Abstract
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.
Highlights
Both traditional teaching and online teaching advocate individualized education
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. en, we propose an approach that analyzes practice-exam time-series dataset based on generalized estimating equations (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. e longer the answer time and the less the cost time, the better the students’ performance
Summary
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. 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. En, 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. Researchers suppose that personal characteristics predispose academic performance Differences in these characteristics cause individuals to react to learning in their own ways [3]. Many researchers underpinned the importance of individual differences in personal characteristics for learning outcome [4,5,6]. Focusing on quality assurance of language courses, Luo and Ye examined what has influenced learners’ perceptions and identifies the specific quality criteria of five types of them with the data collected from English as a second language learners on China’s biggest MOOC platform “iCourse” through qualitative study [14]
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