Building an Inter-disciplinary Research Community to Protype Computationally-intensive Analysis of Large Scale Educational Datasets

New York
Causal
Research Methods
Other
Higher Education
Principal Investigator: 
Paul Attewell
Project Overview
Background & Purpose: 

Our goal is to build an interdisciplinary community of researchers interested in using data mining techniques to analyze large scale educational datasets. Our activities will include (1) a seminar and workshop series on various data mining techniques given by visiting experts (2) a workshop series on the development of new analytical software tools (3) a doctoral level course on data mining taught by a computer scientist and a sociologist; (4) prototyping some new data mining methods. These activities will draw doctoral students and faculty from computer science and the social sciences, from New York metropolitan area institutions with some participants coming from the rest of the US and from overseas.

Setting: 

These activities will take place at the Graduate Center of the City University of New York.

Research Design: 

We are using a variety of Data Mining techniques, including neural network methods, data partitioning and decision trees, cluster analysis, and classifiers to analyze NCES and other large scale datasets. Causal evidence will be generated using quasi-experimental methods and statistical modeling.

Findings: 

Findings will be posted as they become available.

Other Products: 

Products include a book on these techniques, software prototypes of certain applications, and online presentations and papers.

On the Web: 
http://datamining.ws.gc.cuny.edu