Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data
Data mining is the process which is used to analyze the large database to find the useful pattern. Data mining can be used to learn about students behavior from data collected using the course management system such as Moodle (Modular Object-Oriented Developmental Learning Environment). Here in this paper we show how data mining techniques such as clustering and association rule algorithm is useful in Course Recommendation System which recommends the course to the student based on choice of other students for particular set of courses collected from Moodle. As a result of Course Recommendation System, we can recommend to new student who has recently enrolled for some course e.g. Operating System, the new course to be opted e.g. Distributed System. Our approach uses combination of clustering technique  Simple K-means and association rule algorithm  Apriori and finds the result. These results were compared with the results of open source data mining tool-Weka. The result obtained using combined approach matches with real world interdependencies among the courses. Other combinations of clustering and association rule algorithms are also discussed here to select the best combination. This Course Recommendation System could help in building intelligent recommender system. This approach of recommending courses to new students can be immensely be useful in MOOC (Massively Open Online Courses).
moodle simple k-means apriori weka farthest first expectation maximization tertius predictiveapriori