An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

Abstract

Computer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. What is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation. Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context. What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context. The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context. Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently.

An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective

“…(adapted from Holland, 1996) orative meaning-making processes are interwoven with the multimodality and dynamics of individuals, groups, learning environments, technologies, and resources during the CSCL process. Moreover, the CSCL community has paid attention to analyzing and understanding CSCL processes from the group or system perspectives, such as collaborative cognitive load, group-level metacognition, or group cognition (Dindar et al, 2020;Kuhn et al, 2020;Zheng et al, 2021). Overall, complex adaptive systems theory has the potential to contribute new perspectives to our understanding of educational phenomena, particularly the collaborative learning process, from a holistic, systematic, and non-linear perspective Holland, 1996;Vogler et al, 2017).…”

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

“…The main reason for adapting Bloom’s cognitive domain taxonomy was that a previous study found that Bloom’s taxonomy was appropriate for measuring cognitive engagement (Crompton et al, 2019 ). Metacognitive engagement was automatically classified into planning, monitoring, reflection and evaluation as well as off-topic information adapted from Zheng et al ( 2021 ). Behavioral engagement was automatically classified into knowledge building, regulation, support and agreement, asking questions, and off-topic information based on the Zheng et al ( 2021 ).…”

“…Metacognitive engagement was automatically classified into planning, monitoring, reflection and evaluation as well as off-topic information adapted from Zheng et al ( 2021 ). Behavioral engagement was automatically classified into knowledge building, regulation, support and agreement, asking questions, and off-topic information based on the Zheng et al ( 2021 ). Emotional engagement was automatically classified into positive, negative, and neutral based on Pang and Lee ( 2008 ).…”

“…Second, the online discussion transcripts of the 40 groups were analyzed via the computerassisted knowledge graph method to examine the collaborative knowledge-building level. The computer-assisted knowledge graph method was developed by Zheng (2017), and has been validated in previously published studies (Zheng et al, 2021(Zheng et al, , 2022. It consists of three steps.…”

“…Table 4 The coding scheme of socially shared regulation SSR coding scheme has been validated in a previous study (Zheng et al, 2021); it recognized SSR behaviors more effectively than other coding schemes. Table 4 shows the SSR coding scheme.…”