Mining Students Social Data to Understand Their Learning Experience

Abstract

Students informal conversation on social network (Eg.Facebook, Twitter )can be used to know their educational experiences such as opinions, concerns and learning process.These data from such sites will help us to know about students learning and their difficulties.The complexity is that we need human interpretation in analysis of the data.In this paper, we developed a workflow to integrate both qualitative analysis andlarge-scale data mining techniques. We focused on engineering students' Twitter posts to understand issues and problems intheir educational experiences. We found engineering students encounter problems such as heavy study load, lack of socialengagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classifytweets reflecting students' problems. We then used the algorithm to train a detector of student problems from about 35,000tweets. This work, for the first time, presents a methodology and results thatshow how informal social media data can provide insights into students' experiences.