Learning Tools Using Block-based Programming for AI Education
Chris-Bennet Fleger, Yousuf Amanuel, Johannes Krugel
Abstract
This work identifies the capabilities of a block-based programming approach for learning machine learning concepts. It focuses on the following overarching research question: “How can block-based programming tools be used to facilitate the understanding and application of machine learning concepts in K-12 education?”. To answer this question, guidelines for conducting a systematic literature review are followed, resulting in the study of 17 different learning tools. These tools are examined for their technical nature, content coverage, design features, intelligibility, evaluations, and deployability. The findings suggest that the vast majority of tools focus on a high-level representation of classification models that children can create in an extended version of the Scratch programming environment. By this, however, only one facet of machine learning is addressed, and deeper insights into the underlying functions are not provided. In addition, technical, linguistic, and conceptual barriers to the design of tools and the wider curricula become apparent.