Countering Position Bias in Instructor Interventions in MOOC Discussion Forums

Muthu Kumar Chandrasekaran, Min-Yen Kan


Abstract
We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2% in F1 and 24.4% in recall on average.
Anthology ID:
W18-3720
Original:
W18-3720v1
Version 2:
W18-3720v2
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NLP-TEA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
135–142
Language:
URL:
https://www.aclweb.org/anthology/W18-3720
DOI:
10.18653/v1/W18-3720
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3720.pdf
Poster:
 W18-3720.Poster.pdf