Applying Binary Logistic Regression to Map Cognitive Styles Based on Students’ Errors in Algebraic Thinking Generalization Problems According to Newman’s Theory
DOI:
https://doi.org/10.26740/jrpipm.v10n1.p48-58Keywords:
Binary logistic regression, Algebraic thinking generalization problems, Cognitive styles, Students' errorsAbstract
Algebraic generalization problems pose a significant challenge, requiring teachers to recognize influencing factors like Field Independent (FI) and Field Dependent (FD) cognitive styles. The purpose of this study was to apply binary logistic regression to map students' cognitive styles, either FI or FD, based on their error patterns when solving algebraic thinking generalization problems. These errors were classified using the five categories of Newman’s Theory: Reading (R), Comprehension (C), Transformation (T), Process Skill (S), and Encoding (E). This exploratory correlational study involved 40 tenth-grade students from SMA IT Granada Samarinda. Cognitive style (the dependent variable) was measured using the GEFT, while the Newman error categories (the independent variables) were identified from a generalization instrument adopted from TIMSS (2003–2019). The results found that 23 out of 40 students made mistakes, consisting of 9 FI students and 31 FD students. The binary logistic regression results showed that the Process Skill (S) error was the strongest predictor for the FI style, with an odds ratio of 18.025. This means that students who make an S error are 18 times more likely to be classified as FI. This finding leads to the conclusion that FI students struggle with the details of procedural implementation, despite possessing a strong strategic understanding. Binary logistic regression proved effective as a diagnostic tool to support more personalized mathematics learning strategies.
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