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Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems

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Title: Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems
Language: English
Authors: Steven Dang, Michael Yudelson, Kenneth R. Koedinger
Source: Grantee Submission. 2017.
Peer Reviewed: Y
Page Count: 9
Publication Date: 2017
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305B150008
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Student Behavior, Intelligent Tutoring Systems, Educational Technology, Academic Achievement, Self Control, Executive Function, Middle School Students, Algebra, Mathematics Education, Suburban Schools, Student Motivation, Metacognition
Assessment and Survey Identifiers: Motivated Strategies for Learning Questionnaire
DOI: 10.1145/3051457.3051470
Abstract: The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed. [This paper was published in: "Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S 2017)," ACM, 2017.]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED664038
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2017.
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  Data: 10.1145/3051457.3051470
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  Data: The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed. [This paper was published in: "Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S 2017)," ACM, 2017.]
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        Value: 10.1145/3051457.3051470
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
    Subjects:
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Educational Technology
        Type: general
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      – SubjectFull: Self Control
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      – SubjectFull: Middle School Students
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      – SubjectFull: Algebra
        Type: general
      – SubjectFull: Mathematics Education
        Type: general
      – SubjectFull: Suburban Schools
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      – SubjectFull: Student Motivation
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      – SubjectFull: Metacognition
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      – SubjectFull: Motivated Strategies for Learning Questionnaire
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      – TitleFull: Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems
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            NameFull: Kenneth R. Koedinger
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              Y: 2017
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