Reducing Student Misconceptions Through Problem-Based Learning with a Computational Chemistry-Assisted Question Map Approach - Hulyadi; Muhali; Gargazi (Postgraduate, University of Mataram, 2023)
The aim of this research is to reduce student misconceptions using problem-based learning with computational chemistry assistance and a question map approach in the study of organic compound molecular structures and events resulting from the exposure of organic molecules to instruments with high-energy electron bombardment. This study employs a qualitative descriptive research design, focusing on in-depth descriptions and understanding of phenomena or events. The research focuses on a deeper understanding of students' capabilities in analyzing and interpreting the structure of organic chemical molecules. The research specifically examines students' ability to dissect questions outlined in the question map, interpret reasons, and describe problematic phenomena within the learning context. The issues addressed by students involve critical and in-depth examinations of theoretical concepts. In this study, data analysis is carried out inductively, wherein the researcher develops theories or findings based on collected data rather than preconceived hypotheses. The research findings demonstrate that problem-based learning with computational chemistry assistance using a question map approach is effective in reducing student misconceptions when analyzing the molecular structure of biodiesel.