TITLE:
An Empirical Study on the Cultivation of College Students’ Computational Thinking in the Context of Deep Learning
AUTHORS:
Rong Chen, Yihui Lai
KEYWORDS:
Deep Learning, Computational Thinking, Empirical Study, Cultivation
JOURNAL NAME:
Creative Education,
Vol.15 No.10,
October
29,
2024
ABSTRACT: In the era of rapid development of information technology, deep learning, as the core driving force in the field of artificial intelligence, is leading profound changes in the education industry. Computational thinking, as a key ability to solve complex problems and design innovative systems, has become one of the important indicators to measure the comprehensive quality of college students. This paper focuses on the cultivation of college students’ computational thinking in the context of deep learning, and explores through empirical research how to effectively improve college students’ computational thinking ability in the current technological environment, as well as its impact and promotion on the cultivation of college students’ computational thinking. By designing and implementing an empirical study targeting students majoring in Electronic Information Engineering at Nanchang Normal University, this study aims to explore the actual effects of deep learning-based teaching models in enhancing college students’ computational thinking abilities. Students majoring in Electronic Information Engineering at Nanchang Normal University were selected as the research subjects. A questionnaire survey was conducted, which constructed nine dimensions including decomposition ability, abstract ability, modeling ability, algorithmic thinking, creativity, cooperation ability, iterative optimization, transfer ability and evaluation. These dimensions were found to have significant Pearson correlations at the 0.01 level (two-tailed). Further exploration of gender differences in each dimension revealed that there were significant differences between males and females in terms of decomposition ability, abstract ability, modeling ability, creativity, iterative optimization, transfer ability, and evaluation, with males having significantly higher average scores than females. However, no significant gender differences were observed in algorithmic thinking and cooperation ability. The study points out that deep learning technology provides a new perspective for computational thinking education, contributing to the cultivation of students’ innovative thinking and autonomous learning abilities. This research provides practical references and theoretical foundations for the teaching reform of electronic majors in universities.