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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.
Authors: Tetsuo Tanaka1, Mari Ueda2
Source: Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age. 2023, p11-17. 7p.
Subject Terms: *Classrooms, *Educators, *Students, *Learning, Prolog (Computer program language)
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. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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DbLabel: Education Research Complete
An: 174156425
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PubType: Conference
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  Data: SCORE PREDICTION FROM PROGRAMMING EXERCISE SYSTEM LOGS USING MACHINE LEARNING.
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  Data: *<searchLink fieldCode="DE" term="%22Classrooms%22">Classrooms</searchLink><br />*<searchLink fieldCode="DE" term="%22Educators%22">Educators</searchLink><br />*<searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Prolog+%28Computer+program+language%29%22">Prolog (Computer program language)</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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age is the property of International Association for Development of the Information Society (IADIS) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 7
        StartPage: 11
    Subjects:
      – SubjectFull: Classrooms
        Type: general
      – SubjectFull: Educators
        Type: general
      – SubjectFull: Students
        Type: general
      – SubjectFull: Learning
        Type: general
      – SubjectFull: Prolog (Computer program language)
        Type: general
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      – TitleFull: SCORE PREDICTION FROM PROGRAMMING EXERCISE SYSTEM LOGS USING MACHINE LEARNING.
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          Name:
            NameFull: Tetsuo Tanaka
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            NameFull: Mari Ueda
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            – D: 01
              M: 01
              Text: 2023
              Type: published
              Y: 2023
          Titles:
            – TitleFull: Proceedings of the IADIS International Conference on Cognition & Exploratory Learning in Digital Age
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