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

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, w...

Full description

Saved in:
Bibliographic Details
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
K-12 education
large-scale training
Machine learning
natural language processing
Programming languages
Scratch
Secondary school students
Secondary schools
Tooee
ISSN:2076-3417, 2076-3417
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app122413008