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Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data

机译：结合机器学习算法，基于历史数据在电子学习系统中推荐课程

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摘要

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 student's 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)".
机译：数据挖掘是用于分析大型数据库以找到有用模式的过程。数据挖掘可用于从使用课程管理系统（例如Moodle（面向对象的模块化开发学习环境））收集的数据中了解学生的行为。在本文中，我们展示了数据挖掘技术（例如聚类和关联规则算法）在“课程推荐系统”中如何有用，该课程推荐系统根据从Moodle收集的特定课程集的其他学生的选择向学生推荐课程。通过课程推荐系统，我们可以向刚入读某些课程的新学生推荐课程，例如操作系统，要选择的新课程，例如分布式系统。我们的方法结合使用聚类技术-简单K均值和关联规则算法-Apriori并找到结果。将这些结果与开源数据挖掘工具Weka的结果进行了比较。使用组合方法获得的结果与课程之间的实际相互依赖关系相匹配。这里还讨论了聚类和关联规则算法的其他组合，以选择最佳组合。该课程推荐系统可以帮助构建智能推荐系统。这种向新学生推荐课程的方法在“ MOOC（大规模开放在线课程）”中非常有用。

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