Unfolding the potential of computer-assisted argument mapping practices for promoting self-regulation of learning and problem-solving skills of pre-service teachers and their relationship
Introduction
Whether we recognize it or not, teachers generally encounter complex problems permeating every aspect of teaching profession such as the issues that require making teaching and learning decisions (e.g., what is the most effective teaching method to use in this lesson?), classroom management issues (e.g. how do I manage disruptive behaviors of students in this class?), and instructional issues (e.g., how do I use a teaching material that enables my students to engage in this lesson?) (Day & Gu, 2010). These complex teaching problems are commonly known as decision-making problems, which are characterized by ill-structured problems and contextualized uncertain and incomplete goals with no single solution existing (Belland & Drake, 2013; Jonassen, 2011).
Accordingly, despite the nature of the teaching profession, very often, teachers have difficulties in (1) identifying the problems, and even if they do so, they are not able to (2) explain and solve those problems in a systematic way (van Es & Sherin, 2021). When experiencing these issues, teachers generally have major mental or emotional pressure, burnout, and attrition, and do not want to pursue the profession, especially at the beginning of their professional lives (Scott & Hilt, 2019). These decision-making problems are also encountered by pre-service teachers during their teaching practices (Sherin & Van Es, 2009). Accordingly, previous studies have illustrated that pre-service teachers usually do not use theoretical knowledge for solving their pedagogical problems (e.g., Knight‐Bardsley & Mcneill, 2016; Yeh & Santagata, 2015). In fact, they often implement “trial and error strategies” (Husu, 2004, p.82), which are not considered effective strategies for solving complicated problems (e.g., Reimann & Bannert, 2017).
When dealing with such decision-making problems, problem solvers should move back and forth between problem-solving (PS) stages by judging whether the “right” problem has been solved, and whether the “right” solution is proposed (van Bruggen et al., 2003). This implies that purposeful self-regulated (SR) judgments in each stage are essential (e.g., Kitsantas et al., 2019). It is exactly for this reason that this iterative and goal-oriented argumentative process where inferences that supported by evidence are made as key factor in the context of solving decision-making problems (e.g., Freeley & Steinberg, 2013) require the use of a series of strategies of planning, monitoring, and evaluating (Ge et al., 2016; Hong & Choi, 2011). Along with this understanding, it is not surprising that self-regulation of learning (SRL) can play an active role in the PS process (Aydin et al., 2019; El-Adl & Alkharusi, 2020). This current research builds upon this theoretical perspective that the essence of PS is engaging in argumentative process (e.g., Cho & Jonassen, 2002; Spector & Park, 2012) mediated by self-regulation of learning (SRL) skills (e.g., Ge et al., 2016).
Unfortunately, argumentation process is not easy as it seems in the context of PS, rather it is viewed as a tedious and challenging procedure (e.g., Lawrence et al., 2016, pp. 371–378). This is because by nature, the argumentative process leads to high cognitive demands on students (Ellerton, 2022). For instance, students generally ignore opposing viewpoints when justifying a solution, a phenomenon commonly labelled my-side bias (Nussbaum, 2008), in turn failing in providing alternative ideas during argumentation (Tawfik, 2017). Accordingly, the complex nature of argumentation with organizing multiple knowledge requires students to actively SR their learning to generate better arguments (van Gog et al., 2020). However, it is widely known that students’ monitoring judgments are often inaccurate during PS process (e.g., Baars et al., 2017). Specifically, it is assumed that trying to reach a specific goal or finding a solution to a particular problem leads to use strategies that do not contribute to learning, and this results in an unproductive cognitive load on working memory (Sweller et al., 2019). In that sense, given the complex nature of argumentation, it is critical to minimize any additional load imposed by the means of instruction (the extrinsic load), thereby, enabling students to focus more on the problem and how to resolve it (Shehab & Nussbaum, 2015). Along this line, different researchers have been looking for answers to the question that how we make argumentative process even less challenging and more enjoyable.
Accordingly, there is common agreement about students' need of support from an external source (instructor or tools) as they engage in argumentation when they solve complicated problems (e.g., Delen et al., 2014). A generally accepted type of support especially related to cognitive tasks within complex learning environments is the use of scaffolds (e.g., Dabbagh & Kitsantas, 2005). However, there is little clarity as to which types of scaffolds are effective in supporting students in solving complex problems, still providing important pieces of the puzzle (e.g., Jonassen, 2011; Liu & Liu, 2020). Research has shown cognitive tools have the potential to support students in such work by providing scaffold through: (1) minimizing the cognitive load so that students can focus more on PS tasks (Jonassen, 2011); (2) readily providing visualizations of students thinking and learning in meaningful and organized ways so that student can monitor and regulate their performances (Poitras & Lajoie, 2014); and (3) triggering students' pre-existing knowledge and assumptions (Jonassen, 2011). Notably, it is important to highlight that simply by using any cognitive tools students may not engage in judgmental and evaluative behavior about their performances automatically without having a specific design or feature for it (Kitsantas et al., 2019). Compared to the other cognitive tools computer-assisted argument mapping (CAAM) tools can offer an effective way of self-assessment and self-directed by reflecting on what is said, validating own ideas by providing supportive and counter evidence, modifying things that needs to be changed because CAAM displays students' logic of thinking graphically in a more structured form (Davies, 2019). Evidently, in this process, CAAM allows students to reread their thoughts and develops them further by offering time flexibility, which also minimizes the workload of students. As might be expected, this process can also be advantageous in facilitating SR outcomes (Tawfik & Jonassen, 2013). That is, accurate self-monitoring with an optimal level of support given is key for the effectiveness of SRL (de Bruin & van Merriënboer, 2017). In this regard, as might be expected, monitoring students’ progress by using CAAM tools have also great capacity to augment SRL process (e.g., Pahlavani & Maftoon, 2015; Robillos, 2021). As such, CAAM naturally affords students to externalize their thoughts on the topic given (Nückles et al., 2020), which preserves them from spending more time and more resources for regulating their performances, in turn increase in the quality of performance (Helsdingen et al., 2011).
With scaffolding as a primary function of cognitive tools, by articulation cognitive tools can also support students’ use of SRL strategies by planning, monitoring, and revising their knowledge during PS process (Usher & Schunk, 2017). Still, limited research has yet to thoroughly question the effectiveness of using specific cognitive tools for better learning in the context of PS (Jonassen, 1997; Liu & Liu, 2020), and for augmenting the use of SRL (Reimann & Bannert, 2017). Up to now, there has been limited attention to using CAAM tools in improving SRL (Pahlavani & Maftoon, 2015; Robillos, 2021) and PS skills (Gargouri & Naatus, 2017). The results of empirical studies in this field are far less than conclusive. In fact, the potential of using CAAM tools in facilitating these skills needs further exploration. Also, we still know very little about how this is transferred to the design of an online learning environment.
Hence, this study provides an exciting opportunity to advance our knowledge of unambiguous link between argumentation, SRL and PS, and the significant potential of CAAM practices combined with proper integration of multiple scaffolds to promote students’ these skills through generating better arguments. More specifically, we are interested in investigating the role of CAAM practices by using an online and free tool on how pre-service teachers justify an appropriate solution given the available evidence and understand their autonomy and control ability within an online learning environment. In this study, the following research questions were sought:
- 1.
Did SRL skills significantly increase more for pre-service teachers who engaged in supplementary CAAM practices as homework assignments compared to pre-service teachers who did not engage in CAAM?
- 2.
Did PS skills significantly increase more for pre-service teachers who engaged in supplementary CAAM practices as homework assignments compared to pre-service teachers who did not engage in CAAM practices?
- 3.
What was the relationship between pre-service teachers' SRL and PS skills?
Section snippets
Theoretical framework
The current research theoretically illustrates the link between SRL and PS skills and the potential of supplementary CAAM practices as homework assignments for promoting these skills.
Design
In this study, we paid attention to various decision-making problems as a stimulus for authentic pedagogical cases. In other words, our focus was on developing the pre-service teachers’ SRL and PS skills through generating argument maps for authentic cases (e.g., Tawfik & Jonassen, 2013), rather than directly teaching how to acquire these skills.
A pre-test post-test quasi-experimental study with a control group was used to explore the potential of engaging supplementary CAAM practices as
Characteristics of participants
Table 1 illustrates the main characteristics of pre-service teachers who participated in the study. All students in the control and experimental groups were taught by the same instructor. The mean age was 20.3 years. (SD = 0.71) in the CAAM group and 20.07 years. (SD = 0.87) in the control group. Over half of the sample was female (76.7%) in the CAAM group and (70.0%) in the control group. In terms of educational attainments of students’ parents, most of them had attended primary school in both
Discussion
The major objective of this experimental research is two-fold. First, the study examined the significance of CAAM practices as supplementary homework assignments in promoting pre-service teachers’ SRL (environment structuring, goal setting, time management, help-seeking, task strategies, and self-evaluation strategies). PS skills (problem-solving confidence, approach-avoidance style, and personal control). Second, the study also explored the relationship between these skills. The flow of the
Conclusion and instructional implications
Solving ill-structured problems can be difficult for students, in part because they need to justify or argue for their solutions and against others' ideas. In this process, it is vital to establish students’ conceptualization, control their actions and make their decisions. Given the reported advantages of CAAM for promoting higher-order thinking skills, and ironically, the apparent lack of empirical evidence on how technological tools influence promoting SRL and PS skills, integrating
Limitations and future research
Rather than coming to a definite conclusion, it is worth pointing out that all data presented and discussed should constitute a set for further studies because this research leads to addressing several questions in need of further investigation.
Although current research provides a significant opportunity to extend our understanding of how to improve PS and SRL skills through having students engage in supplementary CAAM practices, there is considerable space for further progress in determining
Credit author statement
Elanur Yilmaz: Conceptualization, Methodology, Software, Data curation, Formal analysis, Resources, Writing – original draft preparation, Validation, Visualization, Writing- Reviewing and Editing, Elif Sönmez: Conceptualization, Investigation, Supervision, Validation.
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