The Recommendation of Elective Courses Using Data Mining and Collaborative Filtering Techniques

• Main Authors: Pei-shan Li, 李佩珊
• Other Authors: Hsueh-Chi Shih
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/98121240770913385308

碩士 === 雲林科技大學 === 資訊管理系碩士班 === 96 === In the field of education, students play the role of customer. How to actively offer information and assist them in choosing suitable elective courses are agonizing problems in many universities. Although various recommender systems have been proposed to solve these problems, many studies focus on the arrangement of the learning path between different chapters in the same course, such as e-learning, but only few studies for recommending different courses are proposed. In this study, we proposed a methodology to provide personalized recommendations on elective courses based on Data Mining and Collaborative Filtering techniques. The research objects are the 4-year and 2-year technological program students in the Department of Information Management in a National University of Science and Technology. Firstly, students were classified into two groups: freshmen and seniors. Secondly, for freshmen, we used the Collaborative Filtering approach to find the top-N recommendation courses of each cluster of students. For seniors, according to students’ enrolling records and scores, we used the Sequential Patten approach to find the preferred sequential courses and then used the Association Rule approach to find the related courses based on the recommendation results of the Sequential Patten approach. Finally, we used the F1-measure to measures the quality of our recommendation results.