Name
Abstract | ||
|---|---|---|
This paper describes two diagnostic tools to predict students are at risk of dropping out from an online class. While thousands of students have been attracted to large online classes, keeping them motivated has been challenging. Experiments on a large, online HCI class suggest that the tools these paper introduces can help identify students who will not complete assignments, with an F1 score of 0.46 and 0.73 three days before the assignment due date. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2441955.2441987 | CSCW Companion |
Keywords | Field | DocType |
diagnostic tool,online class,online hci class,large online class,f1 score,complete assignment,assignment due date,online education | Data science,Educational technology,F1 score,Computer science,Online participation,Online research methods,Multimedia,Diagnostic tools | Conference |
Citations | PageRank | References |
9 | 0.73 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Cheng | 1 | 799 | 34.10 |
| Chinmay Eishan Kulkarni | 2 | 383 | 30.71 |
| Scott Klemmer | 3 | 2977 | 197.02 |