Name
Abstract | ||
|---|---|---|
A learning diagnosis system collects data from a learner's learning process, and analyzes it to build a suitable model for the learner, which can then be incorporated into an intelligent tutoring system to provide customized tutoring services. However, if the collected data reflects inconsistent learner behaviors or unpredictable learning tendencies, then the reliability of the learner model is degraded. In this paper, the outliers in the learner's data are eliminated by a k-NN method We apply this method to an experimental data set obtained using DOLLS-HI, a learner diagnosis system that uses housing interior learning contents to diagnose learning styles. The resulting diagnosis model shows improved reliability than before eliminating the outliers. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICALT.2007.25 | 7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS |
Keywords | Field | DocType |
data engineering,information analysis,data analysis,user interface,voting,intelligent systems,user interfaces | Learning styles,Intelligent tutoring system,Experimental data,Computer science,Outlier,User centred design,User interface,Multimedia | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Se Kim | 1 | 191 | 20.22 |
| Tae Bok Yoon | 2 | 80 | 13.13 |
| Hyun Jin Cha | 3 | 53 | 4.13 |
| Young Mo Jung | 4 | 52 | 3.18 |
| Eric Wang | 5 | 30 | 4.26 |
| Jee-Hyong Lee | 6 | 316 | 49.65 |