LEARNING CONCEPT NETWORKS IN THE PHOTOSYNTHESIS BASED ON STUDENTS’ COGNITIVE LEVELS

An important educational goal is enabling students to learn scientific concepts. The scientific concepts learned in class are developed within students' cognitive structures. Despite the successful application of Semantic Network Analysis (SNA) to study these cognitive structures, there has been limited examination of students' concept networks based on their individual characteristics. Therefore, this study aims to evaluate the differences in students' characteristics based on their cognitive levels, which influence their thinking and behavior. To analyze these differences, this study compares concept and connected concept networks, focusing on photosynthesis, a challenging life sciences topic. The study's results indicate that students could not clearly distinguish concepts by subtopic, but there were changes in the concept network after class. Although the types and number of concepts students knew were similar depending on their cognitive level, the concept network structure differed. Additionally, some students could not distinguish between similar concepts. Thus, teachers are advised to differentiate between similar concepts during instruction and address personal variables such as students' cognitive levels.