Joseph E. Beck

Learn More
It has been found in recent years that many students who use intelligent tutoring systems game the system, attempting to succeed in the educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we introduce a system which gives a gaming student(More)
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 estimates, all of which make identical predictions about student performance. However, these parameter estimates make different claims, some of which are clearly incorrect, about the(More)
This paper describes an effort to model a student’s changing knowledge state during skill acquisition. Dynamic Bayes Nets (DBNs) provide a powerful way to represent and reason about uncertainty in time series data, and are therefore well-suited to model student knowledge. Many general-purpose Bayes net packages have been implemented and distributed;(More)
Student modeling is very important for ITS due to its ability to make inferences about latent student attributes. Although knowledge tracing (KT) is a well-established technique, the approach used to fit the model is still a major issue as different model-fitting approaches lead to different parameter estimates. Performance Factor Analysis, a competing(More)
ion of the learning environment Many systems attempt to provide instruction by simulating a realistic working environment in which the student can learn the task. There are many reasons for developing such systems, including the possible danger of training using the actual equipment and the lack of domain experts who can devote their expensive time to(More)
In this paper we discuss how machine learning, and specifically how naive Bayes classifiers, can be used for user modeling tasks. We argue that in general, machine learning techniques should be used to improve a user modeling system’s interactions with users. We further argue that a naive Bayes classifier is a reasonable approach to many user modeling(More)
Much of the power of a computer tutor comes from its ability to assess students. In some domains, including oral reading, assessing the proficiency of a student is a challenging task for a computer. Our approach for assessing student reading proficiency is to use data that a computer tutor collects through its interactions with a student to estimate his(More)
Student modeling has a long history in the field of intelligent educational software and is the basis for many tutorial decisions. Furthermore, the task of assessing a student’s level of knowledge is a basic building block in the educational data mining process. If we cannot estimate what students know, it is difficult to perform fine-grained analyses to(More)