Exploring Student Learning Interests with SupportVector Machine in Pedagogical Strategies

Abstract

In the realm of education and instructional design, understanding the impact of pedagogical strategies on students' learning interests is of paramount importance. This study employs a machine learning approach, specifically Support Vector Machines with a linear kernel, to comprehensively explore this relationship. The research investigates the influence of various pedagogical strategies on students' learning interests using classification techniques. Our findings reveal robust model capabilities, with an Area Under the Curve of 93.3%, Classification Accuracy of 95%, precision of 95.3%, and recall of 95%. Feature importance analysis identifies key contributors, with the 'Inclusive Learning Environment' and 'Active Class Discussions' aspects showing significant influence. Our research underscores the critical role of pedagogical strategies, particularly in shaping the learning environment and promoting active class discussions, as they significantly impact students' learning interests. This study enriches our understanding of the model's capabilities and highlights the need to consider real-world contexts and validate its performance on external datasets for successful application and generalization. As educators and institutions aim to create engaging and effective learning environments, the insights derived from this research offer actionable recommendations for improving pedagogical strategies. This research serves as a valuable resource for those seeking to enhance the learning experiences of students, providing a foundation for further exploration of pedagogical dynamics and their influence on student learning interests.