Publication Date
| In 2015 | 0 |
| Since 2014 | 0 |
| Since 2011 (last 5 years) | 2 |
| Since 2006 (last 10 years) | 2 |
| Since 1996 (last 20 years) | 2 |
Descriptor
| Models | 2 |
| Prediction | 2 |
| Accuracy | 1 |
| Cluster Grouping | 1 |
| Comparative Analysis | 1 |
| Data | 1 |
| Data Analysis | 1 |
| Elementary Secondary Education | 1 |
| Homogeneous Grouping | 1 |
| Intelligent Tutoring Systems | 1 |
| More ▼ | |
Source
| International Educational… | 2 |
Author
| Heffernan, Neil T. | 2 |
| Pardos, Zachary A. | 1 |
| Sarkozy, Gabor N. | 1 |
| Trivedi, Shubhendu | 1 |
| Wang, Yutao | 1 |
Publication Type
| Speeches/Meeting Papers | 2 |
| Reports - Descriptive | 1 |
| Reports - Research | 1 |
Education Level
| Elementary Secondary Education | 1 |
Audience
Showing all 2 results
Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T. – International Educational Data Mining Society, 2012
Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…
Descriptors: Homogeneous Grouping, Prediction, Tutors, Cluster Grouping
Wang, Yutao; Heffernan, Neil T. – International Educational Data Mining Society, 2012
The field of educational data mining has been using the Knowledge Tracing model, which only look at the correctness of student first response, for tracking student knowledge. Recently, lots of other features are studied to extend the Knowledge Tracing model to better model student knowledge. The goal of this paper is to analyze whether or not the…
Descriptors: Reaction Time, Students, Knowledge Level, Models


