Mining Social Media Data for Understanding Students' Learning Experiences

Xin Chen, Mihaela Vorvoreanu, Krishna P.C. Madhavan
2014 IEEE Transactions on Learning Technologies  
ARTICLE INFO Social media sites such as Twitter, Facebook, and YouTube provide great venues for students to share joy and struggle, vent emotion and stress, and seek social support. On various social media sites, students discuss and share their everyday encounters in an informal and casual manner. Students' digital footprints provide vast amount of implicit knowledge and a whole new perspective for educational researchers and practitioners to understand students' experiences outside the
more » ... led classroom environment. In this paper, a work-flow is developed which combines both qualitative investigation and large-scale data mining scheme. It is found that certain issues like heavy study load, hectic schedule and lack of sleep are encountered by the students. Hence these issues are classified using Naive Bayes Multi-label Classifier algorithm. This classification can help in understanding the student's problem in a very efficient way.
doi:10.1109/tlt.2013.2296520 fatcat:zp552le4bvh5bba5vocbhr4ofy