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.

Contextual slip and prediction of student performance after use of an intelligent tutor

Abstract: Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests outside of the system Recent work has suggested that contextual estimation of student guessing and slipping leads to better prediction within the tutoring software (Baker, Corbett, & Aleven, 2008a, 2008b) However, it is not yet clear whether this new variant on knowledge tracing is effective at predicting the latent student knowledge that leads to successful post-test performance In this paper, we compare the Contextual-Guess-and-Slip variant on Bayesian Knowledge Tracing to classical four-parameter Bayesian Knowledge Tracing and the Individual Difference Weights variant of Bayesian Knowledge Tracing (Corbett & Anderson, 1995), investigating how well each model variant predicts post-test performance We also test other ways to utilize contextual estimation of slipping within the tutor in post-test prediction, and discuss hypotheses for why slipping during tutor use is a significant predictor of post-test performance, even after Bayesian Knowledge Tracing estimates are controlled for.

Itoh-Tsujii Inversion in Standard Basis and Its Application in Cryptography and Codes

Abstract: This contribution is concerned with a generalization of Itoh and Tsujii's algorithm for inversion in extension fields GF(q^m). Unlike the original algorithm, the method introduced here uses a standard (or polynomial) basis representation. The inversion method is generalized for standard basis representation and relevant complexity expressions are established, consisting of the number of extension field multiplications and exponentiations. As the main contribution, for three important classes of fields we show that the Frobenius map can be explored to perform the exponentiations required for the inversion algorithm efficiently. As an important consequence, Itoh and Tsujii's inversion method shows almost the same practical complexity for standard basis as for normal basis representation for the field classes considered.

CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment

Abstract: Time-of-arrival (TOA)-based indoor geolocation suffer from huge distance measurement error caused by multipath and nonline-of-sight (NLOS) conditions. In this paper, we presented a new distance mitigation algorithm based on channel classification and Kalman filter to enhanced TOA performance in multipath and NLOS indoor extreme environment. This algorithm could significantly reduce the ranging error caused by the extreme channel condition in indoor area. We compared the performance of our algorithm with the traditional TOA distance mitigation algorithms, such as Kalman filter, biased Kalman filter, binary hypothesis testing, and ANN, using a commercially available TOA-based geolocation system in typical indoor and underground environments. Results show the performance of our algorithm is much superior to the others.