학술논문
M-CAFE : Managing MOOC Student Feedback with Collaborative Filtering
Document Type
Conference
Author
Source
Proceedings of the Second (2015) ACM Conference on Learning @ Scale. :309-312
Subject
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.