학술논문

M-CAFE : Managing MOOC Student Feedback with Collaborative Filtering
Document Type
Conference
Source
Proceedings of the Second (2015) ACM Conference on Learning @ Scale. :309-312
Subject
collaborative filtering
course assessment
instructor support
moocs
Language
English
Abstract
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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