应对MOOC论坛讲师干预中的立场偏见

NLP-TEA@ACL Pub Date : 2018-07-01 DOI:10.18653/v1/W18-3720
Muthu Kumar Chandrasekaran, Min-Yen Kan
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引用次数: 2

摘要

我们系统地证实,教师受到大规模在线开放课程(MOOC)讨论论坛的用户界面呈现的强烈影响。在一个大规模的数据集中,我们最终表明,教师干预表现出强烈的位置偏差,正如在干预时线程出现在用户界面上的位置所衡量的那样。我们测量并消除这种偏差,使无偏统计建模和评估成为可能。我们表明,我们的去偏见分类器在具有足够干预数量的课程上,比最先进的干预预测提高了8.2%的F1和24.4%的平均召回率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Countering Position Bias in Instructor Interventions in MOOC Discussion Forums
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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