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Collaborative learning as a strategy for srl: sharing the load

Students can learn collaboratively by actively working together and putting effort into the attainment of a shared learning goal (e.g., Janssen, Kirschner, Erkens, Kirschner, & Paas, 2010; Johnson & Johnson, 2009; Kirschner, Sweller, Kirschner, & Zambrano, 2018; Slavin, 2014). Several meta-analyses have shown that collaborative learning is related to academic (individual and group) achievement (e.g., Lou, Abrami, & d’Apollonia, 2001; Roseth, Johnson, & Johnson, 2008; Springer, Stanne, & Donovan, 1999). There are several explanations from different disciplines as to why students learn from each other in collaborative settings. For example, social cohesion as a result of working interdependently in collaborative learning can aid learning (O’Donnell & O’Kelly, 1994). Also, collaborative learning can create cognitive conflict and students can question each other’s understanding, which can enhance learning (Slavin, 1996). Social interaction and social support are elements in collaborative learning that can create the opportunity for students to develop higher order skills such as reasoning and critical thinking skills (Johnson & Johnson, 2009). Moreover, another explanation for why students can learn from each other in collaborative learning settings is that collaborative learning can facilitate information processing and memory (Topping, 1996).

Strategies to make collaborative learning effective are combining group goals with individual accountability (e.g., grades based on average performance on individual assignments), appealing to student’s motivation (e.g., rewards, task attractiveness), and creating interdependence in goals, roles, and tasks (e.g., Slavin, 1996). For collaborative learning to be successful, students need to be able to operate as a team. A meta-analysis by DeChurch and Mesmer-Magnus (2010) showed that team cognition is essential for team effectiveness. Team cognition concerns the manner in which important knowledge for team functioning is organized and distributed within the team. There are two strategies to operationalize team cognition, that is shared mental models and transactive memory. Shared mental models are cognitive understandings of important aspects in the performance context that are shared (i.e., compatible) among the members of a team. Teams with shared mental models can operate efficiently without the need for overt communication, which is important for expert teams. Transactive memory can be seen as a cognitive architecture in which the knowledge of individual group members is included but also knowledge about who possesses what knowledge. Transactive memory is important if there is a degree of specialization or differentiation of knowledge within a team. DeChurch and Mesmer-Magnus (2010) showed that shared mental models and transactive memory were significantly related to team behavioral processes (e.g., planning, goal-setting, coordinating, and team-back-up behavior), motivational state, and team performance.

Similar to the concept of team cognition, it has been proposed that learners in a collaborative learning setting can be seen as an information processing system (Kirschner,

Paas, & Kirschner, 2009a, 2009b). In this system the information in the learning task and the cognitive load associated with the task can be divided among the learners in the group. This way the load can be divided among multiple collaborating working memories. According to the mutual cognitive interdependence principle, this collective WM can be introduced by effective collaborative learning in which students communicate and coordinate the relevant knowledge they have with each other (Kirschner, Paas, & Kirschner, 2011). From a CLT-perspective, dividing the demands of learning a complex task among different learners who are collaborating, can lead to a more effective and efficient way of learning (Paas & Sweller, 2012). That is, the collection of individual WM capacities of the group members can create an expanded processing capacity, which makes it advantageous to work together on more complex tasks (Kirschner et al., 2009a). Especially for complex tasks, sharing the load of high element interactivity across multiple WMs, instead of one, could be effective. Collaboration would serve as a scaffold for the learning process (Kirschner et al., 2018). This will only be effective if WM costs of communication and coordination are decreased by training or by learning in structured or scripted learning environments (Kirschner et al., 2018; Paas & Sweller, 2012). This means that collaboration would be a beneficial approach to learning in which communication and coordination are important strategies to make the collaboration successful.

A study by Kirschner et al. (2009b) investigated groups as information processing systems. Secondary school students learned how to solve biology problem-solving tasks either individually or in small groups. Students indicated their experienced mental effort (i.e., measure of cognitive load; Paas, 1992), and took a test consisting of retention and transfer tasks. The results showed that students who learned in small groups invested less mental effort during the learning phase. Most importantly, an interaction between the type of test (retention or transfer) and condition was found, which indicated that students who learned individually showed more efficient retention performance, and learners who learned collaboratively showed more efficient transfer performance. Presumably, because learners in the small groups could use each other’s processing capacity (i.e., information processing system), they were able to process the learning content more deeply and construct higher quality schemata in long-term memory.

To sum up, collaborative learning was found to be successful (e.g., Roseth et al., 2008) and, more importantly, has the potential of ameliorating the limitations of individual WM (e.g., Kirschner et al., 2011). Looking back at the problem of inaccurate monitoring and its effect on the SRL process, possibly collaborative learning could be a way to free up cognitive resources that could then be used to monitor and regulate learning processes more successfully at both the individual and the group level. In order to make collaborative learning effective, several strategies such as training communication and coordination between team members are important. Both from a CLT perspective and the concept of team cognition, one could argue that there is potentially more WMC (Kirschner et al., 2011) in effective collaborative learning, which in turn could affect behavioral processes such as planning and goal-setting if team cognition is achieved (DeChurch & Mesmer-Magnus, 2010). Hence, effective collaborative learning could also be a scaffold for other SRL processes like monitoring and control at the individual level and at the group level.

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