Artificial intelligence learning platform in a visual programming environment: exploring an artificial intelligence learning model

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students’ learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence...

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Vydáno v:Educational technology research and development Ročník 72; číslo 2; s. 997 - 1024
Hlavní autoři: Chang, Jui-Hung, Wang, Chi-Jane, Zhong, Hua-Xu, Weng, Hsiu-Chen, Zhou, Yu-Kai, Ong, Hoe-Yuan, Lai, Chin-Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2024
Springer
Springer Nature B.V
Témata:
Artificial intelligence
Cognitive style
Computer science
Development Article
Education
Educational Technology
Learning and Instruction
Learning strategies
Least Squares Statistics
Methods
Problem solving
Students
Computational thinking
Self-regulated learning
Learning platform
Cognitive style
Artificial intelligence
ISSN:1042-1629, 1556-6501
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Shrnutí:Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students’ learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study employed the use of a self-built AI platform (Ladder) for students to systematically learn and apply AI learning model established by the partial least squares (PLS) method to investigate the influence between variables (learning attitudes, self-regulated learning, AI anxiety, individual impact, computational thinking abilities, cognitive styles). This study was particularly conducted in the Department of Computer Science and Information Engineering of a top national university in Southern Taiwan. The valid data were collected from 65 students (55 male students; 10 female students). Furthermore, this study demonstrated the relationship between cognitive style, self-regulated learning and computational thinking. For the first time, it explored the impact of AI anxiety and completed existing research on it. The results of this study show that interest in learning positively affects learning attitudes. In addition, learning attitudes have a positive influence on each individual’s performance. Based on multiple theories and the artificial intelligence learning platform, the model proposed in this study effectively understood students’ learning status.
Bibliografie:ObjectType-Article-1
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ISSN:1042-1629
1556-6501
DOI:10.1007/s11423-023-10323-z