Please use this identifier to cite or link to this item: https://doi.org/10.18653/v1/W18-3720
Title: Countering Position Bias in Instructor Interventions in MOOC Discussion Forums
Authors: Muthu Kumar Chandrasekaran
Min-Yen Kan 
Issue Date: 2018
Publisher: Association for Computational Linguistics
Citation: Muthu Kumar Chandrasekaran, Min-Yen Kan (2018). Countering Position Bias in Instructor Interventions in MOOC Discussion Forums. The 5th Workshop on Natural Language Processing Techniques for Educational Applications : 135-142. ScholarBank@NUS Repository. https://doi.org/10.18653/v1/W18-3720
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
Source Title: The 5th Workshop on Natural Language Processing Techniques for Educational Applications
URI: https://scholarbank.nus.edu.sg/handle/10635/172416
DOI: 10.18653/v1/W18-3720
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