Analyzing Students’ Mathematical Communication Failure Through ChatGPT-Assisted Scaffolding Based on Cognitive Styles
Keywords:
Cognitive style, Mathematical communication, Scaffolding, ChatGPT, Sequences and seriesAbstract
This study aims to analyze the forms of students’ mathematical communication failure in sequences and series and to examine how ChatGPT functions as adaptive scaffolding for students with Field Dependent (FD) and Field Independent (FI) cognitive styles. This study employed a qualitative case study design involving four eleventh-grade students selected purposively based on the Group Embedded Figures Test (GEFT). Data were collected through mathematical communication tests, think-aloud protocols, and semi-structured interviews. The analysis was based on mathematical communication indicators proposed by the National Council of Teachers of Mathematics (NCTM) and Greenes and Schulman. During problem-solving activities, ChatGPT was used as an adaptive scaffolding tool by providing guiding questions, conceptual clarification, and reflective prompts rather than direct answers. The findings revealed that FD students experienced difficulties in symbolic formalization and connecting representations, whereas FI students tended to provide incomplete explanations and verification processes. ChatGPT-assisted scaffolding supported each cognitive style differently by helping FD students formalize mathematical ideas and encouraging FI students to explain and verify their reasoning more explicitly. These findings contribute to mathematics learning innovation by demonstrating how AI-based adaptive scaffolding can provide personalized support according to students’ cognitive characteristics and improve mathematical communication during problem solving.
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