Resumen
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.