Leveraging Regulative Learning Facilitators to Foster Student Agency and Knowledge (Co-)Construction Activities in CSCL Environments

Leveraging Regulative Learning Facilitators to Foster Student Agency and Knowledge (Co-)Construction Activities in CSCL Environments

Tayebeh Sadegh
Copyright: © 2022 |Pages: 15
DOI: 10.4018/IJOPCD.293209
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Abstract

In this study, by combing three research fields of regulative learning, learning analytics (LA) and students' agency in computer-supported collaborative learning (CSCL), the authors tried to illuminate how students regulated their learning though group composition, peer assessment, and LA strategies to foster agency and knowledge (co-)construction activities in CSCL environments. To fulfill this aim, 60 students from Fasa University, Iran were assigned to experimental and control groups based on their self-regulated learning (SRL) skills (high, medium, low, and very low SRL skills). Data analysis confirmed the positive relationship between group composition, peer assessment, LA, regulative learning, agency, and knowledge (co-)construction. The results revealed that through applying group composition, peer assessment, and LA, socially-shared regulated learning, co-regulated learning, and consequently SRL skills were improved, which finally fosters agency and knowledge (co-)construction activities in CSCL environments.
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Introduction

For many researchers, collaboration appears to be one of the most promising ways, not only to promote, but also to achieve desired changes in teaching and learning practices (Bruner, 1996; Engeström, 1987; Strauß and Rummel, 2020). Collaboration, as it is understood in CSCL (Dillenbourg 1999), is much more than just communication between individuals, contributing information to each other, or coordinating activities to reach individual or shared goals. Roschelle and Teasley (1995) suggested that effective collaboration is constituted through interactive, dynamic, and sustained dialogues over time leading to knowledge construction as the outcome of collaboration. Furthermore, approaches to knowledge construction often show collaboration activities that are explained as a mere collection of individual actions rather than collective achievement (Barron, 2003). In order to bring about the production of new knowledge and advancement of individual and collective knowledge, Scardamalia (2002) proposed the focus should be deviated from just individual performance of assigned collaborative tasks and include collective contribution in knowledge advancement. She used the term epistemic agency as characterized by the improvement of ideas through making collective contributions and through relating personal ideas to one another. Epistemic agency in Scardamalia’s viewpoint comprises individual intentionality for idea improvement which was later extended by Damsa and colleagues (2010). By placing focus on agency at collective level, they stepped further from just improving ideas to shared knowledge construction and knowledge practices and introduced shared epistemic agency as “a capacity that enables groups to deliberately carry out collaborative, knowledge-driven activities with the aim of creating shared knowledge” (Damsa et al., 2010, p. 154). In 2017, Jääskelä criticized these studies for not taking the agency from holistic conceptual base but rather focusing on unitary dimension of agency (e.g., epistemic agency). By extending the focus beyond unitary dimensions, they offered a novel multidimensional conceptualization of student agency consisting of personal, relational and participatory resource domain. Additionally, Jääskelä and colleagues (2020) developed another study to link this conceptual and methodological development on student agency to LA. In this study they tried to pave the way for supporting students’ agency through developing the tools and algorithms for analyzing agency experiences. LA is very effective in terms of combining and analyzing students’ historical data, developing students' learning performances, enhancing the effectiveness of learning, identifying the students who are at risk of academic failure, thereby providing interventions (Lu et al., 2017; Tlili et al., 2019). These intervention strategies play a major role in LA to enable at-risk students to improve their learning by adjusting their behavior (e.g., Ma et al., 2015; Yilmaz, 2020).

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