Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy
The introduction of programming education in K-12 schools to promote computational thinking has attracted a great deal of attention from scholars and educators. Debugging code is a central skill for students, but is also a considerable challenge when learning to program. Learners at the K-12 level o...
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| Language: | English |
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International Forum of Educational Technology & Society
2025-04-01
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| Series: | Educational Technology & Society |
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| Online Access: | https://www.j-ets.net/collection/published-issues/28_2#h.njwqi1ffqtu2 |
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| author | Shu-Jie Chen, Xiaofen Shan, Ze-Min Liu, Chuang-Qi Chen |
| author_facet | Shu-Jie Chen, Xiaofen Shan, Ze-Min Liu, Chuang-Qi Chen |
| author_sort | Shu-Jie Chen, Xiaofen Shan, Ze-Min Liu, Chuang-Qi Chen |
| collection | DOAJ |
| description | The introduction of programming education in K-12 schools to promote computational thinking has attracted a great deal of attention from scholars and educators. Debugging code is a central skill for students, but is also a considerable challenge when learning to program. Learners at the K-12 level often lack confidence in programming debugging due to a lack of effective learning feedback and programming fundamentals (e.g., correct syntax usage). With the development of technology, large language models (LLMs) provide new opportunities for novice programming debugging training. We proposed a method for incorporating an LLM into programming debugging training, and to test its validity, 80 K-12 students were selected to participate in a quasi-experiment with two groups to test its effectiveness. The results showed that through dialogic interaction with the model, students were able to solve programming problems more effectively and improve their ability to solve problems in real-world applications. Importantly, this dialogic interaction increased students’ confidence in their programming abilities, thus allowing them to maintain motivation for programming learning. |
| format | Article |
| id | doaj-art-86b3a976b35d4c6daab8ff18351cd51b |
| institution | OA Journals |
| issn | 1176-3647 1436-4522 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | International Forum of Educational Technology & Society |
| record_format | Article |
| series | Educational Technology & Society |
| spelling | doaj-art-86b3a976b35d4c6daab8ff18351cd51b2025-08-20T02:27:46ZengInternational Forum of Educational Technology & SocietyEducational Technology & Society1176-36471436-45222025-04-01282259278https://doi.org/10.30191/ETS.202504_28(2).TP01Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacyShu-Jie Chen, Xiaofen Shan, Ze-Min Liu, Chuang-Qi ChenThe introduction of programming education in K-12 schools to promote computational thinking has attracted a great deal of attention from scholars and educators. Debugging code is a central skill for students, but is also a considerable challenge when learning to program. Learners at the K-12 level often lack confidence in programming debugging due to a lack of effective learning feedback and programming fundamentals (e.g., correct syntax usage). With the development of technology, large language models (LLMs) provide new opportunities for novice programming debugging training. We proposed a method for incorporating an LLM into programming debugging training, and to test its validity, 80 K-12 students were selected to participate in a quasi-experiment with two groups to test its effectiveness. The results showed that through dialogic interaction with the model, students were able to solve programming problems more effectively and improve their ability to solve problems in real-world applications. Importantly, this dialogic interaction increased students’ confidence in their programming abilities, thus allowing them to maintain motivation for programming learning.https://www.j-ets.net/collection/published-issues/28_2#h.njwqi1ffqtu2large language modelsgenerative artificial intelligencedebugging skillscomputational thinkingself-efficacyprogramming education |
| spellingShingle | Shu-Jie Chen, Xiaofen Shan, Ze-Min Liu, Chuang-Qi Chen Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy Educational Technology & Society large language models generative artificial intelligence debugging skills computational thinking self-efficacy programming education |
| title | Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy |
| title_full | Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy |
| title_fullStr | Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy |
| title_full_unstemmed | Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy |
| title_short | Employing large language models to enhance K-12 students’ programming debugging skills, computational thinking, and self-efficacy |
| title_sort | employing large language models to enhance k 12 students programming debugging skills computational thinking and self efficacy |
| topic | large language models generative artificial intelligence debugging skills computational thinking self-efficacy programming education |
| url | https://www.j-ets.net/collection/published-issues/28_2#h.njwqi1ffqtu2 |
| work_keys_str_mv | AT shujiechenxiaofenshanzeminliuchuangqichen employinglargelanguagemodelstoenhancek12studentsprogrammingdebuggingskillscomputationalthinkingandselfefficacy |