Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method

Personalized learning paths are designed to optimize learning time and improve student learning performance by providing an appropriate learning sequence based on the unique characteristics of each student. A common method for constructing personalized learning paths is based on the student's knowledge but disregards the student's interest in the subject matter. This research employs a deep learning and rule-based approach to recommend suitable material based on the topic's difficulty, student interest, and knowledge level. The difficulty level of the topic is predicted using deep learning. A questionnaire is used to determine the level of student interest, which is then processed using a rule-based approach to generate a learning path. Modeling a dynamic learning path requires measuring student knowledge in each topic and updating the learning path accordingly. Comparing the learning outcomes of students who utilized conventional e-learning versus those who followed a personalized learning path constitutes the evaluation. The results demonstrated that students scored 29% higher, or 15.06 points, than those who utilized conventional e-learning.