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Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates during AI-Supported Peer Tutoring?

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Title: Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates during AI-Supported Peer Tutoring?
Language: English
Authors: Conrad Borchers, Kexin Yang, Jionghao Lin, Nikol Rummel, Kenneth R. Koedinger, Vincent Aleven
Source: International Educational Data Mining Society. 2024.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 14
Publication Date: 2024
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1822861
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Elementary Secondary Education
Junior High Schools
Middle Schools
Secondary Education
Descriptors: Artificial Intelligence, Peer Teaching, Tutoring, Technology Uses in Education, Dialogs (Language), Mathematics Instruction, Elementary Secondary Education, Problem Solving, Instructional Effectiveness, Intelligent Tutoring Systems, Middle School Students
Abstract: Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act classification with cognitive modeling of fine-grained learning processes during problem-solving to illuminate this question. We estimate how much peer-tutored students improve in a collaborative tutoring system for linear equation-solving in K-12 mathematics in relationship to the peer dialog types they engage in. This work establishes a reliable BERT classifier with an accuracy of close to 80% to classify chat messages during peer tutoring into minimal, facilitative, and constructive, serving as instructional factors. Based on data from 394 students, peer tutor dialog was rare. Only 8% of tutee problem-solving steps were followed by peer tutor chat messages. Still, facilitative tutor dialog was associated with an increased tutee learning rate. Meanwhile, tutor dialog classified as constructive was associated with lower learning rates. Content analysis suggested that such dialog often reinforced incorrect solutions, gave away answers, or was unrelated to the taught content. Hence, considering problem-solving solution contexts could improve the assessment of peer tutoring dialog. Peer tutors engaging in little dialog could be attributed to the high cognitive demand of learning to tutor while still learning the content they tutor on. Providing peer tutors with instructional support to engage in constructive dialog may improve the tutee's learning. [For the complete proceedings, see ED675485.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675550
Database: ERIC
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  Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2024.
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  Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
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  Label: Abstract
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  Data: Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act classification with cognitive modeling of fine-grained learning processes during problem-solving to illuminate this question. We estimate how much peer-tutored students improve in a collaborative tutoring system for linear equation-solving in K-12 mathematics in relationship to the peer dialog types they engage in. This work establishes a reliable BERT classifier with an accuracy of close to 80% to classify chat messages during peer tutoring into minimal, facilitative, and constructive, serving as instructional factors. Based on data from 394 students, peer tutor dialog was rare. Only 8% of tutee problem-solving steps were followed by peer tutor chat messages. Still, facilitative tutor dialog was associated with an increased tutee learning rate. Meanwhile, tutor dialog classified as constructive was associated with lower learning rates. Content analysis suggested that such dialog often reinforced incorrect solutions, gave away answers, or was unrelated to the taught content. Hence, considering problem-solving solution contexts could improve the assessment of peer tutoring dialog. Peer tutors engaging in little dialog could be attributed to the high cognitive demand of learning to tutor while still learning the content they tutor on. Providing peer tutors with instructional support to engage in constructive dialog may improve the tutee's learning. [For the complete proceedings, see ED675485.]
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    Languages:
      – Text: English
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      Pagination:
        PageCount: 14
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Peer Teaching
        Type: general
      – SubjectFull: Tutoring
        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Dialogs (Language)
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      – SubjectFull: Mathematics Instruction
        Type: general
      – SubjectFull: Elementary Secondary Education
        Type: general
      – SubjectFull: Problem Solving
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      – SubjectFull: Instructional Effectiveness
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      – SubjectFull: Intelligent Tutoring Systems
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      – SubjectFull: Middle School Students
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      – TitleFull: Combining Dialog Acts and Skill Modeling: What Chat Interactions Enhance Learning Rates during AI-Supported Peer Tutoring?
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