A Block-Based Interactive Programming Environment for Large-Scale Machine Learning Education

• Published in: Applied sciences Vol. 12; no. 24; p. 13008
• Main Authors: Park, Youngki, Shin, Youhyun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Education; Environmental education; Interfaces; K-12 education; large-scale training; Learning algorithms; Machine learning; natural language processing; Programming environments; Programming languages; Scratch; Secondary school students; Secondary schools; Students; Teachers; Tooee
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122413008

The existing block-based machine learning educational environments have a drawback in that they do not support model training based on large-scale data. This makes it difficult for young students to learn the importance of large amounts of data when creating machine learning models. In this paper, we present a novel programming environment in which students can easily train machine learning models based on large-scale data using a block-based programming language. We redefine the interfaces of existing machine learning blocks and also develop an effective model training algorithm suitable for block-based programming languages to enable “instant training” and “large-scale training”. As example educational applications based on this environment, we presented what is termed a “Question-Answering Chatbot” program trained on 11,822 text data instances with 7784 classes as well as a “Celebrity Look-Alike” program trained on 4431 image data instances with 7 classes. The experimental results show that teachers and pre-service teachers give high scores on all four evaluation measures for this environment.