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Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History

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Title: Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History
Language: English
Authors: Lokkila, Erno, Christopoulos, Athanasios, Laakso, Mikko-Jussi
Source: Informatics in Education. 2023 22(2):277-294.
Availability: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Peer Reviewed: Y
Page Count: 18
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Prior Learning, Programming, Computer Science Education, Markov Processes, Likert Scales, Introductory Courses, Novices, Algorithms, Student Behavior, Teaching Methods, Learning Processes, Programming Languages, Undergraduate Students, Artificial Intelligence, Foreign Countries
Geographic Terms: Finland
ISSN: 1648-5831
2335-8971
Abstract: Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1392996
Database: ERIC
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  Data: Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History
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  Data: <searchLink fieldCode="SO" term="%22Informatics+in+Education%22"><i>Informatics in Education</i></searchLink>. 2023 22(2):277-294.
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  Data: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
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  Data: Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 277
    Subjects:
      – SubjectFull: Prior Learning
        Type: general
      – SubjectFull: Programming
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Markov Processes
        Type: general
      – SubjectFull: Likert Scales
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      – SubjectFull: Introductory Courses
        Type: general
      – SubjectFull: Novices
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Student Behavior
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      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Learning Processes
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      – SubjectFull: Programming Languages
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      – SubjectFull: Undergraduate Students
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Finland
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    Titles:
      – TitleFull: Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History
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            NameFull: Lokkila, Erno
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            NameFull: Laakso, Mikko-Jussi
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            – TitleFull: Informatics in Education
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