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Enriching Multimodal Data: A Temporal Approach to Contextualize Joint Attention in Collaborative Problem-Solving

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Title: Enriching Multimodal Data: A Temporal Approach to Contextualize Joint Attention in Collaborative Problem-Solving
Language: English
Authors: Yiqiu Zhou (ORCID 0009-0000-9004-9307), Jina Kang (ORCID 0000-0002-7043-5427)
Source: Journal of Learning Analytics. 2023 10(3):87-101.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 15
Publication Date: 2023
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1822796
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Attention Control, Cooperative Learning, Data Analysis, Computer Assisted Instruction, Learning Analytics, Problem Solving, Astronomy, Science Instruction, Computer Simulation, Comparative Analysis, High Achievement, Low Achievement, Interaction Process Analysis, Introductory Courses, Undergraduate Students, Learning Management Systems
ISSN: 1929-7750
Abstract: Collaboration is a complex, multidimensional process; however, details of how multimodal features intersect and mediate group interactions have not been fully unpacked. Characterizing and analyzing the temporal patterns based on multimodal features is a challenging yet important work to advance our understanding of computer-supported collaborative learning (CSCL). This paper highlights the affordances, as well as the limitations, of different temporal approaches in terms of analyzing multimodal data. To tackle the remaining challenges, we present an empirical example of multimodal temporal analysis that leverages multi-level vector autoregression (mlVAR) to identify temporal patterns of the collaborative problem-solving (CPS) process in an immersive astronomy simulation. We extend previous research on joint attention with a particular focus on the added value from a multimodal, temporal account of the CPS process. We incorporate verbal discussion to contextualize joint attention, examine the sequential and contemporaneous associations between them, and identify significant differences in temporal patterns between low- and high-achieving groups. Our paper does the following: 1) creates interpretable multimodal group interaction patterns, 2) advances understanding of CPS through examination of verbal and non-verbal interactions, and 3) demonstrates the added value of a complete account of temporality including both duration and sequential order.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1411481
Database: ERIC
FullText Text:
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  Data: Enriching Multimodal Data: A Temporal Approach to Contextualize Joint Attention in Collaborative Problem-Solving
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  Data: <searchLink fieldCode="AR" term="%22Yiqiu+Zhou%22">Yiqiu Zhou</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-9004-9307">0009-0000-9004-9307</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jina+Kang%22">Jina Kang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7043-5427">0000-0002-7043-5427</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2023 10(3):87-101.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: 15
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  Data: 1929-7750
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  Data: Collaboration is a complex, multidimensional process; however, details of how multimodal features intersect and mediate group interactions have not been fully unpacked. Characterizing and analyzing the temporal patterns based on multimodal features is a challenging yet important work to advance our understanding of computer-supported collaborative learning (CSCL). This paper highlights the affordances, as well as the limitations, of different temporal approaches in terms of analyzing multimodal data. To tackle the remaining challenges, we present an empirical example of multimodal temporal analysis that leverages multi-level vector autoregression (mlVAR) to identify temporal patterns of the collaborative problem-solving (CPS) process in an immersive astronomy simulation. We extend previous research on joint attention with a particular focus on the added value from a multimodal, temporal account of the CPS process. We incorporate verbal discussion to contextualize joint attention, examine the sequential and contemporaneous associations between them, and identify significant differences in temporal patterns between low- and high-achieving groups. Our paper does the following: 1) creates interpretable multimodal group interaction patterns, 2) advances understanding of CPS through examination of verbal and non-verbal interactions, and 3) demonstrates the added value of a complete account of temporality including both duration and sequential order.
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  Data: 2024
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  Data: EJ1411481
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 87
    Subjects:
      – SubjectFull: Attention Control
        Type: general
      – SubjectFull: Cooperative Learning
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Computer Assisted Instruction
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      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Problem Solving
        Type: general
      – SubjectFull: Astronomy
        Type: general
      – SubjectFull: Science Instruction
        Type: general
      – SubjectFull: Computer Simulation
        Type: general
      – SubjectFull: Comparative Analysis
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      – SubjectFull: High Achievement
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      – SubjectFull: Low Achievement
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      – SubjectFull: Interaction Process Analysis
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      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Learning Management Systems
        Type: general
    Titles:
      – TitleFull: Enriching Multimodal Data: A Temporal Approach to Contextualize Joint Attention in Collaborative Problem-Solving
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            NameFull: Yiqiu Zhou
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            NameFull: Jina Kang
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