Learning Performance in Block-based Programming Education for Children: Predictors and Moderator

Authors

Xiaobin Zhang, Huazhong University of Science & TechnologyFollow
Ling Zhao, Huazhong University of Science & TechnologyFollow
Yuni Li, Huazhong University of Science and TechnologyFollow
Zhaohua Deng, Huazhong University of Science and TechnologyFollow

Paper Number

1257

Abstract

Learning programming by building blocks is a typical application of visual programming for children, which is considered as a creative approach to understand complex logic behind programming. Besides the visual elements that could enhance children’s learning motivation, what factors could influence learning performance is still a key issue. Using data from 14,718 learners of the Codemao platform, this study investigates how individual effort (degree of practice, frequency of interaction, complexity of works) and need-to-belong influence learner’s learning performance. The results show that individual effort positively predicts learning performance, while need-to-belong plays a “double-sword” effect. Specifically, need-to-belong positively moderates the relationship between the complexity of the works and learning performance, while it has a negative moderating effect on the relationship between the degree of practice, frequency of interaction and learning performance. Finally, we discuss the theoretical and practical implications of our findings.

Comments

Paper Number 1257

Recommended Citation

Zhang, Xiaobin; Zhao, Ling; Li, Yuni; and Deng, Zhaohua, "Learning Performance in Block-based Programming Education for Children: Predictors and Moderator" (2022). PACIS 2022 Proceedings. 143.
https://aisel.aisnet.org/pacis2022/143