Exploring Primary School Students’ Computational Thinking Process in an AI-EnhancedSTEAM Course: A Lag Sequential Analysis Approach

Abstract

STEAM education requires learners to be able to utilize interdisciplinary knowledge to draw wisdom from different disciplines to solve complex real-world problems, for which computational thinking capacity is necessary for decomposing problems, abstracting problems, and designing algorithms. The personalized support capabilities of artificial intelligence can facilitate effective STEAM courses. Analyzing students' computational thinking behaviors in AI-enhanced STEAM classrooms can help teachers understand patterns of learning behavior. In this study, the computational thinking behavioral patterns of teachers and students in an AI-enhanced school-based STEAM course developed for a voluntary teaching program at an under-represented primary school were explored. This course lasted for six weeks, and a total of 798 behavioral samples were collected from students and teachers. Three behavioral sequences—decompose problems, design algorithms, and summarize—were derived from lagged sequence analysis.