Measuring Self-Regulated Learning Skills Through Social Interactions in a Teachable Agent Environment

Biswas, Gautam; Jeong, Hogyeong; Kinnebrew, John S.; Sulcer, Brian; Roscoe, Rod D.

doi:10.1142/s1793206810000839

Cited by 155 publications

(87 citation statements)

References 21 publications

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“…Within the broader learning by teaching paradigm, research has focused primarily on learning outcomes (e.g., Biswas et al, 2010;Palinscar & Brown, 1984;Peets et al, 2009;Roscoe & Chi, 2007). Few studies have explored self-reported or actual cognitive or metacognitive processes during learning by teaching (De Backer et al, 2012;King et al, 1998;Roscoe, 2014;Roscoe & Chi, 2007).…”

Section: The Current Studymentioning

confidence: 99%

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Muis

Psaradellis

Chevrier

et al. 2016

Journal of Educational Psychology

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We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technologyrich learning environment. Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for learning (control condition). Students' conceptualizations (task definitions) of the problem, self-regulatory processes, and mathematics achievement were then compared across the 2 conditions. To measure task definitions of the mathematics problem, students developed concept maps of the problem using a tablet application. To capture self-regulatory processes, students were asked to think out loud as they solved the problem. Results revealed that students in the learning by preparing to teach intervention developed a more detailed and better-organized concept map of the problem compared with students in the control condition. Students in the learning by preparing to teach intervention also engaged in more metacognitive processing strategies and had higher levels of mathematics problem solving achievement compared with students in the control condition. No differences were found, however, in planning and goal setting or in use of cognitive strategies across the 2 conditions. Implications of this research suggest students' initial task definitions may be a key factor in differences found when learning by teaching compared with solely learning for learning.

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Section: The Current Studymentioning

confidence: 99%

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Muis

Psaradellis

Chevrier

et al. 2016

Journal of Educational Psychology

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show abstract

“…Similarly, researchers have identified patterns of behavior in the Betty's Brain system that are indicative of low and high levels of self-regulation (Biswas et al 2009). Prompting students to use SRL strategies when these patterns of behavior occur has shown promise in improving student learning.…”

Section: Srl and Intelligent Tutoring Systemsmentioning

confidence: 99%

“…For this reason, the ability to identify and support students' SRL strategies has been the focus of much work in the AI in Education community (Azevedo et al 2010;Biswas et al 2009;Conati and VanLehn 2000). Such work has focused primarily on examining SRL in highly structured problem-solving and learning environments.…”

mentioning

confidence: 99%

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Sabourin

Shores

Mott

et al. 2013

Int J Artif Intell Educ

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Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has proven challenging. This paper presents an initial investigation into selfregulated learning in a game-based learning environment. Evidence of goal setting and monitoring behaviors is examined through students' text-based responses to update their 'status' in an in-game social network. Students are then classified into SRL-use categories. This article describes the methodology used to classify students and discusses analyses demonstrating the learning and gameplay behaviors across students in different SRL-use categories. Finally, machine learning models capable of predicting these classes early in students' interaction are presented.

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“…Interdisciplinary researchers have been designing and developing computer-based learning environments (CBLEs) (e.g., multimedia, hypermedia, intelligent tutoring systems, multi-agent systems) to foster and promote effective self-regulated learning in students as they learn about various topics, such as biology, physics, and ecology (Aleven et al, 2010;Azevedo & Aleven, 2013;Biswas et al, 2010;Graesser et al, 2007;Jonassen & Land, 2012;D'Mello et al, 2013;Lajoie et al, 2013;Lester et al, 2013;Woolf, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Some CBLEs are multi-agent, such that they are programmed to include pedagogical agents, PAs, who are present to assist students by providing scaffolding and feedback during learning, problem solving, strategy training, and skill acquisition (Azevedo et al, 2012a;Biswas et al, 2010;D'Mello et al, 2013;Graesser & McNamara, 2010;Lester et al, 2013). In addition to content learning, these agents are programmed to assist learning about different aspects of SRL, such as planning, goal-setting, metacognitive monitoring, strategy use, and reflection (see Azevedo & Aleven, 2013;Azevedo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Taub¹,

Azevedo²,

Bouchet³

et al. 2014

Computers in Human Behavior

113

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Measuring Self-Regulated Learning Skills Through Social Interactions in a Teachable Agent Environment

Cited by 155 publications

References 21 publications

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Learning by preparing to teach: Fostering self-regulatory processes and achievement during complex mathematics problem solving.

Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

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