Hybrid Reinforcement Approach for Customized Learning and Path Suggestion
A student can learn anywhere in the world by the unique chance created by E-learning and its advantages. Because of network information technology development, the MOOCs platform has become very popular. At the same time, students get confused in choosing the correct course which fits for the career, creating an increasing burden in the current scenario. This is because of the increased online learning contents. A sequential recommendation system addresses this issue based on students‘ past learned courses. This approach is completely different from interactive recommendation models as the learners are suggested to choose the course based on their own preferences. In the proposed method, we have addressed the above problem by using algorithms like fuzzy decision making and SVM integrated with reinforcement learning.