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Abstract Title: Quantitative Modeling of Changes in Student Understanding
Abstract: We employ four distinct statistical methods to model day-to-day changes in student responses to simple conceptual questions.  These questions were administered several times per week to separate, randomly selected groups of introductory physics students.  The four methods applied to parameter estimation were least-squares estimation, maximum-likelihood estimation, autoregressive integrated moving average series modeling (ARIMA), and Bayesian estimation.  Each method is able to estimate the impacts of specific learning interventions such as lectures, homework assignments, and exams.  We present brief introductions to each of these four methods, as well as a comparison of their results to demonstrate the advantages and disadvantages for each.
Abstract Type: Contributed Poster

Author/Organizer Information

Primary Contact: Aaron R. Warren
Purdue University - North Central
Department of Mathematics, Physics, and Statistics
1401 S. US-421
Westville, IN 46391
Phone: 219-785-5659
Co-Author(s)
and Co-Presenter(s)
Thomas M. Scaife
Department of Chemistry and Engineering Physics
University of Wisconsin - Platteville

Andrew F. Heckler
Department of Physics
The Ohio State University