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Score Prediction from Programming Exercise System Logs Using Machine Learning

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Title: Score Prediction from Programming Exercise System Logs Using Machine Learning
Language: English
Authors: Tanaka, Tetsuo, Ueda, Mari
Source: International Association for Development of the Information Society. 2023.
Availability: International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org
Peer Reviewed: Y
Page Count: 7
Publication Date: 2023
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Scores, Prediction, Programming, Artificial Intelligence, Web Based Instruction, Web Sites, Educational Environment, Progress Monitoring, Feedback (Response), Computer Science, College Students, Foreign Countries
Geographic Terms: Japan
Abstract: In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records their results. For educators, the system offers insights into each student's progress, the evolution of their source code, and the instances of errors. While teachers find these functions beneficial, the method of providing feedback to students needs improvement. Immediate feedback is proven to be more effective for student learning. If the final course score could be predicted based on early data (e.g., from the 1st or 2nd week), students could adapt their study strategies accordingly. This paper demonstrates that one can predict the final score using the system's operational logs from the initial phases of the course. Furthermore, the score predictions can be revised weekly based on new class logs. We also explore the potential of offering tailored advice to students to enhance their final score. [For the full proceedings, see ED636095.]
Abstractor: As Provided
Entry Date: 2023
Accession Number: ED636328
Database: ERIC
FullText Text:
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  Data: Score Prediction from Programming Exercise System Logs Using Machine Learning
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  Data: <searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Web+Based+Instruction%22">Web Based Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Web+Sites%22">Web Sites</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Environment%22">Educational Environment</searchLink><br /><searchLink fieldCode="DE" term="%22Progress+Monitoring%22">Progress Monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science%22">Computer Science</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records their results. For educators, the system offers insights into each student's progress, the evolution of their source code, and the instances of errors. While teachers find these functions beneficial, the method of providing feedback to students needs improvement. Immediate feedback is proven to be more effective for student learning. If the final course score could be predicted based on early data (e.g., from the 1st or 2nd week), students could adapt their study strategies accordingly. This paper demonstrates that one can predict the final score using the system's operational logs from the initial phases of the course. Furthermore, the score predictions can be revised weekly based on new class logs. We also explore the potential of offering tailored advice to students to enhance their final score. [For the full proceedings, see ED636095.]
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
    Subjects:
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Web Based Instruction
        Type: general
      – SubjectFull: Web Sites
        Type: general
      – SubjectFull: Educational Environment
        Type: general
      – SubjectFull: Progress Monitoring
        Type: general
      – SubjectFull: Feedback (Response)
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      – SubjectFull: Computer Science
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Japan
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    Titles:
      – TitleFull: Score Prediction from Programming Exercise System Logs Using Machine Learning
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            NameFull: Tanaka, Tetsuo
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            NameFull: Ueda, Mari
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              Type: published
              Y: 2023
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            – TitleFull: International Association for Development of the Information Society
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