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Investigating Markov Model Accuracy in Representing Student Programming Behaviours

  • 2024
  • Chapter
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Abstract

The chapter explores the use of Markov models to accurately represent and analyze student programming behaviors in introductory programming courses. By examining source code evolutions and clustering algorithms, it uncovers distinct problem-solving approaches and transitions among students. This detailed analysis not only enhances our understanding of student learning processes but also offers valuable insights for educators to tailor their teaching methods effectively.

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Literature
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Metadata
Title
Investigating Markov Model Accuracy in Representing Student Programming Behaviours
Authors
Herman Kandjimi
Hussein Suleman
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-64881-6_4

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