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.

Self Learning is nothing but Pre-Recorded Videos. These videos recorded while we were conducted instructor-Led online classes. Course bundle is collection of multiple batch videos for the same course by the same faculty. Bundles helps to clarify our questions. What is Course Future Updates?

Questions of how AI can help in learning are particularly important this year. This article contains some ways that students will find it beneficial for their learning process. Educational AI allows for a more personal approach to teaching. How do students learn better? They can have more time with a personal tutor.