Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis

M. Pérez-Sanagustín, Ronald Pérez-Álvarez, Jorge Maldonado-Mahauad, Esteban Villalobos, Isabel Hilliger, Josefina Hernandez, Diego Sapunar, Pedro Manuel Moreno-Marcos, P. Muñoz-Merino, C. D. Kloos, J. Marín
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引用次数: 5

Abstract

This work in progress paper investigates if timely feedback increases learners' passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.
基于预测数据的反馈能提高mooc学习者的通过率吗?初步分析
这篇正在进行的论文调查了及时反馈是否能提高MOOC学习者的通过率。在Coursera平台上对2421名学习者进行了一项实验,测试每周向具有相同退学概率的学习者群体发送信息是否能提高留存率。这些信息可以包含以下信息:(1)在课程中花费的平均时间,或(2)每次学习的平均时间,或(3)完成的练习,或(4)完成的视频讲座。初步结果显示,与在没有干预的情况下参加同一课程的1,445名学习者的数据相比,干预后的完成率提高了12%。我们讨论了这些初步结果的局限性以及由此衍生的未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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