Please use this identifier to cite or link to this item:
https://doi.org/10.18653/v1/W18-3720
DC Field | Value | |
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dc.title | Countering Position Bias in Instructor Interventions in MOOC Discussion Forums | |
dc.contributor.author | Muthu Kumar Chandrasekaran | |
dc.contributor.author | Min-Yen Kan | |
dc.date.accessioned | 2020-08-12T01:57:23Z | |
dc.date.available | 2020-08-12T01:57:23Z | |
dc.date.issued | 2018 | |
dc.identifier.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 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/172416 | |
dc.description.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 | |
dc.publisher | Association for Computational Linguistics | |
dc.type | Conference Paper | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.18653/v1/W18-3720 | |
dc.description.sourcetitle | The 5th Workshop on Natural Language Processing Techniques for Educational Applications | |
dc.description.page | 135-142 | |
dc.published.state | Published | |
dc.grant.id | C-252-000-123-001 | |
dc.grant.fundingagency | NUS Learning Innovation Fund ll National Research Foundation | |
Appears in Collections: | Elements Staff Publications |
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nlp-tea-countering.pdf | 270.93 kB | Adobe PDF | OPEN | Post-print | View/Download |
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