@inproceedings{chandrasekaran-kan-2018-countering,
title = "Countering Position Bias in Instructor Interventions in {MOOC} Discussion Forums",
author = "Chandrasekaran, Muthu Kumar and
Kan, Min-Yen",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Ng, Vincent and
Komachi, Mamoru",
booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3720",
doi = "10.18653/v1/W18-3720",
pages = "135--142",
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.",
}
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%0 Conference Proceedings
%T Countering Position Bias in Instructor Interventions in MOOC Discussion Forums
%A Chandrasekaran, Muthu Kumar
%A Kan, Min-Yen
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chandrasekaran-kan-2018-countering
%X 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.
%R 10.18653/v1/W18-3720
%U https://aclanthology.org/W18-3720
%U https://doi.org/10.18653/v1/W18-3720
%P 135-142
Markdown (Informal)
[Countering Position Bias in Instructor Interventions in MOOC Discussion Forums](https://aclanthology.org/W18-3720) (Chandrasekaran & Kan, NLP-TEA 2018)
ACL