Revealing Data Feature Differences Between System- and Learner-Initiated Self-regulated Learning Processes Within Hypermedia

Wiedbusch, Megan; Dever, Daryn A.; Wortha, Franz; Cloude, Elizabeth B.; Azevedo, Roger

doi:10.1007/978-3-030-77857-6_34

Cited by 7 publications

(9 citation statements)

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“…More recently, Wiedbusch et al (2021b) examined why learners lack sufficient SRL skills to successfully implement strategies (e.g., JOL, note-taking, self-testing, etc.). The authors used principal component analysis (PCA) on log files to further explore underlying patterns in the frequency of strategy deployment occurring with and without PA scaffolding.…”

Section: Role Of Cognitive Strategies During Learning With Metatutormentioning

confidence: 99%

“…Fourth, we argue that the concepts of meta-learning, meta-thinking, and meta-reasoning from the psychological and computational sciences are key to acquiring, internalizing, using, and transferring SRL knowledge and skills across tasks, domains, and contexts ( Cox, 2011 ; Cox et al, 2016 ; Ackerman and Thompson, 2017 ). Fifth, data visualizations of students’ multimodal SRL processes are key to enhancing their understanding of SRL, just as visualizations are key in designing teacher dashboards that provide actionable data for effective instructional decision-making thus creating a human-AI complementarity ( Molenaar and Knoop-van Campen, 2019 ; Holstein and Aleven, 2021 ; Wiedbusch et al, 2021b ). Sixth, while we acknowledge that cognition, metacognition, and emotions are important for SRL, more attention needs to be paid to the role of motivation (as states that also fluctuate during task performance, perhaps at different time epochs, and that can be deeply intertwined to the concept of flow ( Csikszentmihalyi et al, 2018 ).…”

Section: Lessons Learned and Open Challengesmentioning

confidence: 99%

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Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

et al. 2022

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Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students’ SRL while they learn about the human circulatory system. MetaTutor’s architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners’ cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

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Section: Role Of Cognitive Strategies During Learning With Metatutormentioning

confidence: 99%

Section: Lessons Learned and Open Challengesmentioning

confidence: 99%

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

et al. 2022

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“…CE); and (2) fatigue from exerting themselves as self-regulating learners due to the high cognitive load required to deploy effective SRL strategies (Wang & Lajoie, 2023). Findings from this research question enhance current literature and work on PA scaffolding in that PAs are necessary for learners to be supported in accurate and efficient SRL strategy use (Dever et al, 2022;Wiedbusch et al, 2021). Furthermore, literature supports the fading of PA scaffolding as learners demonstrate SRL competency (Davis, 2015;Noroozi et al, 2017).…”

Section: Discussionmentioning

confidence: 67%

“…Similarly, Lalle et al (2017) showed that learners who were performance goal oriented and engaged with PAs more outperformed their peers who spent attentional effort on the PAs. Finally, Wiedbusch et al (2021) used principal component analysis to find that PAs effectively scaffolded SRL strategies as the PAs themselves followed Winne's (2018) information process theory of SRL, suggesting that theoretically grounded rule-based prompt and feedback can scaffold adaptive SRL strategy use. It is important to note that these studies are situated within the MetaTutor ITS and that these findings consistently showed that SRL-based PAs improved learning outcomes.…”

Section: Pedagogical Agents As Scaffolders Of Srlmentioning

confidence: 99%

Investigating pedagogical agents' scaffolding of self‐regulated learning in relation to learners' subgoals

Dever,

Wiedbusch,

Romero

et al. 2024

Brit J Educational Tech

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Intelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self‐regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in scaffolding and teaching SRL during learning with MetaTutor, an ITS on the human circulatory system. Undergraduates (N = 118) were randomly assigned to a condition: Control Condition (i.e. learners could only self‐initiate SRL strategies) and Prompt and Feedback Condition (i.e. PAs prompted learners to engage in SRL). Learners' log‐file data captured when strategies were used, the initiator of the strategy (i.e. learner and PA), and the relevance of instructional content pages in relation to learner subgoals. While results showed that PAs were effective scaffolders of SRL in which they prompted learners to engage in SRL strategies more when content was relevant towards their subgoals and as time on page and task increased, there were mixed findings about the effectiveness of PAs as teachers of SRL. Findings show how production rules guiding PA prompts can improve their scaffolding and teaching of SRL across the learning task – through contextualizing SRL strategies to the instructional content and in relation to the relevance of the content to learners' subgoals.Practitioner notesWhat is already known about this topic Most learners struggle to efficiently and effectively use self‐regulated learning (SRL) strategies to attain goals and subgoals. There is a need for SRL to be scaffolded for learners to manage multiple goals and subgoals while learning about complex STEM topics. Intelligent tutoring systems (ITSs) typically incorporate pedagogical agents (PAs) to prompt learners to engage in SRL strategy and provide feedback. There are mixed findings on the effectiveness of PAs in scaffolding learners' SRL. What this paper adds We consider PAs not only scaffolders but also teachers of SRL. Results showed that while PAs encouraged the use of SRL strategies when the content was relevant to subgoals, they did not discourage the use of SRL strategies when the content was not relevant. Results for this study were mixed in their support of PAs as teachers of SRL. Learners increasingly depended on PAs to prompt SRL strategies as time on task progressed. Implications for practice and/or policy PAs are effective scaffolders of SRL with more research needed to understand their role as teachers of SRL. PA scaffolding is more essential as time on task progresses. When deploying specific cognitive and metacognitive SRL strategies, the relevance of the content to learners' subgoals should be taken into account.

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“…Emerging evidence points to key roles that multimodal data can play in this context (Jang et al, 2017;Taub et al, 2021) and has sparked promising data-driven techniques for discovering insights into SRL processes (Cloude et al, 2021a;Wiedbusch et al, 2021). Yet, major issues remain regarding roles for various SRL processes (e.g., cognitive and metacognitive; Mayer, 2019) and their properties: evolution or recursive nature over time, frequency and duration, interdependence, quantity vs. quality (e.g., accuracy in metacognitive monitoring), and methods for fusing multimodal trace data to link SRL processes to learning task performance.…”

Section: Introductionmentioning

confidence: 99%

System design for using multimodal trace data in modeling self-regulated learning

et al. 2022

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Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how such data relate to learning and performance. Analyzing and interpreting multimodal data about learners' SRL processes as they work in real-time is conceptually and computationally challenging for researchers. In this paper, we discuss recommendations for building a multimodal learning analytics architecture for advancing research on how researchers or instructors can standardize, process, analyze, recognize and conceptualize (SPARC) multimodal data in the service of understanding learners' real-time SRL and productively intervening learning activities with significant implications for artificial intelligence capabilities. Our overall goals are to (a) advance the science of learning by creating links between multimodal trace data and theoretical models of SRL, and (b) aid researchers or instructors in developing effective instructional interventions to assist learners in developing more productive SRL processes. As initial steps toward these goals, this paper (1) discusses theoretical, conceptual, methodological, and analytical issues researchers or instructors face when using learners' multimodal data generated from emerging technologies; (2) provide an elaboration of theoretical and empirical psychological, cognitive science, and SRL aspects related to the sketch of the visionary system called SPARC that supports analyzing and improving a learner-instructor or learner-researcher setting using multimodal data; and (3) discuss implications for building valid artificial intelligence algorithms constructed from insights gained from researchers and SRL experts, instructors, and learners SRL via multimodal trace data.

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Revealing Data Feature Differences Between System- and Learner-Initiated Self-regulated Learning Processes Within Hypermedia

Cited by 7 publications

References 16 publications

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

Lessons Learned and Future Directions of MetaTutor: Leveraging Multichannel Data to Scaffold Self-Regulated Learning With an Intelligent Tutoring System

Investigating pedagogical agents' scaffolding of self‐regulated learning in relation to learners' subgoals

System design for using multimodal trace data in modeling self-regulated learning

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