Paper Info
Title
Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation
Abstract
SimStudent is a machine-learning agent that learns cognitive skills by demonstration. SimStudent was originally built as a building block for Cognitive Tutor Authoring Tools to help an author build a cognitive model without significant programming. In this paper, we evaluate a second use of SimStudent, viz., student modeling for Intelligent Tutoring Systems. The basic idea is to have SimStudent observe human students solving problems. It then creates a cognitive model that can replicate the students' performance. If the model is accurate, it would predict the human students' performance on novel problems. An evaluation study showed that when trained on 15 problems, SimStudent accurately predicted the human students' correct behavior on the novel problems more than 80% of the time. However, the current implementation of SimStudent does not accurately predict when the human students make errors.
Year
Venue
Keywords
2007
AIED
human student,basic idea,building block,cognitive skill,cognitive model,correct behavior,learning cognitive skills,predicting students,intelligent tutoring systems,cognitive tutor authoring tools,current implementation,cognitive skills,machine learning,cognitive modeling
Field
DocType
Volume
Inductive logic programming,Programming by demonstration,Computer science,Cognitive tutor,Cognitive skill,Artificial intelligence,Cognitive model,Machine learning
Conference
158
ISSN
Citations 
PageRank 
0922-6389
10
0.70
References 
Authors
6
5
Name
Order
Citations
PageRank
Noboru Matsuda120023.45
William W. Cohen2101781243.74
Jonathan Sewall326424.17
Gustavo Lacerda4806.57
Kenneth R. Koedinger53551403.07