Using learning analytics to explore the multifaceted engagement in collaborative learning.

Autor: Xing, Wanli, Zhu, Gaoxia, Arslan, Okan, Shim, Jaesub, Popov, Vitaliy
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Zdroj: Journal of Computing in Higher Education; Dec2023, Vol. 35 Issue 3, p633-662, 30p
Abstrakt: Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement measurements to address these issues through employing learning analytics and educational data mining (e.g., Azevedo in Educ Psychol 50(1):84–94, 2015; Henrie in Comput Educ 90:36–53, 2015). This study responded to this call by developing learning analytics to study the multifaceted aspects of engagement (i.e., group behavioral, social, cognitive, and metacognitive engagement) and its impact on collaborative learning. The results show that group behavioral engagement and group cognitive engagement have a significantly positive effect on group problem-solving performance; group social engagement has a significantly negative effect; the impact of group metacognitive engagement is not significant. Furthermore, group problem-solving performance has a significant positive effect on individual cognitive understanding, which partially mediates the impact of group behavioral engagement and fully mediates the impact of group social engagement on individual cognitive understanding. The findings have important implications for developing domain-specific learning analytics to measure students' sub-constructs of engagement in CSCL. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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