From Absent Diagnosis to Deep Intervention: How Computational Thinking Shapes Mathematics Teachers’ Pedagogical Guidance

Abstract

Although Programs that Simulate Mathematical Processes (PSMP) offer a computational approach to teaching mathematical concepts and integrating computational thinking (CT) into mathematics instruction, this study focuses on the development of mathematics teachers’ pedagogical guidance when addressing student errors and misconceptions in PSMP-based tasks. It also examines the factors that influence this development. Twenty-eight mathematics teachers selected through criterion sampling completed written assessments involving PSMP-based tasks. Twelve participants were subsequently selected for in-depth interviews to capture a range of intervention strategies. Directed content analysis revealed a continuum progressing from an absence of diagnosis, characterised by incorrect, vague, or incomplete guidance, to diagnosis-based interventions ranging from misaligned to shallow or indirect guidance, and ultimately to deep diagnosis. This progression reflects differences in teachers’ capacity to identify and address student errors and misconceptions. The findings also indicate a relationship between teachers’ application of CT processes and the quality of their pedagogical guidance. Teachers who applied CT processes appropriately, including data processing and updating, algorithm visualisation, logical flow control, and testing and debugging, provided general or deep diagnosis-based interventions. In contrast, teachers who applied these processes inappropriately offered misguided, questionable, or fragmented support. These findings highlight the importance of CT competence in shaping the quality of mathematics teachers’ responses to student errors and misconceptions in PSMP-based tasks.