Learning Style Identification Using Semi-Supervised Self-Taught Labeling
Hani Y. Ayyoub Omar S. Al-Kadi
Education is a dynamic field that must be adaptable to sudden changes and
disruptions caused by events like pandemics, war, and natural disasters related
to climate change. When these events occur, traditional classrooms with
traditional or blended delivery can shift to fully online learning, which
requires an efficient learning environment that meets students' needs. While
learning management systems support teachers' productivity and creativity, they
typically provide the same content to all learners in a course, ignoring their
unique learning styles. To address this issue, we propose a semi-supervised
machine learning approach that detects students' learning styles using a data
mining technique. We use the commonly used Felder Silverman learning style
model and demonstrate that our semi-supervised method can produce reliable
classification models with few labeled data. We evaluate our approach on two
different courses and achieve an accuracy of 88.83% and 77.35%, respectively.
Our work shows that educational data mining and semi-supervised machine
learning techniques can identify different learning styles and create a
personalized learning environment.