Course Recommender System for Student Enrollment Using Augmented Reality
Sakuna Anuvareepong, Supawadee Phooim, Nawachon Charoenprasoplarp, Samitra Vimonratana · 2017
To comply with Thailand 4.0, this paper aims at developing an innovative course recommender system for student enrollment in Martin de Tours School of Management and Economics, Assumption University. The research model namely, AR-CoReSSe (Course Recommender System for Student Enrollment using Augmented Reality) is consisted of 4 main components; Hybrid recommender system, Collaborative filtering (CF), RFD (Recency, Frequency, Duration) model and Augmented Reality (AR) technology. The results from data analysis of the proposed model show that the real time relevance feedback of hybrid recommender system, analytical recommended information of the CF model, students period of time preparing for the enrollment of RFD and video presentation by lecturer of AR have significant relationship with students majoring in marketing, non-major students and students majoring in finance and banking, all at highly significant level.