knowledge r = 0.52, p < 0.001; co-regulation r = 0.36, p = 0.009].
Table 2
Correlations (r) between any two of the four variables
Variable
1
2
3
4
1. Prior scientific knowledge
1
2. Metacognitive self-regulation
− 0.15
1
3. Metacognitive co-regulation
0.11
0.61***
1
4. Developed scientific models
0.52***
0.07
0.36**
1
** p < 0.01; *** p < 0.001
Further multiple regression analysis was conducted, and the findings are presented in Table 3. Overall, the three variables accounted for 36.6% of the variance in students’ modeling performances in the drawing posttests, indicating an acceptable model [R2 = 0.366, F(3, 47) = 9.03, p < 0.001]. The variance inflation factor (VIF) values among the three predictor variables ranged from 1.10 to 1.72, suggesting no multicollinearity issues.
Table 3
Results of multiple regression analysis
Dependent variable
Predictors
Unstandardized coefficient
Standardized coefficient
t
Sig. (p)
B
Std. error
β
Developed scientific models
Prior scientific knowledge
0.80
0.21
0.47
3.84
< 0.001***
Metacognitive self-regulation
− 0.60
1.15
− 0.08
− 0.52
0.61
Metacognitive co-regulation
2.60
1.10
0.36
2.36
0.02*
(constant)
− 5.78
3.65
− 1.59
0.12
* p < 0.05; ** p < 0.01; *** p < 0.001
The multiple regression results revealed that students’ prior scientific knowledge and self-reported co-regulation during drawing had significant positive weights. Specifically, students who possessed greater prior scientific knowledge of carbon cycling and exhibited more metacognitive co-regulation during online collaborative drawing tended to achieve higher modeling performance in the posttests, even after controlling for other variables in the model.
Conversely, students’ self-regulation during online collaborative drawing did not directly or significantly predict their modeling performance in the posttests. However, given the significant correlation between students’ self-regulation and co-regulation (Table 2), and the contribution of co-regulation to their posttest modeling performance (Table 3), it could be inferred that students’ self-regulation likely exerted an indirect effect through co-regulation on their modeling performance.
Discussion
Collaborative Drawing-to-Learn as an Instructional Approach to Facilitating Scientific Modeling
The use of a collaborative drawing-to-learn approach as a pedagogy is supported by sociocultural and situated learning perspectives that indicate the importance of social and cultural factors in knowledge construction, and highlight the nature of human knowledge as social and communicative (Mercer, 2013; Mercer et al., 2019). In fact, communities and collaboration are viewed as pivotal mechanisms for student learning based on the viewpoint of situated learning theory (Li et al., 2009; Schwen & Hara, 2003; Wenger, 2011). However, relatively few empirical studies have investigated how to engage students in collaborative drawing-to-learn approaches and what the mechanism is for facilitating learning. The current study addressed this research gap by developing a learning unit and its correspondent scaffolding to engage students in collaborative drawing-to-learn with technology, and investigated the effects and factors.
Specifically, the practice of making drawings and models and using them as semiotic tools for sense-making, communication, and problem-solving is valued in STEM disciplines (Cromley et al., 2020; Gilbert & Treagust, 2009; Kozma & Russell, 2005; Wu & Rau, 2019). Research has provided evidence that the drawing-to-learn approach may foster students’ understanding of disciplinary concepts and development of mental models and representational skills (Chang et al., 2020; Cromley et al., 2020; Fiorella & Zhang, 2018). The results of our study provide evidence that incorporating a collaborative element into the drawing-to-learn approach has effects on students’ development of scientific models, a performance that requires integration of content knowledge and representation skills.
Moreover, the scaffolded collaborative drawing learning environment supported students’ self- and co-regulation during the drawing process. With the scaffolding, the students were self-aware of their learning, paid attention to each other’s ideas, and engaged in thoughtful consideration, evaluation, and reflection on the comments and feedback provided by their peers. On average, students rated high in terms of their self- and co-regulation during the scaffolded collaborative learning environment. Moreover, group discussion analysis revealed evidence of the immediate effects of the scaffolding to support students in task-planning, prompting, suggesting, and commenting, with all group members in the majority of groups demonstrating the highest degree of co-regulation (Hadwin et al., 2018; Urban & Urban, 2023). These results indicate that the designed unit and scaffolding were able to engage students in high quality collaborative learning with the drawing technology, which involves an essential part that often determines the success of education (Mercer et al., 2019). Afforded by the drawing technology, students in our study generated their best models of carbon cycling during class, including rich and adequate components, sequences, and mechanisms, showing their deep understanding of carbon cycling.
Influential Factors for Collaborative Drawing-to-Learn
The cognitive model of drawing construction (CMDC) delineates the mechanism of how engaging students in drawing may facilitate learning. It underscores the pivotal role of both prior knowledge and self-regulation in the process and product of drawing (Van Meter & Firetto, 2013). The current study extends the CMDC in the context of collaborative drawing. The result of this study adds to the CMDC by indicating another significant factor that needs to be considered, namely students’ metacognitive co-regulation (Hadwin et al., 2018; Raes et al., 2016). Specifically, metacognitive co-regulation in collaborative drawing construction may involve the eight types identified in this study: task-planning, task-dividing, co-confirming, co-monitoring, co-evaluating, prompting, suggesting, and commenting.
The multiple regression results of this study provide evidence that students’ prior scientific knowledge and metacognitive co-regulation are significant factors that need to be considered when engaging students in the collaborative drawing-to-learn pedagogy. However, no significant direct effect of metacognitive self-regulation was found on the drawing product in the context of collaborative drawing. Nevertheless, in this study we found a positive significant correlation between students’ self-regulation and co-regulation. Empirical research indicates that students who demonstrate strong self-regulation skills may be better at engaging in positive co-regulation with their peers (Efklides, 2008). The results of this study support this finding. Moreover, the results indicate that students’ self-regulation may have an indirect effect on the drawing product, mediated by their co-regulation. In other words, students who demonstrate more metacognitive self-regulation are likely to engage more often in metacognitive co-regulation, and students who demonstrate more metacognitive co-regulation are more likely to construct drawings of models with more scientific merits. Future research may further investigate and validate these relationships with larger sample sizes.
Moreover, research indicates that co-regulation may further enable self-regulation or even more productive socially shared regulation (Hadwin et al., 2018; Urban & Urban, 2023). The group discussion results in this study show evidence that many groups demonstrated co-regulation and reached the socially shared regulation. Whether and how student development of co-regulation may lead to their further development of self-regulation requires prolonged future studies.
The CMDC suggests that prior content knowledge may influence the cognitive process during drawing (Van Meter & Firetto, 2013). However, the relationships between prior content knowledge and metacognition are unclear. Theoretical and empirical research on metacognition indicates that metacognition is a fundamental factor influencing cognition and academic performances, such as acquiring conceptual understanding and scientific knowledge (Chang et al., 2022; Craig et al., 2020; Ohtani & Hisasaka, 2018). Considering both the literature and the results of this study, it seems that students’ prior content knowledge and their metacognition (including self- or co-regulation) are independent of each other, both of which are the foundations for the collaborative drawing-to-learn approach.
Design of Scaffolding for Collaborative Drawing-to-Learn
A notable finding from this study is that compared to the models made by individual students in the pre- and posttests, the carbon cycling models created by groups were rated the highest on their scientific merits. This result not only provides empirical evidence for the theory of the zone of proximal development (ZPD) which posits that students can achieve beyond their existing level of competence and knowledge with support from teachers, peers, and other resource tools (Vygotsky, 1980) but also indicates the effectiveness of the designed scaffolding and activities in terms of supporting students’ achievement of a level that they might have not been able to attain individually.
Nevertheless, the findings also indicate a need for future research to consider the issue of when would be the best time to fade scaffolding (Doo et al., 2020). For example, it is not surprising that students in the posttests did not perform as well as they did when they received support during collaboration, but whether they would perform better in the posttests if they engaged in more scaffolded activities for longer periods of time is worth future investigation.
The results indicated that the content, representation, and collaboration scaffolding designed based on the difficulties identified in the literature (e.g., Bergan-Roller et al., 2018; Hussein, 2021; Markauskaite et al., 2020; Zangori et al., 2017) and implemented using the prompting questions and jigsaw approach (Brown & Campione, 1996; Chen & Law, 2016; Pozzi, 2010) were effective in terms of eliciting students’ self-regulation and co-regulation to direct their collaboration and learning with the collaborative drawing technology. With the scaffolding, the majority of groups even reached the highest degree of co-regulation, socially shared regulation (Hadwin et al., 2018; Urban & Urban, 2023). However, relatively few students were aware of, assessed, or analyzed their own or their peers’ learning strategies. Given that learning strategies are important in STEM education (Griese et al., 2015), future design of scaffolding may need to focus on promoting student use of learning strategies and investigate the effect.
Conclusions
Developing students’ ability to adequately create and use visual representations and symbol systems to build scientific models comprises an important goal of science education (NGSS Lead States, 2013; National Research Council [NRC], 2012). In this study, we engaged high school students in our scaffolded collaborative drawing learning environment to develop their ability to construct scientific models of carbon cycling. Based on the results, it can be concluded that the designed learning activities and scaffolding, including the online collaborative drawing activities with collaboration, content, and representation scaffolding, were effective in terms of facilitating both the seventh- and 12th-graders’ engagement in metacognitive self- and co-regulation and their modeling of carbon cycling.
One limitation of the study is that it was based on a one-group pretest–posttest research design involving a relatively small sample. To address this limitation, mixed methods were used, including analysis of group discussions to search for evidence of the immediate effects of the scaffolding. Moreover, judging by their modeling performances across different timepoints and metacognitive regulation demonstrated and self-reported via multiple data sources, it is evident that the students demonstrated satisfactory degrees of metacognitive regulation with the scaffolding, and developed a better understanding of the components, sequences, and mechanisms of carbon cycling to generate models of carbon cycling with better quality. However, the results may not generalize to other contexts, and further research is needed to investigate their generalizability.
Nevertheless, using this case as empirical evidence, the study extends the theoretical perspective of the CMDC (Van Meter & Firetto, 2013) to the context of collaborative drawing, identifying the significant role of prior content knowledge and metacognitive co-regulation in the quality of the drawing product. Specifically, the study indicates that in the context of collaborative drawing, students’ co-regulation has a significant and direct effect on the drawing product, whereas students’ self-regulation may be mediated by their co-regulation to have an indirect effect. The designed unit and scaffolding provide an example for effective use of computer-based drawing technology to support learning that research calls for (Cromley et al., 2020). However, this study was not designed with the purpose of comparing the use of computer-based versus paper-based drawings as pedagogical approaches. Nevertheless, our observations revealed few usability or technological issues of making computer-based drawings. In addition, as the study suggests, the differing quality of paper-based and computer-based models may be influenced by various factors, including individual versus collaborative work, and the presence or absence of scaffolding. These factors present avenues for future research exploration.
Declarations
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study and its procedure were approved by the Research Ethics Committee at National Taiwan Normal University (approval no. 202205HS014).
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Conflict of Interest
The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
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Three types of scaffolding in the form of prompting questions
Collaboration scaffolding
Content scaffolding
Representation scaffolding
-Taking an overview, how can we collaboratively complete all the worksheets and tasks?
- What is our plan?
- What is our purpose of creating the “carbon cycle model”?
- What difficulties or issues might we currently have in order to complete the carbon cycle model? How do we intend to overcome these difficulties or solve these problems?
- During the process of drawing or discussing, how do we resolve differences in opinions among team members?
- After the initial completion of the carbon cycle model, what ideas or concepts have we not yet expressed in the diagram? Or is the expression not comprehensive and clear enough? How should we make modifications?
-Sources of the carbon element: In what forms does the carbon element exist in the following places? Please list the names of substances containing carbon. (1) Atmosphere; (2) Hydrosphere; (3) Biosphere; (4) Geosphere
-Transformation of the carbon element: Through which processes does the carbon element transition between different spheres? Please list the names of these processes. (1) Carbon transition from the atmosphere to the biosphere; (2) Carbon transition from the biosphere to the atmosphere; (3) Carbon transition from the biosphere to the geosphere; (4) Carbon transition from the geosphere to the atmosphere; (5) Other carbon element transitions between reservoirs that you know
-Balance of the carbon cycle (1) When the carbon cycle reaches balance, what does it mean? (2) What does an imbalanced carbon cycle mean? (3) Which human activities can cause an imbalanced carbon cycle? How does the imbalance occur?
-What should a good “carbon cycle diagram” include in terms of objects/components or features? How should they be represented? (1) How should the reactants containing the carbon element be represented? (2) How should the products containing the carbon element be represented? (3) How should the transformation of the carbon element across different spheres be represented? (4) How should the explanation of the carbon cycle processes be represented? (5) Apart from the above, what else needs to be represented?
-There are various ways and forms to express ideas concretely. When drawing the carbon cycle model, which elements can be represented using the following forms? (1) text (2) symbols (e.g., chemical symbols) (3) images (4) lines (5) colors
- Building upon the above, can these elements fully and clearly represent the concepts related to the carbon cycle? If not, how should they be modified?
Coding scheme for group discussion demonstrating metacognitive regulation
Metacognitive regulation
Description
Self-regulation
A student acknowledges, manages, or controls his or her own learning process or behaviors during the task
Self-evaluating
A student indicates awareness or judgement of his/her own drawings or abilities
Self-reflecting
A student thinks carefully about his/her own ideas or actions
Co-regulation
Group members manage or control the group’s learning processes or behaviors during the task
Task-planning
One or more group members plan or decide on the order of the tasks to complete
Task-dividing
One or more group members divide tasks to team members
Confirming
One or more group members prove and acknowledge other group members’ understanding or actions during the task
Monitoring
One or more group members watch or notice other group members’ actions or progress during the task
Evaluating
One or more group members check the appropriateness, correctness, or comprehensiveness of other group members’ actions during the task
Prompting
One or more group members elicit other group members to initiate an action or reflect on a specific thought that aligns with the goal of the task
Suggesting
One or more group members propose an idea or action or a plan for other group members to consider during the task
Commenting
One or more group members express their opinion regarding an idea or action of other group members
Socially shared regulation
All group members regulate their collective activity to develop shared awareness or understanding; Everyone agrees and complements the original idea by adding new information to the dialogue
Overview of the frequencies, percentages, and example excerpts of metacognitive regulation demonstrated by groups during collaborative drawing
Metacognitive regulation
n (% = n/12)
Example excerpts (originally in Mandarin Chinese)
Self-regulation
Self-evaluating
7 ( 58.3%)
Student#30420: The rocks I drew seem a bit terrible (Group#30408)
Self-reflecting
8 ( 66.7%)
Student#30426: What color should the road be? Brown (Group#30401)
Co-regulation
Task-planning
12 (100.0%)
Student#30503: Let’s draw a tree, and then photosynthesis. Write something about photosynthesis
Student#30510: Hey… Let me write it
Student#30508: I’ll draw the tree… Where’s the color brown?
Student#30503: Where are you drawing your tree?
Student#30510: I’ll draw volcanic activity
(Group#30509)
Task-dividing
9 ( 75.0%)
Student#70511: Sure, you (talking to Student#70506) look up the information online, I’ll write
Student#70512: Then what should I do?
Student#70511: You just… help look up… and then…
Student#70512: Look up what?
Student#70511: Look up information for the questions
(Group#70504)
Confirming
6 ( 50.0%)
Student#70505: The usage of this is the same as PowerPoint
Student#70509: Right
Student#70510: Got it
(Group#70507)
Monitoring
8 ( 66.7%)
Student#70507: We still have to finish drawing this
Student#70504: Yeah, this part needs to be finished
(Group#70505)
Evaluating
12 (100.0%)
Student#30432: It’s not necessary to draw a volcano, right?
Student#30424: But we have to. Doesn’t it show how carbon in the lithosphere enters the atmosphere?
(Group#30407)
Prompting
11 ( 91.7%)
Student#30527: How do we represent carbon cycling?
Student#30522: We have already drawn this, here
(Group#30507)
Suggesting
11 ( 91.7%)
Student#30408: I think you can add one more animal and one more arrow
Student#30426: Where does the arrow connect to?
(Group#30401)
Commenting
10 ( 83.3%)
Student#30432: The sea you drew is taller than the house
Student#30419: I want to show the sense of 3D
Student#30432: It’s too much
(Group#30407)
Socially shared regulation
11 ( 91.7%)
Student#30503: What else, besides digestion?
Student#30510: Dissolution, geological processes, and weathering
Student#30503: What’s the deal with volcanic activity?
Student#30510: It’s just that volcanic eruptions release a lot of carbon dioxide
Student#30510: What about geological processes?
Student#30503: What are geological processes? Earthquakes?
Student#30508: Such as weathering. You can draw a rock and then some wind
(Group#30509)
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