Making strides towards AI-supported regulation of learning in collaborative knowledge construction

Ouyang, Fan; Wu, Mian; Zhang, Liyin; Xu, Weiqi; Zheng, Luyi; Cukurova, Mutlu

doi:10.1016/j.chb.2023.107650

Cited by 20 publications

(5 citation statements)

References 46 publications

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“…Furthermore, a learning analytic dashboard (Akçapınar & Hasnine, 2022) and monitoring learners' interactions within the learning environment (Yilmaz et al., 2022) could also be used to provide feedback to learners with low SRL skills. Other studies also report that personalized feedback on knowledge construction (Ouyang et al., 2023), motivational profiles (Rienties et al., 2012) and planning phases in collaborative settings also improved SRL skills. Another set of two studies used educational agents to scaffold team‐based learning (Kumar, 2021) and learning‐by‐teaching (Lee et al., 2021).…”

Section: Resultsmentioning

confidence: 91%

“…This group of study focused on providing: (1) adaptive and personalized feedback to artefacts produced by students to help them achieve a better quality of artefact reduction (Saqr & López‐Pernas, 2023; Wambsganss, Janson, Käser, et al., 2022; Wambsganss, Janson, & Leimeister, 2022; Wambsganss, Söllner, et al., 2022); (2) means for reflecting on their own learning behaviour (Akçapınar & Hasnine, 2022; Yilmaz et al., 2022); and (3) different adaptive and personalized scaffoldings to collaborative learning processes to foster better use of self‐regulatory skills (Hadwin et al., 2018; Ouyang et al., 2023; Rienties et al., 2012). All these studies focused on improving a specific subset of SRL skills.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Ouyang et al. (2023) analysed multi‐level characteristics (individual–group) of collaborative knowledge construction (CKC) and classified learner behaviours into CKC states, to enable personalized scaffolding opportunities. Within the same topic of CKC, Geng et al.…”

Section: Resultsmentioning

confidence: 99%

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Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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Adaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

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Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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“…Gorman et al (2020) used computational, quantitative models (including discrete recurrence, non-linear prediction algorithms, and average mutual information) to detect real-time changes in group communication reorganization patterns during collaborative training. The main premise of this emerging research stream is that, compared to traditional learning analytics, such as social network analysis (e.g., Ouyang, 2021) or content analysis (e.g., Jeong, 2013), integration of algorithm-enabled methods in learning analytics and educational data mining can better deal with complex, nonlinear information, as well as extract and represent multi-level and high-dimensional features of CSCL (de Carvalho & Zárate, 2020;Ouyang et al, 2023).…”

Section: The Multimodal Collaborative Learning Analytics Of Cpsmentioning

confidence: 99%

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

Ouyang

Cukurova

2023

Intern. J. Comput.-Support. Collab. Learn

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Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.

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“…In the context of (S)SRL research, AI has enabled promising facilities for better understanding and supporting learning regulation (Molenaar, 2022; Nguyen, Järvelä, Wang, et al, 2022). Specifically, these approaches have enabled the analysis of the multi‐level and multifaceted characteristics of regulation in collaborative learning (Ouyang, Wu, et al, 2023; Ouyang, Xu, et al, 2023). In the past, the complexity and dynamics of collaborative learning often required researchers to examine different facets of regulation separately.…”

Section: Introductionmentioning

confidence: 99%

Human and artificial intelligence collaboration for socially shared regulation in learning

Järvelä

Nguyen

Hadwin

2023

Brit J Educational Tech

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Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human‐AI collaboration by introducing a novel trigger concept and a hybrid human‐AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human‐AI collaboration in facilitating effective SSRL in teaching and learning. Practitioner notesWhat is already known about this topic For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor. Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning. It is questionable whether traditional theories of how people learn are useful in the age of AI. What this paper adds Introduces a trigger concept and a hybrid Human‐AI Shared Regulation in Learning (HASRL) model to offer insights into how the human‐AI collaboration could occur to operationalize SSRL research. Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning. Provides clear suggestions for future human‐AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills. Implications for practice and/or policy Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning. Researchers and practitioners could benefit from methodological development incorporating human‐AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.

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Making strides towards AI-supported regulation of learning in collaborative knowledge construction

Cited by 20 publications

References 46 publications

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

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

Human and artificial intelligence collaboration for socially shared regulation in learning

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