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EDM
2008
108views Data Mining» more  EDM 2008»
13 years 6 months ago
Do Students Who See More Concepts in an ITS Learn More?
Active engagement in the subject material has been strongly linked to deeper learning. In traditional teaching environments, even though the student might be presented with new con...
Moffat Mathews, Tanja Mitrovic
EDM
2008
193views Data Mining» more  EDM 2008»
13 years 6 months ago
Can we predict which groups of questions students will learn from?
In a previous study ([4]), we used the ASSISTment system to track student knowledge longitudinally over the course of a schools year, based upon each student using our system about...
Mingyu Feng, Neil T. Heffernan, Joseph E. Beck, Ke...
EDM
2008
141views Data Mining» more  EDM 2008»
13 years 6 months ago
An Open Repository and analysis tools for fine-grained, longitudinal learner data
We introduce an open data repository and set of associated visualization and analysis tools. The Pittsburgh Science of Learning Center's "DataShop" has data from tho...
Kenneth R. Koedinger, Kyle Cunningham, Alida Skogs...
EDM
2008
96views Data Mining» more  EDM 2008»
13 years 6 months ago
Labeling Student Behavior Faster and More Precisely with Text Replays
We present text replays, a method for generating labels that can be used to train classifiers of student behavior. We use this method to label data as to whether students are gamin...
Ryan Shaun Joazeiro de Baker, Adriana M. J. B. de ...
EDM
2008
97views Data Mining» more  EDM 2008»
13 years 6 months ago
Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to it...
Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koe...