Exploring Student Learning Interests with Support Vector Machine in Pedagogical Strategies

Abstract

In the realm of education and instructional design,
understanding the impact of pedagogical strategies on students'
learning interest 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 interest
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 interest. This study enriches our understanding of the
model's capabilities and highlights the need to consider realworld 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 interest.