Examining the Efficacy of Adaptive Learning Algorithms in Enhancing Student Performance in STEM Subjects: A Case Study from a UK Secondary School

Abstract

This study investigates the impact of adaptive learning algorithms on student performance in STEM (Science, Technology, Engineering, Mathematics) subjects, focusing on a secondary school in the UK. Employing a mixed-methods approach, we conducted quantitative analyses through standardized test scores and qualitative insights via student interviews. Our findings reveal a statistically significant improvement in student engagement and performance, with an average score increase of 15% post-intervention. Moreover, qualitative data indicate enhanced self-efficacy among students utilizing adaptive learning tools. This research contributes to the discourse on personalized education, highlighting the potential of technology in addressing diverse learning needs in contemporary educational settings.