Test Size Reduction via Sparse Factor Analysis
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United States of America Patent
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Jan 1, 2015
app pub date -
Jun 27, 2014
filing date -
Jun 28, 2013
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A database of questions is designed to test understanding of a set of concepts. A subset of the questions is selected for administering to one or more learners in a test. One desires for the subset to be small, to minimize testing workload for the learners and grading workload for instructors. However, to preserve the ability to accurately estimate learners' knowledge of the concepts, the questions of the subset should be appropriately chosen and not too small in number. We propose among other things a non-adaptive algorithm and an adaptive algorithm for test size reduction (TeSR) using an extended version of the Sparse Factor Analysis (SPARFA) framework. The SPARFA framework is a framework for modeling learner responses to questions. Our new TeSR algorithms find fast approximate solutions to a combinatorial optimization problem that involves minimizing the uncertainly in assessing a learner's knowledge of the concepts.
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