Personalized Article Recommendation Based on Student's Rating Mechanism in an Online Discussion Forum
Chuen‐He Liou · 2016
Online discussion forum is one of e-learning activities to construct learner's knowledge and interact with their classmates, which is also a module of Learning Management System (LMS). However, students all have the same latest articles even though their article preferences are different. Traditional LMS could not recommend the personalized articles for individual student based on her or his own preference. In this study, an article recommendation method was proposed based on student's rating to recommend the personalized articles in an online forum. Experiment was conducted with two hundred nursing students in a university of northern Taiwan. Student could rate the articles which classmate posted by the proposed rating mechanism, which is similar to the Like button in Facebook. Experiment result shows that the proposed method performs well compared to collaborative filtering (CF) method. The proposed method could recommend the better personalized articles than the typical CF method.