The Application of Machine Learning Algorithms in Analyzing Students' Conceptual Error Patterns in Science Learning

Science education often faces challenges related to conceptual errors made by students, which can hinder their understanding of the material being taught. This study aims to identify and analyze common conceptual errors through the application of machine learning algorithms. The method employed in this research is a literature review approach, where various relevant studies are analyzed to understand the application of machine learning in the context of science education. The findings of the study indicate that several machine learning algorithms, such as large language models and interactive visualization techniques, can be utilized to detect misconceptions in students with higher accuracy and provide more personalized feedback. Although there are some challenges related to the accuracy and reliability of the systems used, the application of these techniques shows significant potential in enhancing the effectiveness of science education. Overall, the application of machine learning in science education can make a substantial contribution to helping teachers analyze and correct the conceptual errors made by students, ultimately improving the quality of the learning process. The implications of this research suggest the need for further development to create technology-based education systems that can better respond to students' learning needs.