alexa Developing an Intelligent Recommendation System for Cou
ISSN: 2151-6219

Business and Economics Journal
Open Access

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Research Article

Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses

Grewal DS1* and Kaur K2

1Research & Development, Desh Bhagat University, Mandi Gobindgarh, Ludhiana, India

2Guru Nanak Dev Engineering College, Ludhiana, India

Corresponding Author:
Grewal DS
Dean Research & Development, Desh Bhagat University
Mandi Gobindgarh, India
Tel: 082838 11111
E-mail: [email protected]

Received Date: March 15, 2016; Accepted Date: March 30, 2016; Published Date: April 06, 2016

Citation: Grewal DS, Kaur K (2016) Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses. Bus Eco J 7:209. doi:10.4172/2151-6219.1000209

Copyright: © 2016 Grewal DS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Choosing a right course in formative years is very important decision as his future depends on this one decision. Student by himself is not mature enough to take right decision in his early life. Selecting wrong courses means mismatch between student aptitude, capability and personal interest. Faculty or parents have neither the required knowledge nor experience. Since there is no other reliable source generally available that can guide the student towards the most suitable direction, recommender system has been evolved to provide him guidance in selecting a right course. Recommender system is a computer programme prepared with the help of experts where the details of background of the students and their aptitudes help finding a course for his future study. This paper proposes feasible predictions for student’s course selection based on their marks and choice of job interest. Clustering technique is used to find structures and relationship within the data. This paper also reveals the research process of preparation of such a recommender system.

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