Development of a Computational Thinking-Based Learning Model to Improve Students’ Algebraic Thinking Skills

Abstract

This study aims to describe the development of a computational thinking-based learning model to improve students’ algebraic thinking skills. The study addresses students’ difficulties in understanding algebraic structures, recognizing patterns, using variables, and constructing symbolic generalizations. A qualitative case study approach was used to explore the learning process in depth. The research involved Grade VIII students who participated in algebra learning activities designed around decomposition, pattern recognition, abstraction, and algorithmic thinking. Data were collected through semi-structured interviews, classroom observations, documentation, and analysis of students’ written work. The findings revealed four main themes. First, decomposition helped students identify known information, unknown elements, and relationships within algebraic problems. Second, pattern recognition supported students in moving from repeated arithmetic procedures to generalization. Third, abstraction helped students understand variables as representations of quantities, patterns, and mathematical relationships. Fourth, algorithmic thinking enabled students to organize solution steps systematically and evaluate errors in their reasoning. The study shows that computational thinking can strengthen algebraic thinking by making students’ reasoning processes more visible, structured, and reflective. These findings contribute theoretically to the integration of computational thinking and algebraic thinking in mathematics education. Practically, the model offers teachers a structured approach for designing algebra instruction that emphasizes reasoning, representation, and problem-solving processes. Future studies are recommended to examine the model across different grade levels, school contexts, and algebraic topics.