Exploring the Effectiveness and Moderators of Block-Based Visual Programming on Student Learning: A Meta-Analysis
Yue Hu, Cheng-Huan Chen, Chien‐Yuan Su
Abstract
Block-based visual programming tools, such as Scratch, Alice, and MIT App Inventor, provide an intuitive and easy-to-use editing interface through which to promote programming learning for novice students of various ages. However, very little attention has been paid to investigating these tools’ overall effects on students’ academic achievement and the study features that may moderate the effects of block-based visual programming from a comprehensive perspective. Thus, the present study carried out a meta-analysis to systemically examine 29 empirical studies (extracting 34 effect sizes) using experimental or quasi-experiments involving the programming learning effects of employing block-based visual programming tools to date (until the end of 2019). The results showed a small to medium significant positive overall mean effect size (fixed-effect model g = 0.37; random-effects model g = 0.47) of the use of these block-based visual programming tools with respect to students’ academic achievement. Furthermore, the overall mean effect size was significantly affected by the educational stage, programming tool used, experimental treatment, and school location. Discussions and implications based on the findings are provided.