Using hidden Markov model to detect problem-solving strategies in an interactive programming environment

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 compa...

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Bibliographic Details
Published in:Educational technology research and development Vol. 73; no. 4; pp. 2113 - 2130
Main Authors: Wu, Linjing, Xiang, Xuelin, Yang, Xueyan, Jin, Xuan, Chen, Liang, Liu, Qingtang
Format: Journal Article
Language:English
Published: New York Springer Nature B.V 01.08.2025
Subjects:
Algorithms
Behavior
C plus plus
Cognitive Processes
Data Analysis
Data collection
Educational Games
Elementary Education
Elementary School Students
Elementary Schools
Environment
Grammar
Instructional Materials
Learning Activities
Learning analytics
Learning Processes
Literature Reviews
Low Achievement
Markov Processes
Probability
Problem solving
Program Evaluation
Programming languages
Research design
Researchers
Spelling
Student Behavior
Students
Syntax
Teaching
Israel
ISSN:1042-1629, 1556-6501
Online Access:Get full text
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Summary: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.
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ISSN:1042-1629
1556-6501
DOI:10.1007/s11423-025-10506-w