Learning@Scale: A Framework for Topic Generation and Labeling from MOOC Discussions

Our first results from our Google Research Grant exploring personalised learning at scale has been accepted as a Work in Progress paper at the upcoming Learning@Scale conference. We are excited to be able to share some of our preliminary results in such a forum, and to be able to share our thoughts on where this work will go next!

T. Atapattu and K. Falkner, A Framework for Topic Generation and Labeling from MOOC Discussions. Accepted for Learning @ Scale 2016 (Work in Progress).

This study proposes a standardised open framework to automatically generate and label discussion topics from Massive Open Online Courses (MOOCs). The proposed framework expects to overcome the issues experienced by MOOC participants and teaching staff in locating and navigating their information needs effectively. We analysed two MOOCs – Machine Learning and Statistics: Making Sense of Data offered during 2013 and obtained statistically significant results for automated topic labeling. However, more experiments with additional MOOCs from different MOOC platforms are necessary to generalise our findings.

Tagged in CSER, research, Computer Science