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Countering Position Bias in Instructor Interventions in MOOC Discussion Forums

机译:在MOOC论坛中应对教师干预中的职位偏见

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摘要

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 F_1 and 24.4% in recall on average.
机译:我们系统地确认,大型在线公开课程(MOOC)讨论论坛的用户界面演示对教师产生了很大的影响。在大规模数据集中,我们最终表明,教师干预表现出很强的位置偏见,这是通过干预时线程在用户界面上出现的位置来衡量的。我们测量并消除了这种偏差,从而实现了无偏统计建模和评估。我们表明,与最新技术相比,我们的去偏分类器在F_1上具有足够的干预数量,而在召回率上的平均干预水平则提高了8.2%。

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