Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Using hidden Markov model to detect problem-solving strategies in an interactive programming environment. |
| Συγγραφείς: |
Wu, Linjing, Xiang, Xuelin, Yang, Xueyan, Jin, Xuan, Chen, Liang, Liu, Qingtang |
| Πηγή: |
Educational Technology Research & Development; Aug2025, Vol. 73 Issue 4, p2113-2130, 18p |
| Θεματικοί όροι: |
Hidden Markov models, Problem solving, Academic achievement, Learning strategies, Teaching methods, Computer programming education, Behavioral assessment |
| Περίληψη: |
Problem-solving strategies are crucial in learning programming. Owing to their hidden nature, traditional methods such as interviews and questionnaires cannot reflect the details and differences of problem-solving strategies in programming. This study uses the Hidden Markov Model to detect and compare the problem-solving strategies of different groups in an interactive programming environment. The results suggest that high- and low-performance students have significant differences in their problem-solving strategies in programming. High-performance students had more "blank behaviors" in programming than low-performance students in video recordings. Low-performance students spent more time "searching teaching materials" than high-performance students. In the transfer task, high-performance students began the task by "identifying the problem," while low-performance students were involved in the "implementing of strategies." Additionally, high- and low-performance students improved from basic to transfer tasks. These findings shed light on why students performed differently in programming and how and when teachers needed to provide instructions to students in programming education. [ABSTRACT FROM AUTHOR] |
|
Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |