Adaptive Learning Algorithms for Differentiated Instruction in Mathematics Education

Abstract

Adaptive learning systems are improving education for the better, especially in mathematics where students have a
diverse range of understanding. This paper discusses adaptive algorithms and their application towards students’
individual learning needs in mathematics. The study proposes a system that integrates decision trees with
reinforcement learning, and learner analytics, which allows autonomous real-time monitoring, prediction, and
dynamic updating of instructional pathways based on the learner’s progression. A prototype was developed and tested
with a sample of 120 middle school students from different schools, who were trained at different levels of proficiency.
With the adaptive algorithm, these students obtained significantly higher gains in problem-solving accuracy and
understanding of underlying concepts compared to their peers instructed with traditional methods. Teacher evaluations
also indicated a constructive shift in learner behavior, with improvement in overall class control and enhanced student
engagement in the lessons. The study contributes to the knowledge of algorithmic personalization in relation to equity
and efficiency in the mathematics education system. Information derived from the evaluation of the system is of great
value in the integration of such technologies into curriculum design and pedagogy. This document triadically exposits
on the use of adaptive algorithms towards closing the learning gaps in mathematics education and increased access
for all learners irrespective of their learning abilities.