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Using Data Mining Models to Predict Students' Academic Performance before the Online Course Start

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Bibliographic Details
Title: Using Data Mining Models to Predict Students' Academic Performance before the Online Course Start
Language: English
Authors: Xu, Tonghui
Source: Journal of Educators Online. Jan 2023 20(1).
Availability: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, Online Courses, Academic Achievement, Data Analysis, Information Retrieval, Pattern Recognition, Models, Prediction, Interaction, Success, Algorithms, Student Behavior, Time Factors (Learning)
ISSN: 1547-500X
Abstract: The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data to predict their academic performance. In this study, this interaction information is called "week 0" data. The results suggest that "week 0" interaction data can be used to identify the academic success of online students and predict first assignment performance.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1383946
Database: ERIC
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  Data: Using Data Mining Models to Predict Students' Academic Performance before the Online Course Start
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  Data: The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data to predict their academic performance. In this study, this interaction information is called "week 0" data. The results suggest that "week 0" interaction data can be used to identify the academic success of online students and predict first assignment performance.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
    Subjects:
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Data Analysis
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      – SubjectFull: Information Retrieval
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      – SubjectFull: Pattern Recognition
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      – SubjectFull: Models
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      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Interaction
        Type: general
      – SubjectFull: Success
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Student Behavior
        Type: general
      – SubjectFull: Time Factors (Learning)
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      – TitleFull: Using Data Mining Models to Predict Students' Academic Performance before the Online Course Start
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