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.

The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

Abstract: The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.

One-step reverse precipitation synthesis of water-dispersible superparamagnetic magnetite nanoparticles

Abstract: Hydrophilic functionalization of nanoparticle surface is essential for their biomedical applications. Herein, we report a facile one-step reverse precipitation method to synthesize water-dispersible, biocompatible, and carboxylate-functionalized superparamagnetic magnetite (Fe3O4) nanoparticles with the help of biocompatible sodium citrate salt. Transmission electron microscope (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), zeta potential measurement, dynamic light scattering (DLS), and superconducting quantum interference device (SQUID) were used to characterize the as-prepared magnetite nanoparticles. The size of the as-prepared magnetite nanoparticles was tuned from 27 ± 3.8 to 4.8 ± 1.9 nm by changing the sodium citrate concentration from 25 to 125 mM. The sodium citrate concentration also influenced the water-dispersible stability of the as-prepared magnetite nanoparticles, which was due to the electrostatic repulsion.

Public key cryptography in sensor networks—revisited

Abstract: The common perception of public key cryptography is that it is complex, slow and power hungry, and as such not at all suitable for use in ultra-low power environments like wireless sensor networks. It is therefore common practice to emulate the asymmetry of traditional public key based cryptographic services through a set of protocols [1] using symmetric key based message authentication codes (MACs). Although the low computational complexity of MACs is advantageous, the protocol layer requires time synchronization between devices on the network and a significant amount of overhead for communication and temporary storage. The requirement for a general purpose CPU to implement these protocols as well as their complexity makes them prone to vulnerabilities and practically eliminates all the advantages of using symmetric key techniques in the first place. In this paper we challenge the basic assumptions about public key cryptography in sensor networks which are based on a traditional software based approach. We propose a custom hardware assisted approach for which we claim that it makes public key cryptography feasible in such environments, provided we use the right selection of algorithms and associated parameters, careful optimization, and low-power design techniques. In order to validate our claim we present proof of concept implementations of two different algorithms—Rabin’s Scheme and NtruEncrypt—and analyze their architecture and performance according to various established metrics like power consumption, area, delay, throughput, level of security and energy per bit. Our implementation of NtruEncrypt in ASIC standard cell logic uses no more than 3,000 gates with an average power consumption of less than 20 μW. We envision that our public key core would be embedded into a light-weight sensor node architecture.

Modeling individualization in a bayesian networks implementation of knowledge tracing

Abstract: The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade Surprisingly, models currently in use do not allow for individual learning rates nor individualized estimates of student initial knowledge Corbett and Anderson, in their original articles, were interested in trying to add individualization to their model which they accomplished but with mixed results Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously, in a single step With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter We explore three difference strategies for setting the initial individualized knowledge parameters and report that the best strategy is one in which information from multiple skills is used to inform each student's prior Using this strategy we achieved lower prediction error in 33 of the 42 problem sets evaluated The implication of this work is the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill Adaptation of instruction based on individualized knowledge and learning speed is discussed as well as open research questions facing those that wish to exploit student and skill information in their user models.