Understanding students learning experiences through mining user generated contents on social media

Abstract

This paper presents a work of mining informal social media data to provide insights into students’ learning experiences. Analyzing such kind of data is a challenging task because of the data volume, the complexity and diversity of languages used in these social sites. In this study, we developed a framework which integrating both qualitative analysis and different data mining techniques in order to understand students’ learning experiences. This is the first work focusing on mining Vietnamese forums for students in natural science fields to understand issues and problems in their education. The results indicated that these students usually encounter problems such as heavy study load, sleepy problem, negative emotion, English barriers, and carreers’ targets. The experimental results are quite promising in classifying students’ posts into predefined categories developed for academic purposes. It is expected to help educational managers get necessary information in a timely fashion and then make more informed decisions in supporting their students in studying.