Paper Info
Title
Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System
Abstract
Student modeling and cognitive diagnostic assessment are important issues that need to be addressed for the development and successful application of intelligent tutoring systems (ITS). ITS needs the construction of complex models to represent the skills that students are using and their knowledge states, and practitioners want cognitively diagnostic information at a finer grained level. Traditionally, most assessments treat all questions on the test as sampling a single underlying knowledge component. Can we have our cake and eat it, too? That is, can we have a good overall prediction of a high stakes test, while at the same time be able to tell teachers meaningful information about fine-grained knowledge components? In this paper, we introduce an online intelligent tutoring system that has been widely used. We then present some encouraging results about a fine-grained skill model with the system that is able to predict state test scores. This model allows the system track about 106 knowledge components for eighth grade math. In total, 921 eighth grade students were involved in the study. We show that our fine-grained model could improve prediction compared to other coarser grained models and an IRT-based model. We conclude that this intelligent tutoring system can be a good predictor of performance.
Year
DOI
Venue
2009
10.1109/TLT.2009.17
TLT
Keywords
Field
DocType
complex model,fine-grained knowledge component,fine-grained model,online intelligent tutoring system,irt-based model,coarser grained model,analyze different grain-sized skill,knowledge component,intelligent tutoring system,knowledge state,fine-grained skill model,artificial intelligence,grain size,statistical analysis,probability density function,mixed effects model,computational modeling,data mining
Intelligent tutoring system,Computer science,Mixed model,Sampling (statistics),Artificial intelligence,Cognition,Multimedia,Statistical analysis
Journal
Volume
Issue
ISSN
2
2
1939-1382
Citations 
PageRank 
References 
7
0.81
10
Authors
4
Name
Order
Citations
PageRank
Mingyu Feng120027.57
Neil T. Heffernan21087135.49
Cristina Heffernan31159.83
Murali Mani453941.26