Research

Learning is important and fascinating. Besides my teaching, I'm interested in how technology can be used to help people learn. This virtual lab is a group of people and projects aimed at helping people learn more effectively and (sometimes) with more fun.

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Computer games for educational or training purposes have been around for many years. Yet, we understand little about what makes people want to play them, or how they learn from them. This project takes a multidisciplinary look at these questions. In one project, we are investigating the effectiveness of Advisor to the King, a game we designed to help people learn to analyze arguments by giving them a job in the King's bureaucracy.

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Students are smart: they only work as hard as they have to. But governmental standards are raising the bar. New STEM-related standards require students to identify arguments in texts that they read, to integrate ideas across texts, and to also interpret information from graphs and charts.

As part of the READI project under the Institute for Educational Science's Reading for Understanding program, we have been developing techniques for measuring student reading skills by automatically analyzing the explanations that they write.

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Neuromorphic processing aims to model the incredibly complex interactions of neural systems at a very detailed level. Bolstered by hardware advances including the SpiNNaker board and TrueNorth, and research projects like the Human Brain Project, this area is on the cutting edge of computing research for modeling human (and other intelligent) behavior.

Structured Latent Semantic Analysis (SLSA) is a response to the failures of traditional syntactic natural language understanding (NLU) and of statistical, corpus-based semantic NLU. SLSA is a hybrid NLU system which combines shallow parsing with vector-based semantics. The semantics are taken from LSA which uses word occurrence information in a large corpus to create vectors in a high dimensional space for each word and each document in the corpus. The similarity between words and documents is measured by taking the cosine of the vectors.

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Peer tutors don't bring special techniques to the table. They don't even have deep understanding of their students' answers. Yet they are very effective at increasing student knowledge. The AutoTutor system, developed at the University of Memphis with Art Graesser, was the first of a new breed of tutoring systems that interact as peer tutors do.

Research Methods Tutor took the approach of AutoTutor, and brought it to a much more difficult domain -- Research Methods in Psychology -- and tested it in the context of a full quarter-long course, where it produced significant gains in student learning on top of regular classroom instruction.

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