An Adaptive Learning System Integrated with Fuzzy Logic to Improve Higher-Order Thinking Skills

Abstract

Adaptive learning systems utilize Artificial Intelligence (AI) to build intelligent learning systems. This study describes: (i) a model of an adaptive learning system to improve Higher-Order Thinking (HOT) skills, (ii) the use of fuzzy logic to provide appropriate learning activities, and (iii) an implementation to examine its effect on HOTs. The adaptive learning model includes student, adaptation, content, communication, and instructional models. Fuzzy logic is applied to the adaptation model with four variables: quizzes, individual activities, group assessments, and forum discussions. Then, it produces a learning material that automatically adjusts to the student's thinking skills level. This study involved 164 undergraduate students in the first-year programming course. The four variables produced a t-test significance below 0.05, thus significantly influencing HOT skills, namely evaluating, analyzing, and creating. The results show that experimental classes using adaptive learning have higher thinking skills than traditional learning in control classes. Further research can optimize technical support, including infrastructure, bandwidth connections, and server capability.