WPI Worcester Polytechnic Institute

Computer Science Department
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AIRG Topics - Spring 2011


Our group meets on Thursdays at 11:00 a.m., FL 246.


Jan 27
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Feb 3
Dovan Rai
"Causal Modeling of user data from Monkey's Revenge: a math tutor with game-like elements"
    We have used causal modeling to understand data from a math learning environment with game-like elements, Monkey's Revenge. We collected data from 297 middle school students in various categories such as their attitude, enjoyment, performance and learning. Although the data are observational, we explored causal modeling approaches and have obtained some interesting results that not only confirmed our prior hypotheses about data but also generated interesting hypotheses that would not have been possible to induce from statistical methods alone. Based on our comparison of this relatively new approach with traditional statistical tools like correlation and multiple regression, we found that causal modeling infers direct, indirect and spurious associations between variables enabling us to understand interrelationships within the overall data.

Feb 10
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Feb 17
Academic Advising Appointment Day

Feb 24
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March 3
Peter Swire
"A First Look At Dempster-Shafer Theory"
    There are times when we must reason under uncertainty: sensors pick up noise, mistakes get recorded, and experts disagree. How can you draw conclusions from data that seems to contradict itself? Dempster-Shafer theory is a collection of mathematical tools for reasoning over evidence that cannot be fully trusted. This week's AIRG will be a brief and gentle introduction to it.

Mar 10

Mar 17

Mar 24
Yue Gong
"Looking beyond transfer models: finding other sources of power for student models"
    Student modeling plays an important role in educational research. Many techniques have been developed focusing on accurately estimating student performances. In this paper, using Performance Factors Analysis as our framework, we examine what components of the model enable us to better predict, and consequently understand, student performance. Using transfer models to predict is very common across different student modeling techniques, as student proficiencies on those required skills are believed, to a large degree, to determine student performance. However, we found that problem difficulty is an even more important predictor than student knowledge of the required skills. In addition, we found using student proficiencies across all skills works better than just using those skills thought relevant by the transfer model. We tested our proposed models based on two transfer models with fine and coarse grain sizes; the results suggest that the improvement is not simply an illusion due to possible mistakes in associating skills with problems.

Mar 31
Jason Wilson (BAE systems)
"Hybrid Qualitative Simulation of Military Operations"
    Our goal is to enable military planners to rapidly critique alternative battle plans by simulating multiple outcomes of adversarial plans. We describe a novel simulator, SimPath, that combines qualitative reasoning, a geographic information system (GIS), and targeted probabilistic calculations to envision how adversarial battle plans can play out. We outline the problem and describe the overall operation of the simulator. We then explain how qualitative process theory is extended with actions to model military tasks, how envisioning is factored to reduce combinatorial explosions, and how probabilities are computed for transitions and used to filter possibilities. Empirical results, including an experiment conducted by an independent evaluator, are summarized. The results show that it is possible to identify dozens of possible outcomes on each of 9 combinations of adversarial plans (COAs) in under two minutes. We close with a discussion of future work.

Apr 07
Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan & Gabor N. Sarkozy.
"The Utility of Clustering in Prediction Tasks"
    In this work we explore the utility of clustering data to aid in prediction tasks and propose a simple yet very effective bagging meta-algorithm for prediction. In hindsight the work can be thought of something like an adaptive mixture of experts (Hinton, Jordon 1991) that uses clustering to bootstrap. But unlike in other bagging methods, which select a random subset to bootstrap, this method has a specific expert (a predictor) for a specific cluster of the data. By varying the granularity of the clustering we are able to obtain a set of diverse predictions on the test set that are then combined together to get a single much stronger prediction. The single most important aspect of the method is clustering. Methods such as k-means are based on estimating explicit models of the data such as a mixture of spherical gaussians. They perform badly when these assumptions are not met, which is usually the case. When it comes to more complex and unknown shaped data distributions, spectral clustering methods are known to return much stronger results. We also compare results on a few benchmark datasets using both k-means and spectral clustering.

Apr 14

Apr 22
Undergraduate Project Presentation Day

Apr 28

May 2
Spring semester graduate classes end


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AIRG Coordinator / Wed Apr 6 16:07:18 EDT 2011