An Architecture for Recommendation of Courses in E-learning System

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Bhupesh Rawat 1,* Sanjay k. Dwivedi 1

1. Computer Science Department of Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India

* Corresponding author.


Received: 11 Apr. 2016 / Revised: 10 Sep. 2016 / Accepted: 17 Nov. 2016 / Published: 8 Apr. 2017

Index Terms

Data mining, Weka, Moodle, Data preprocessing, E-learning, Educational data mining


Over the last few years, the face of traditional learning has changed significantly, due to the emergence of the web. Consequently several learning systems have emerged such as computer-based learning, web-based learning among others, meeting different kinds of educational needs of the learners and educators as well. E-learning systems allow educators, distribute information, create content material, prepare assignments, engage in discussions, and manage distance classes among others. They accumulate a huge amount of data as a result of learner’s interaction with the site. This data can be used to find students’ learning pattern based on which appropriate courses could be recommended to them. However existing approaches of recommending courses to learner offer the same course to all the learners irrespective of their knowledge and skill level which results in decreasing their academic performance. This paper proposes an architecture for the recommendation of courses to a learner based on his/her profile. The profile of a learner is created by applying k-means algorithm to learner’s interaction data in moodle. The results show that the non active learners should not be recommended advanced courses if they have obtained poor marks and are not active in the concern course.  In the initial stage we discover learners’ performance in data mining course which will further be extended to other courses as well.

Cite This Paper

Bhupesh Rawat, Sanjay K. Dwivedi, "An Architecture for Recommendation of Courses in E-learning System", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.39-47, 2017. DOI:10.5815/ijitcs.2017.04.06


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