RA

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WPI is currently creating a PhD program in Learning Sciences and Technologies and are particularly interested in students with a strong background in Statistics that are interested in applying that knowledge to further the study and development of intelligent tutoring systems that actually track student knowledge.

Contents

General Overview

Graduate Research Assistantships are available for students interested in working on Intelligent Tutoring Systems Research and examining how they are used in real school settings. If you have a background in Computer Science, Cognitive Psychology or Statistics this position is for YOU!


Research will be done using the ASSISTment System. The research goal of the ASSISTment Project is to provide a way to give web-based intelligent tutoring to middle and high school students in mathematics and science and is a joint collaboration between Worcester Polytechnic Institute (WPI) and Carnegie Mellon University (CMU). The ASSISTment System is currently being used by over 3,000 7th and 8th graders in the Worcester Public Schools system as well as schools near Worcester and Pittsburgh. WPI and CMU provide this service free while conducting interesting research on what makes for effective intelligent tutoring. For more information see http://www.wpi.edu/research/discovery/ and on the left scroll down to Intelligent Tutoring Systems that Teach and Assess.

See also A Day in the Life of a GK12 Fellow and Video Introduction

Types of Research Done

The types of research we do includes:

  • Educational data mining: We examine millions of students’ actions for patterns. Educational data mining is to education what bio-informatics is to biology. See EducationalDatamining.org for more information. For sample publications on educational data mining with the ASSISTment System see Zach Pardos’ use of Bayesian networks to see if fine-grained skill models can more accurately predict students’ state test performance. Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan C. Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. Workshop in Educational Data Mining held at the 8th International Conference on Intelligent Tutoring Systems. 2006. (pdf)
  • Creating innovative education applications: We are building systems to email parents and track students’ effort levels. See Jason Walonoski’s paper: Walonoski, J., Heffernan, N.T. Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In Ikeda, Ashley & Chan (Eds.). Proceedings of the 8th International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. pp. 382-391. 2006. (pdf)
  • Cognitive research to determine the most effective ways for tutoring students. For example, Leena Razzaq’s work has shown that we can maximize students’ learning by first recognizing their level of background knowledge; higher performing students learn more from explanations while students with less math background learn more from our intensive tutoring techniques. More detail can be found in her paper: Razzaq, L., Heffernan, N. T., Lindeman, R. W. What level of tutor feedback is best? In Luckin & Koedinger (Eds.) Proceedings of the 13th Conference on Artificial Intelligence in Education. IOS Press. 2007. (pdf)
  • Creating new types of tutors that are smarter and track students' knowledge of the scientific method.
  • Scaling up intelligent tutoring systems to work for millions of students and teachers.

Requirements

All applicants should have a background in Computer Science, Cognitive Psychology or Statistics. The grant requires graduate student fellows to work 15 hours a week helping the school, 10 of which must be at a school. Sometimes you will share your experiences of being a scientist with middle and high school students and inspire them to pursue careers in math and science. Sometimes you will be working with students in the computer lab and then designing new activities to run in the ASSISTment System. Sometimes you will be testing new features that you built for the ASSISTment System with your middle schools students and teachers. The goal is to have a tight relationship between your research goals and your classroom activities. The 5 hours a week that is not in school, will be spent supervising a team of undergraduates who will be building content for ASSISTments. A typical research ASSISTantship (RA) will, with the advice of one of the faculty, come up with new experiments or features that you think will be of research interest, and then implement new ideas in code.

Affiliated Faculty

Affiliated Faculty at Worcester Polytechnic Institute:

  • Neil Heffernan: Intelligent Tutoring Systems (Director of PIMSE)
  • Joseph Beck: Educational Data Mining

Faculty at Carnegie Mellon University associated with the ASSISTment Project:

To Apply

  1. Apply to the computer science department's graduate program and mention in your cover letter and research statement your interest in intelligent tutoring systems. WPI-CS.
  1. You might also want to apply to the PIMSE project. This National Science Foundation (NSF) GK12 grant funds five Computer Science graduate students for up to five years with stipends of $30,000 per year and a full tuition waiver. For the GK12 Application GK12-Graduate-Student-Application. Unfortunately, it only for US residents.
  1. If you not applying for the PIMSE program, please inform Prof Heffernan's team of your interest by email gk12@cs.wpi.edu. Send a short description about why this RA is a good match for your background and interests. To make the application process easier, include copies of whatever materials you sent (or will send) to apply for a graduate degree in Computer Science at WPI.

We will use a rolling admission program and accept students as quickly as possible.

Principal Investigator

Neil Heffernan

Retrieved from "http://beta.assistment.org/wiki/RA"
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