A Semantic Tag-based enhanced Learning Recommendation approach for enhancing student learning experiences

Abstract

With the rapid development of semantic web and ubiquitous learning applications, constructing prior knowledge has also become increasingly important for on-line reading learning. Traditional studies on prior knowledge generation during reading activities have focused on extracting sentences from reading materials that are manually generated by website administrators and educators. This is time-consuming and strenuous, and hence personalized prior knowledge recommendation is difficult to perform. To cope with this problem, we combine the concept of prior knowledge with semantic tagging methods to assist the reading comprehension of students studying science subject, which then identifies suitable supplementary materials for quickly constructing a student’s prior knowledge reservoir. Moreover, we incorporate tags into a tag based learning approach to automatically monitor running activities and student status. The experimental results clearly show that the proposed approach not only signific antly improves the efficiency of knowledge learning but also helps teachers assist students in improving their reading ability.