M-CAFE:用协同过滤管理MOOC学生反馈

Mo Zhou, A. Cliff, Allen Huang, S. Krishnan, Brandie Nonnecke, Kanji Uchino, Samuelson Joseph, A. Fox, Ken Goldberg
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引用次数: 7

摘要

在缺乏面对面交流的情况下,学生对课程内容和作业的持续反馈对MOOC教师来说很有价值。为了收集持续的反馈并可扩展地识别有价值的建议,我们建立了MOOC协作评估和反馈引擎(M-CAFE)。这个移动平台允许MOOC学生对课程和他们自己的表现进行数字评估,并提供关于如何改进课程的文本建议。M-CAFE允许学生可视化他们与同龄人的比较,并阅读和评估其他人的建议,提供点对点的协作过滤。我们基于两个EdX mooc的数据来评估M-CAFE。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
M-CAFE: Managing MOOC Student Feedback with Collaborative Filtering
Ongoing student feedback on course content and assignments can be valuable for MOOC instructors in the absence of face-to-face-interaction. To collect ongoing feedback and scalably identify valuable suggestions, we built the MOOC Collaborative Assessment and Feedback Engine (M-CAFE). This mobile platform allows MOOC students to numerically assess the course, their own performance, and provide textual suggestions about how the course could be improved on a weekly basis. M-CAFE allows students to visualize how they compare with their peers and read and evaluate what others have suggested, providing peer-to-peer collaborative filtering. We evaluate M-CAFE based on data from two EdX MOOCs.
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