Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…

Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and…

This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

This article presents a study on the variables promoting student retention in distance undergraduate courses at Federal University of Pará, aiming to help school managers minimize student attrition and maximize retention until graduation. The theoretical background is based on Rovai's Composite Model and the methodological approach is conditional…

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs…

Clinical decision-making is challenging mainly because of two factors: (1) patient conditions are often complicated with partial and changing information; (2) people have cognitive biases in their decision-making and information-seeking. Consequentially, misdiagnoses and ineffective use of resources may happen. To better support clinical…

Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task that may be dependent. This paper explores four design patterns for modeling locally dependent observations from the same task: (1) No context--Ignore dependence among observables; (2) Compensatory…

Recent developments in cognitive psychology suggest models for knowledge and learning that often fall outside the realm of standard test theory. This paper concerns probability-based inference in terms of such models. The essential idea is to define a space of "student models"--simplified characterizations of students' knowledge, skill,…