Abstract #300960

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JSM 2002 Abstract #300960
Activity Number: 319
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Quality & Productivity*
Abstract - #300960
Title: The Six Sigma Methodology Applied to Mathematics Learning Assessment
Author(s): Patricia Giurgescu*+ and Martin Kotler
Affiliation(s): Pace University and Pace University
Address: 861 Bedford Rd., Pleasantville, New York, 10570, USA
Keywords: six sigma methodology ; inverse problems ; empirical risk minimization ; Bayesian decisions
Abstract:

The paper discusses learning assessment issues from the perspective of the celebrated six sigma methodology. In following the MAIC sequence of steps (measure, analyze, improve, control) and in determining the relationships of the KPOV (key process output variables) and KPIV (key process input variables), the authors draw connections with inverse problems approaches (we measure the effects and want to determine the causes) and with learning theory measures, such as the empirical risk minimization function. The Bayesian paradigm is present in the calibration of the assessment model, based on the data collected: P(model | data) = P(data | model)*P(model)/P(data).

"Good assessment is the foundation of a successful operation"--G. Michaelson in "Sun Tzu - The Art of War for Managers"

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