Name
Abstract | ||
|---|---|---|
We compare the purposes, inputs, representations, and assumptions of three methods to evaluate the fine-grained interactions of intelligent tutors with their students. One method is conventional analysis of randomized controlled trials (RCTs). The second method is learning decomposition, which estimates the impact of each practice type as a parameter in an exponential learning curve. The third method is knowledge tracing, which estimates the impact of practice as a probability in a dynamic Bayes net. The comparison leads to a generalization of learning decomposition to account for slips and guesses. |
Year | Venue | Keywords |
|---|---|---|
2008 | EDM | learning curve,randomized controlled trial |
Field | DocType | Citations |
Exponential function,Computer science,Bayesian network,Artificial intelligence,Machine learning,Tracing | Conference | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jack Mostow | 1 | 1133 | 263.51 |
| Xiaonan Zhang | 2 | 8 | 1.85 |