IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 3: June 2026

Personalized learning with learning style using fuzzy for university students performance

Endina Putri Purwandari (University of Bengkulu)
Endang Widi Winarni (University of Bengkulu)
Siti Soraya Abdul Rahman (Universiti of Malaya)
Jafar Nashrudin Al Azam (Ministry of Religious Affairs)



Article Info

Publish Date
01 Jun 2026

Abstract

The main challenges of traditional learning systems are time-space constraints and teacher-centeredness. The emergence of information technology has given rise to e-learning systems characterized by teacher centred strategy components and one-size-fits-all strategies. Furthermore, the concept of personalization is presented through learning technology that provides educational content to the students learning style. This research develops a personalized system that aligns teaching strategies with students' learning styles using the Myers-Briggs Type Indicator (MBTI). The emphasis is on adaptive and revising teaching strategies to improve student learning performance. The system is developed to create student profiles to determine their learning styles based on the MBTI and fuzzy. The system was tested with undergraduate students at the information systems department in University of Bengkulu. Research shows that students in the experimental group have higher post-test scores, greater learning achievement and performance than the control group. Fuzzy clustering based personalized e-learning could improve university student performance. The use of personalized online learning significantly affects learning management system (LMS) integration, lecturers, and curriculum development.

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Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...