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Do Gender or Major Influence the Performance in Programming Learning? Teaching Mode Decision Based on Exercise Series

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This study introduces a new method using generalized estimating equations (GEE) to analyze student programming performance data over time. Findings reveal that factors like gender, major, and practice habits significantly impact learning outcomes.

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Area of Science:

  • Educational Technology
  • Computer Science Education
  • Data Science in Education

Background:

  • Traditional and online education aim for individualized learning, but data collection for instructional design improvements is challenging.
  • Existing research often overlooks daily practice data, focusing primarily on exam scores.
  • Generalized Estimating Equations (GEE), effective for time-series data, have not been applied to programming education analysis.

Purpose of the Study:

  • To propose an experimental paradigm for programming performance analysis using daily practice and exam data.
  • To collect a comprehensive time-series dataset encompassing student attributes, behaviors, and performance.
  • To apply GEE for analyzing the influence of individual attributes and learning behaviors on programming performance.

Main Methods:

  • Developed an experimental paradigm for programming performance analysis.
  • Collected a semester-long time-series dataset of student attributes, learning behavior, and performance.
  • Applied Generalized Estimating Equations (GEE) for ordinal multinomial responses to analyze the dataset.

Main Results:

  • Programming learning is influenced by student gender and major.
  • Longer answer times and less time spent correlate with better student performance.
  • Students tend to exhibit exam-cramming behavior, leading to slightly poorer daily exercise performance, irrespective of gender.

Conclusions:

  • University programming education should be tailored by major.
  • Teachers should implement gender-specific teaching strategies at different times.
  • GEE provides a novel approach for analyzing time-series educational data in programming.