The Utilisation of Machine Learning Algorithm Support Vector Machine (SVM) for Improving the Adaptive Assessment

In today's digital age, the convergence of science education and machine learning technologies has the potential to revolutionize the teaching and learning processes. This research, conducted by a dedicated science teacher, explores the dynamic intersection of machine learning and science education, with a specific focus on data-driven approaches to enrich the educational experience for students. The study centers on a group of 36 high school students from the Nazarbayev Intellectual School in Shymkent, Kazakhstan, aiming to assess the effectiveness of a novel approach to science instruction. It leverages the principles of multiple intelligence theory to tailor science curriculum and teaching methods to the unique cognitive profiles of individual students. The research outcomes demonstrate significant enhancements in students' computational abilities as a direct consequence of integrating machine learning into science education. By combining the power of data analysis and educational adaptation, this research contributes to the ever-evolving landscape of education and technology. This study is poised to serve as a cornerstone for educators, researchers, and policymakers interested in the evolving nexus of technology and education. It not only underscores the remarkable potential of machine learning in the realm of science education but also suggests a broader transformation of pedagogical methodologies, empowering students to excel in science while nurturing their diverse cognitive strengths.