Recommendation System for Student E-Learning Courses

摘要

Nowadays to develop an e-learning system which provides the learning environment suitable for each individual learner's learning style. In this system, we developed a method to recommend courses that are suitable for a student. A student's course adaptability for a particular course can be estimated based on the result gained from a previous choices which is conducted prior to the beginning of the course by using the multiple regression models that has been derived from the past students' data. The validity of the developed method was confirmed to some extent by applying the method to assess the student's course adaptability for each course. Furthermore, this system explains the functions of the e-learning course recommendation system that can be added by using the method. The traditional system of selecting courses to carry out research work is time consuming, risky and a tedious task, that not only badly affect the performance but the learning experience of a researcher as well. This approach may be helpful to learners to increase their performance and improve their satisfaction level as well. The proposed recommender systems would perform better by mitigating the weakness of basic individual recommender systems. There are many courses available for students, and sometimes it is hard for a student to perceive information related to those courses and decide which course to take. This work aims to build a system to suggest online courses to users based on their profile and the similarity with other users. For this work three techniques were used to extract the information and suggest online courses: Apriori algorithm By combining these three techniques the system can offer more accurate recommendations and only considers the interests of each user. Thus, users will not feel tired while perceiving information of their interest and will keep engaged and interested to use the system.