Name
Abstract | ||
|---|---|---|
Several efforts have been put forth in finding algorithms for identifying optimal learning maps for a given cognitive domain. In (Adjei, et. al. 2014), we proposed a greedy search algorithm for searching data fitting models with equally accurate predictive power as the original skill graph, but with fewer nodes/skills in the graph. In this paper we present PLACEments, an adaptive testing system, and report on how it can be used to determine the strength of the prerequisite skill relationships in a given skill graph. We also present preliminary results that show that different learning maps need to be designed for students with different knowledge levels. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19773-9_51 | ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015 |
Keywords | Field | DocType |
Learning maps, Skill graphs, Adaptive testing | Graph,Curve fitting,Predictive power,Optimal learning,Computer science,Greedy algorithm,Artificial intelligence,Computerized adaptive testing,Cognition,Machine learning | Conference |
Volume | ISSN | Citations |
9112 | 0302-9743 | 2 |
PageRank | References | Authors |
0.45 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seth Adjei | 1 | 19 | 6.02 |
| Neil T. Heffernan | 2 | 1087 | 135.49 |