Capturing Teachers and Students' Computational Thinking Behavioral Patterns in Elementary School STEAM Courses: A Lag Sequential Analysis Approach

Computational thinking is critical to students' ability to solve problems. Analyzing students' computational thinking behaviors in STEAM classrooms can aid teachers in understanding learning behavior patterns, diagnosing potential challenges in developing computational thinking, and adjusting instructional strategies to enhance learning outcomes. This study utilized lag sequential analysis to quantify the learning behavior patterns of both teachers and students in STEAM classrooms. The findings revealed 240 behavior samples from students, with a high frequency of behaviors associated with decomposition, abstraction, algorithm design, and summary. The study identified three important behavioral sequence paths that illustrate the processes of problem decomposition, algorithm design, and summarization by students. The findings have reference value for studying the sequences of computational thinking behaviors in interdisciplinary contexts.