The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor

Lallé, Sébastien; Taub, Michelle; Mudrick, Nicholas V.; Conati, Cristina; Azevedo, Roger

doi:10.1007/978-3-319-61425-0_13

Cited by 12 publications

(18 citation statements)

References 19 publications

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“…Significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral), though the relationships differed between virtual PAs ( Harley et al, 2016a ). These results, along with those from a follow-up study examining goal orientations ( Lallé et al, 2017a , b , 2018 , 2021 ) were critical in establishing the need to contextualize the source of emotion in considering emotional interventions and the design of virtual PAs.…”

Section: Role Of Emotions During Learning With Metatutormentioning

confidence: 91%

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 Emotions During Learning With Metatutormentioning

confidence: 91%

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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“…The idea of giving students the ability to create and configure their own prompts is not only driven by the transfer of the flexibility of modern CBLEs directly to their integrated support, as mentioned in the introduction, but also by the phenomenon of students' poor compliance with instructional tools (e.g., Clarebout & Elen, 2006;Lallé et al, 2017;Schworm & Gruber, 2012), particularly with metacognitive prompts (e.g., Bannert & Mengelkamp, 2013). Students in past studies have complained that the prompts restricted their learning or indicated that they (partly) did not use the prompt in the intended manner (e.g., Bannert et al, 2015).…”

Section: Modifying the Design Of Metacognitive Promptsmentioning

confidence: 99%

“…to more descriptive attributes (e.g., Bannert et al, 2015;Daumiller & Dresel, 2018;Lallé, Taub, Mudrick, Conati, & Azevedo, 2017;Wong, Khalil, Baars, de Koning, & Paas, 2019). Bannert et al (2015), for example, investigated the navigation behavior of students in regulating their learning to gain insight into whether and how students select pages with relevant content from learning websites.…”

Section: Introductionmentioning

confidence: 99%

Do self-created metacognitive prompts promote short- and long-term effects in computer-based learning environments?

Engelmann

Bannert

Melzner

2021

RPTEL

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Students must engage in self-regulated learning in computer-based learning environments; however, many students experience difficulties in doing so. Therefore, this study aims to investigate self-created metacognitive prompts as a means of supporting students in their learning process and improving their learning performance. We conducted an experimental study with a between-subject design. The participants learned with self-created metacognitive prompts (n = 28) or without prompts (n = 29) in a hypermedia learning environment for 40 min while thinking aloud. In a second learning session (stability test), all participants learned about a different topic without prompts. The results showed no clear effect of the self-created metacognitive prompts on the learning process and performance. A deeper analysis revealed that students’ prompt utilization had a significant effect on performance in the second learning session. This study contributes to the research investigating how students can be supported in ways that enhance their learning process and performance.

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“…We hope that future studies will explore other gaze metrics to assess understanding of agency in this or similar context, such as transitions of gaze between relevant AOIs, or length of fixations, that has been proposed to correspond to the depth of information processing (Schwonke et al 2017). Fixation length was measured, together with rate of fixations, in a study by Lallé et al (2017) where school children were using an intelligent tutoring system including pedagogical agents that scaffolded the children in various ways.…”

Section: Exploring Children's Understanding Of Agencymentioning

confidence: 99%

Preschoolers’ Understanding of a Teachable Agent-Based Game in Early Mathematics as Reflected in their Gaze Behaviors – an Experimental Study

Gulz

Londos

Haake

2020

Int J Artif Intell Educ

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This study investigated how preschool children processed and understood critical information in Magical Garden, a teachable agent-based play-&-learn game targeting early math. We analyzed 36 children's (ages 4-6 years) real-time behavior during game-use to explore whether children: (i) processed the information meant to support number sense development; (ii) showed an understanding of the teachable agent as an entity with agency. An important methodological goal was to go beyond observable behavior and shed some light on how cognitive processing and understanding in children of such young age can be studied. First, the children played Magical Garden for three weeks to get acquainted with the game. Second, in an experimental part of the study, the children's gaze behaviors were measured during 5 rounds of interaction with an experimental version of one of the sub-games. The analyses suggest that two of the gaze behaviors were positively correlated with the game performance measure, as hypothesized. Another result was that children looked at the teachable agent significantly more often when the teachable agent had been in charge of gameplay than when it had not. This can be interpreted as an indication that the children had an understanding of their teachable agent as an entity that, like themselves and unlike other dynamic visual elements in the game, made decisions based on own 'knowledge'. In a broader context, the findings are important in showing the potential gains of combining log data with eye-tracking data for developing and refining AI algorithms for adaptive individual feedback and scaffolding.

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The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor

Cited by 12 publications

References 19 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

Do self-created metacognitive prompts promote short- and long-term effects in computer-based learning environments?

Preschoolers’ Understanding of a Teachable Agent-Based Game in Early Mathematics as Reflected in their Gaze Behaviors – an Experimental Study

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