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

Both traditional teaching and online teaching advocate individualized education. One of the difficulties on exploring possible improvements of instructional design is the challenging process of data collection. Existing research mainly focuses on the exam score of students but pays little attention to students' daily practice. As an effective method to handle time-series dataset, the generalized estimating equations (GEE) have not been used in this research field. Considering above issues, we first propose an experimental paradigm of programming performance analysis based on the performance record of students' daily practice-exam and finish collecting a complete time-series dataset in one semester, including students' individual attributes, learning behavior, and learning performance. Then, we propose an approach that analyzes practice-exam time-series dataset based on GEE to study the influence of individual attributes and learning behavior on learning performance. It is the first time to apply the GEE method for ordinal multinomial responses in this research field, by which we conclude several results that gender or major does have a certain difference on the programming learning. The longer the answer time and the less the cost time, the better the students' performance. Regardless of gender, students tend to cram for the exam and perform a little worse in the daily exercise. Finally, targeting at two important individual attributes, we give corresponding teaching mode decisions that university should teach students programming by major and teacher should give different teaching methods to students of different genders at different time points.

Automated summary

Both traditional teaching and online teaching emphasize individualized education. However, the process of exploring improvements in instructional design is hindered by the challenging task of collecting data. Existing research primarily focuses on students' exam scores and overlooks their daily practice. In this study, we propose an experimental paradigm of programming performance analysis based on students' daily practice-exam records and collect a comprehensive time-series dataset, including students' individual attributes, learning behavior, and performance. We then analyze the time-series dataset using generalized estimating equations (GEE) to examine the impact of individual attributes and learning behavior on performance. This is the first application of GEE for ordinal multinomial responses in this research field, from which we conclude that gender and major do contribute to differences in programming learning. Longer answer times and shorter cost times are associated with better performance. Regardless of gender, students tend to cram for exams and perform slightly worse in daily exercises. Finally, we provide teaching mode decisions for universities based on two important individual attributes and recommend different teaching methods for students of different genders at different time points.