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
- Editors:
- Yuen-Hsien Tseng, Hsin-Hsi Chen, Vincent Ng, Mamoru Komachi
- Venue:
- NLP-TEA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 135–142
- Language:
- URL:
- https://aclanthology.org/W18-3720
- DOI:
- 10.18653/v1/W18-3720
- Cite (ACL):
- Muthu Kumar Chandrasekaran and Min-Yen Kan. 2018. Countering Position Bias in Instructor Interventions in MOOC Discussion Forums. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 135–142, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Countering Position Bias in Instructor Interventions in MOOC Discussion Forums (Chandrasekaran & Kan, NLP-TEA 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-3720.pdf