Worcester Polytechnic Institute

About: Worcester Polytechnic Institute is a education organization based out in Worcester, Massachusetts, United States. It is known for research contribution in the topics: Computer science & Population. The organization has 6270 authors who have published 12704 publications receiving 332081 citations. The organization is also known as: WPI.

Wheel-Spinning: Students Who Fail to Master a Skill

Abstract: The concept of mastery learning is powerful: rather than a fixed number of practices, students continue to practice a skill until they have mastered it. However, an implicit assumption in this formulation is that students are capable of mastering the skill. Such an assumption is crucial in computer tutors, as their repertoire of teaching actions may not be as effective as commonly believed. What if a student lacks sufficient knowledge to solve problems involving the skill, and the computer tutor is not capable of providing sufficient instruction? This paper introduces the concept of “wheel-spinning;” that is, students who do not succeed in mastering a skill in a timely manner. We show that if a student does not master a skill in ASSISTments or the Cognitive Tutor quickly, the student is likely to struggle and will probably never master the skill. We discuss connections between such lack of learning and negative student behaviors such as gaming and disengagement, and discuss alterations to ITS design to overcome this issue.

Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures

Abstract: 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 approach, predicts student performance based on the item difficulty and student historical performances. In this study, we compared these two models in terms of their predictive accuracy and parameter plausibility. For the knowledge tracing model, we also examined different model fitting algorithms: Expectation Maximization (EM) and Brute Force (BF). Our results showed that KT+EM is better than KT+BF and comparable with PFA in predictive accuracy. We also examined whether the models' estimated parameter values were plausible. We found that by tweaking PFA, we were able to obtain more plausible parameters than with KT.

Modeling the Effect of Human Body on TOA Based Indoor Human Tracking

Abstract: In time-of-arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line of sight (NLOS) scenario and result in significant ranging error. However, the previous studies on the behavior of indoor TOA ranging did not take the effects of human body into account. In this paper, measurement of TOA ranging error has been conducted in a typical indoor environment and sources of inaccuracy in TOA-based indoor localization have been analyzed. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of transmitter–receiver, signal to noise ratio and bandwidth of the system, which clearly shows the effects of human body on TOA ranging.

Monte Carlo EM for missing covariates in parametric regression models.

Abstract: We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association85, 765–769). We extend this method t o continuous or mixed categorical and continuous covariates, and for arbitrary parametric regression models, by adapting a Monte Carlo version of the EM algorithm as discussed by Wei and Tanner (1990, Journal of the American Statistical Association85, 699–704). In addition, we discuss the Gibbs sampler for sampling from the conditional distribution of the missing covariates given the observed data and show that the appropriate complete conditionals are log‐concave. The log‐concavity property of the conditional distributions will facilitate a straightforward implementation of the Gibbs sampler via the adaptive rejection algorithm of Gilks and Wild (1992, Applied Statistics41, 337–348). We assume the model for the response given the covariates is an arbitrary parametric regression model, such as a generalized linear model, a parametric survival model, or a nonlinear model. We model the marginal distribution of the covariates as a product of one‐dimensional conditional distributions. This allows us a great deal of flexibility in modeling the distribution of the covariates and reduces the number of nuisance parameters that are introduced in the E‐step. We present examples involving both simulated and real data.