Title: MINING FEATURE-OPINION IN EDUCATIONAL DATA FOR COURSE IMPROVEMENT
Issue Number: | Vol. 1, No. 4 |
Year of Publication: | Dec - 2011 |
Page Numbers: | 1076-1085 |
Authors: | Alaa El-Halees |
Journal Name: | International Journal of New Computer Architectures and their Applications (IJNCAA) - Hong Kong |
Abstract:
In academic institutions, student comments about courses can be considered as a significant informative resource to improve teaching effectiveness. This paper proposes a model that extracts knowledge from students' opinions to improve and to measure the performance of courses. Our task is to use user-generated contents of students to study the performance of a certain course and to compare the performance of some courses with each others. To do that, we propose a model that consists of two main components: Feature extraction to extract features, such as teacher, exams and resources, from the user-generated content for a specific course. And classifier to give a sentiment to each feature. Then we group and visualize the features of the courses graphically. In this way, we can also compare the performance of one or more courses.