How Various Educational Features Influence Programming Performance in Primary School Education

In the digital age, programming education has become increasingly important, even in primary schools. However, introducing programming at such an early stage presents unique challenges, given the need for students to grasp mathematical concepts, abstract thinking, and the intricacies of programming syntax. Educational Data Mining (EDM) offers a potential contribution by predicting learning performance, facilitating the optimization of the learning processes, and providing real-time guidance. A notable gap in the current literature about EDM in programming education is its predominant emphasis on the university level. Our research objectives were to identify features influencing primary school students' programming capabilities. A more comprehensive dataset was introduced, incorporating psychometric data and highlighting features such as learning motivation and attitude, computational thinking data, and other potentially influential variables, which set our study apart from previous studies. We found that the strongest predictor was academic performance in Information Technology, followed by psychometric data on students' learning attitudes and motivation. Computational thinking also emerged as a significant feature in predicting programming performance. It's worth highlighting that involvement in extra-curricular activities, like Olympic Mathematics training, showed a significant association, underscoring the importance of mathematical logic and reasoning in programming. This is further bolstered by the evident correlation with academic performance in Mathematics, confirming its pivotal role in shaping programming abilities. Interestingly, the correlation of academic performance in Chinese is also significant, indicating that the language medium of instruction can notably influence success.