Analysis of students’ relational critical thinking abilities using an in-depth learning approach
Abstract
Achieving the Golden Age Indonesia 2045 vision requires advancing human resources, particularly among educators. This is due to low PISA scores, a test of academic ability and critical thinking skills. This research is urgent because it aims to improve educator quality through a deep learning approach. This study aims to analyze students' Relational Critical Thinking (RCT) abilities in mathematics using a deep learning approach. This study employs a quantitative methodology using the Rasch model for data analysis. The research population includes Senior High Schools and Islamic Senior High Schools in Banten Province, with participants selected through random sampling. Data were collected using Google Forms with 15 essay questions on relational critical thinking. The results: 103 students (27.54%) fall into the developing RCT category, 152 students (40.64%) fall into the basic RCT category, and 119 students (31.82%) fall into the incompetent RCT category. These findings demonstrate that students' RCT abilities in mathematics are largely at the basic and incompetent levels. Deep learning can enhance students' RCT. This study recommends that educators consistently implement deep learning project-based learning approaches and use a valid critical thinking instrument.