Guanliang Chen

PhD student in the Learning Analytics team of the Web Information Systems Group in the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS/EWI), Delft University of Technology.

My research domain is Massive Open Online Courses (MOOCs). I explore and analyse the traces generated by MOOC learners and aim to enrich the learner models through external sources, in particular the Social Web with the goal of improving engagement and retention in MOOCs. The graphic below shows the main foci of existing works in this field of research: a lot of works are dedicated towards investigating the traces generated within MOOC environments, while I explore the traces learners generate outside of MOOC environments. Specifically, over the past year, I have been applying techniques from the areas of user modelling, data mining and machine learning to:

  • investigate to what extent learning transfer insights gained in workplace and classroom settings hold in the MOOC context (one workshop paper accepted by LWMOOC 2015 and one full paper accepted by L@S 2016).
  • explore the feasibility of paying students to take MOOCs so as to improve their engagement in the course.
  • explore the kind of information relevant to learning in MOOCs the social Web offers about users (extended abstracts of this work were accepted at GESIS Winter Symposium and ICT.OPEN 2016).
  • investigate the impact of personality in the MOOC environment.

More detailed information is available at: https://angusglchen.github.io/