Modeling temporal self-regulatory processing in a higher education biology course

Greene, Jeffrey A.; Plumley, Robert D.; Urban, Christopher J.; Bernacki, Matthew L.; Gates, Kathleen M.; Hogan, Kelly A.; Demetriou, Cynthia; Panter, A. T.

doi:10.1016/j.learninstruc.2019.04.002

Cited by 40 publications

(20 citation statements)

References 25 publications

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“…Accordingly, we expect students to dynamically and accurately monitor and regulate their cognitive strategies while using MetaTutor to learn about the human circulatory system. While metacognitive processes are ideally captured using trace methods such as concurrent think-aloud protocols ( Azevedo and Cromley, 2004 ; Greene and Azevedo, 2009 ; Greene et al, 2021 ), during a MetaTutor learning session, metacognitive processes can either be prompted by the PA, or students can self-initiate the use of the same metacognitive processes via the SRL Palette (see Figure 1 ). The SRL Palette allows students to judge how well they understand the content they are currently reading (JOL), rate how familiar they are with the content they are currently reading (FOK), and assess how relevant the content text and diagram are for accomplishing their current sub-goal (CE).…”

Section: Role Of Metacognition During Learning With Metatutormentioning

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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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 Metacognition During Learning With Metatutormentioning

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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“…Zimmerman and Moyla point out that self-efficacy is a source of motivation that precedes efforts to learn and affects students' preparation and their willingness to self-regulate their learning [70]. The empirical research show that self-efficacy is directly associated with self-regulated learning strategies [72,73], and both self-efficacy and self-regulated learning strategies contributed to the prediction of students' proficiency [74][75][76].…”

Section: Self-regulated Learning Modelmentioning

confidence: 99%

Self-Regulation in E-Learning Environment

et al. 2021

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The COVID-19 pandemic has contributed to the accelerated spread of e-learning around the world. In e-learning, self-regulation becomes more relevant than ever. Reducing the influence of traditional features of the face-to-face learning environment and increasing the impact of the e-environment place high demands on students’ self-regulation. The author’s self-regulation e-learning model emphasizes the position of e-learning at the intersection of the electronic environment and the learning environment. We observe a collision of the concepts of these two environments. The Internet is a more common environment that provokes the use of unacceptable tools and hints, which is a logical consequence of such behavior to pass the test, and not to gain knowledge. Therefore, the most important thing is that students have their own goals and strategies, and use the large resources of the electronic environment for development, and not for cheating. The authors conducted a survey (N = 767), which showed that students rate their self-efficacy of online learning higher in the e-environment than in the offline learning environment. Self-regulation indicators are the highest in the field of environment, and the lowest when setting goals and in time management.

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“…Computer-supported learning environments (e.g., virtual experiments) give insight into students' actions during learning by means of log-file data, screen captures, and audio and video recordings (Greene et al, 2021). These data sources incorporate temporal information about the timing and the sequence of learning activities.…”

Section: Epistemic Network Analysis As a Path To Analyze Co-occurrencesmentioning

confidence: 99%

It takes two to tango: How scientific reasoning and self-regulation processes impact argumentation quality

Omarchevska

Lachner

Richter

et al. 2021

Journal of the Learning Sciences

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Background: Improving scientific reasoning and argumentation are central aims of science education. Because of their complex nature, self-regulation is important for successful scientific reasoning. This study provides a first attempt to investigate how scientific reasoning and self-regulation processes conjointly impact argumentation quality. Methods: In a study with university students (N = 30), we used fine-grained process data of scientific reasoning and self-regulation during inquiry learning to investigate how the co-occurrences between scientific reasoning and self-regulation processes are associated with argumentation quality. Findings: When modeling the co-occurrence of scientific reasoning and self-regulation processes using epistemic network analysis, differences between students showing either high or low argumentation quality become apparent. Students who showed high argumentation quality engaged in different scientific reasoning processes together more often than students with low argumentation quality, and they made more connections between self-regulation and scientific reasoning processes. Contribution: These findings offer educational implications for teaching scientific reasoning. Integrating self-regulation and scientific reasoning during instruction could be beneficial for improving scientific reasoning and argumentation.

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Modeling temporal self-regulatory processing in a higher education biology course

Cited by 40 publications

References 25 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

Self-Regulation in E-Learning Environment

It takes two to tango: How scientific reasoning and self-regulation processes impact argumentation quality

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