Organizers

Elle Yuan Wang*

Elle is a Doctoral Research Fellow in Cognitive Sciences at Columbia University Teachers College. Her current research focuses on Massive Online Open Courses (MOOCs) learner motivation and prediction of learner post-course development, with the goal of building frameworks toward operationalizing MOOC learner success. Specifically, her projects takes a comprehensive approach by linking three sources of learner data: pre-course learner motivation, within-course learner engagement, as well as post-course development. Particularly, measurement of post-course development reflects both individual learner career development as well as advancement of communities of practice. Her recent publications can be seen in Journal of Online Learning and Teaching and Journal of Learning Analytics.

*Primary Contact

Dan Davis

Dan is a Ph.D. candidate at TU Delft in the Netherlands. Housed in the Lambda-Lab in the Web Information Systems group, his research explores how changes in the online learning environment can effect increases in both student success and engagement. He primarily does so by testing various theory-based (from educational and cultural psychology) interventions in working towards a heightened level of adaptivity in scalable online education.

Guanliang Chen

Guanliang is a Ph.D. candidate working in the Web Information Systems Group in the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS/EWI), Delft University of Technology. His research domain is Massive Open Online Courses, in which he explores and analyzes the traces generated by MOOC learners and aim to enrich learner models through external data sources, in particular, the Social Web with the goal of improving engagement and retention in MOOCs.

Luc Paquette (Ph.D.)

Luc is an Assistant Professor of Curriculum & Instruction at the University of Illinois at Urbana-Champaign, where he specializes in Educational Data Mining and Learning Analytics. His research focuses on the usage of machine learning, data mining and knowledge engineering approaches to analyze the behavior of students while the use digital learning environments such as MOOCs, intelligent tutoring systems and educational games and investigate how those behaviors are related to learning outcomes.