Name
Abstract | ||
|---|---|---|
We present a novel method for predicting the evolution of a student's grade in massive open online courses (MOOCs). Performance prediction is particularly challenging in MOOC settings due to per-student assessment response sparsity and the need for personalized models. Our method overcomes these challenges by incorporating another, richer form of data collected from each student-lecture video-watc... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTSP.2017.2700227 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Videos,Predictive models,Prediction algorithms,Signal processing algorithms,Neural networks,Time series analysis,Electronic mail | Time series,Predictive learning,Learning analytics,Clickstream,Regression,Computer science,Lasso (statistics),Artificial intelligence,Analytics,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 5 | 1932-4553 |
Citations | PageRank | References |
9 | 0.55 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsung-Yen Yang | 1 | 10 | 3.63 |
| Christopher G. Brinton | 2 | 118 | 15.23 |
| Carlee Joe-Wong | 3 | 560 | 49.42 |
| Mung Chiang | 4 | 7303 | 486.32 |