Predictive accuracy and parameter plausibility are two major desired aspects for a student modeling approach. Knowledge tracing, the most commonly used approach, suffers from local...
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better u...
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
In this paper we show how model identifiability is an issue for student modeling: observed student performance corresponds to an infinite family of possible model parameter estimat...
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...