Adaptive Learning Systems based on ILOs of Courses

ABSTRACT

Nowadays, the use of e-learning techniques and methods is a very important challenge due to the importance of digital transformation to all countries. Firstly, the spread of the COVID-19 virus all over the world. Secondly, all students need to study their courses remotely from home to reduce the communication with others to save their life. All teachers need to engage their students effectively to study an online course, get more knowledge and high results at the end of these courses. Data mining is the best tool used to find a hidden pattern. We used an educational data mining tool to help teachers find the pros and cons of using an e-learning course with their students. We need to classify students on these online courses according to their ability to understand materials and quizzes, or assessment methods of the course, by making adaptive e-learning courses. In this paper, we will show the importance of using adaptive e-learning courses and the challenges faced by authors to build these systems, and we will list the different methods used with adaptive learning like gamification, brain-hex models, facial emotions, and we will also list a survey about other authors' techniques and methods used to find the student's learner style. We build a new proposed model of ILOs(Intended Learning Outcomes) adaptive learning with the emotion-based system to let the system find the student's learning style and build the material according to their skills and knowledge outcomes from the course and engage the use of facial emotion while taking the quiz to predict the student's results and the topics he/she needs to study more via our system to achieve high grades and knowledge. Our system finds that the visual students have the highest grades with 75%, followed by kinesthetic with 70% and the lowest grades in auditory with 50%. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.