Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2724660.2728681 | L@S |
Keywords | Field | DocType |
collaborative filtering | World Wide Web,Collaborative filtering,Computer science,Multimedia | Conference |
Citations | PageRank | References |
5 | 0.45 | 1 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mo Zhou | 1 | 8 | 1.84 |
| Alison Cliff | 2 | 6 | 0.83 |
| Allen Huang | 3 | 5 | 0.45 |
| S. Krishnan | 4 | 391 | 36.25 |
| Brandie Nonnecke | 5 | 11 | 3.26 |
| Kanji Uchino | 6 | 11 | 3.13 |
| Sam Joseph | 7 | 5 | 0.45 |
| Armando Fox | 8 | 6238 | 524.64 |
| Ken Goldberg | 9 | 3785 | 369.80 |