ICYCSEE 2016: Social Computing pp 92-101 | Cite as

Thread Structure Prediction for MOOC Discussion Forum

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 624)

Abstract

Discussion forums are an indispensable interactive component for Massive Open Online Courses (MOOC). However, the organization of current discussion forums is not well-designed. Trouble-shooting threads are valuable for both learners and instructors, but they are drowned out in the forums with huge amounts of threads. This work first built a labeled data set for trouble-shooting thread structure prediction by crowdsourcing and then proposed methods for trouble-shooting thread detection and thread structure prediction on the data set. The output of this work can be used to spot trouble-shooting threads and show them along with structure tags in MOOC discussion forums.

Keywords

Thread structure prediction Crowdsourcing Lightly supervised learning MOOC 
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Notes

Acknowledgment

This work is sponsored by Quanta Computers, Inc. under the Qmulus Project and National Natural Science Foundation of China (61572151 and 71573065).

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.MIT Computer Science and Artificial Intelligence LaboratoryCambridgeUSA

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