Towards feasible instructor intervention in MOOC discussion forums
Research Area: Information Extraction Year: 2015
Type of Publication: In Proceedings  
Authors:
  • Muthu Kumar Chandrasekaran
  • Kiruthika Ragupathi
  • Bernard C.Y. Tan
  • Min-Yen Kan
 
   
Abstract:
Massive Open Online Courses allow numerous people from around the world to have access to knowledge that they otherwise have not. However, high student-to-instructor ratio in MOOCs restricts instructors’ ability to facilitate student learning by intervening in discussions forums, as they do in face-to-face classrooms. Instructors need automated guidance on when and how to intervene in discussion forums. Using a typology of pedagogical interventions derived from prior research, we annotate a large corpus of discussion forum contents to enable supervised machine learning to automatically identify interventions that promote student learning. Such machine learning models may allow building of dashboards to automatically prompt instructors on when and how to intervene in discussion forums. In the longer term, it may be possible to automate these interventions relieving instructors of this effort. Such automated approaches are essential for allowing good pedagogical practices to scale in the context of MOOC discussion forums.
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