Developing an intelligent recommendation system for a private university in Thailand
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In Thailand, choosing a program of study for tertiary students is significant due to the associated future job opportunities. Many students have enrolled in course majors without receiving counseling or advices from appropriate authorities or university services. This could have potential mismatch between students’ aptitude, personal interest and capability, and the particular course being taken up. This may lead to low retention rate and failures. In order to improve and support the academic management processes, many universities in Thailand are developing innovative information systems and services with an aim to enhance efficiency and student relationship. Some of these initiatives are in the form of a Student Recommendation System (SRM). In Thailand, this university service is normally provided by professional counselors or advisors who have many years of experience within the organization or in the higher education sector. However, the success or appropriateness of such advice is entirely depending on expertise of the counselor and it is entirely human-driven. In addition, the process is also tedious and time consuming. This paper reports a study on an investigation of possible correlation between student historic data and their final results. Clustering techniques have been used with the aim to find structures and relationship within the data. Results from two clustering methods, k-means and TwoStep methods have been compared. This paper describes the development of the experiments, and the proposed Intelligent Recommendation System framework.
|Publication Type:||Journal Article|
|Murdoch Affiliation:||School of Information Technology|
|Publisher:||International Association for Computer Information Systems|
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