Abstract
The present work is a part of the ESTenLigne project which is the result of several years of experience for developing e-learning in Sidi Mohamed Ben Abdellah University through the implementation of open, online and adaptive learning environment. However, this platform faces many challenges, such as the increasing amount of data, the diversity of pedagogical resources and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to take the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a distributed courses recommender system for the e-learning platform. It aims to discover relationships between student’s activities using association rules method in order to help the student to choose the most appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem. The experimental results show the effectiveness and scalability of the proposed system. Finally, we evaluate the performance of Spark MLlib library compared to traditional machine learning tools including Weka and R.
Highlights
The computing environment for human learning is changing rapidly, due to the emergence of new information and communication technology such as big data [1] and cloud computing [2]
Recent development in new information and communication technologies, especially big data paradigm provides powerful frameworks and techniques which can be used to deal with online learning problems
This paper aims to create a distributed course recommender system for helping students’ to take more appropriate pedagogical resources
Summary
The computing environment for human learning is changing rapidly, due to the emergence of new information and communication technology such as big data [1] and cloud computing [2]. Our system uses association rules for extracting more interesting relationships between learners’ behaviors It aims to find similarities between courses enrollments in the transaction database. Discovering association rules enables us to target students who learn two or more courses together, i.e. finding a list of frequent courses enrollments to determine those that are more likely chosen by the learners. The article uses distributed data mining algorithms and data obtained from Learning Management Systems (LMS) database in order to identify strong correlations between sets of courses followed by students. It gives a brief description of the architecture and methodology of the course recommender system without providing an implementation of the proposed architecture
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