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Classifying the Engineering Mathematics Student: An Investigation of Trends in Learning

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2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Mathematics Division Technical Session 1

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Paper Authors


Paul L. Goethals United States Military Academy

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Dr. Paul Goethals is currently an Academy Professor in the Department of Mathematical Sciences at the United States Military Academy (USMA). He holds a B.A. in Chemistry from Indiana University, an M.S. in Mathematics from Florida State University, and a Ph.D. in Industrial Engineering from Clemson University. Dr. Goethals' research interests include experimental design, process optimization, and work with applications in national defense.

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Karoline Hood United States Military Academy

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In order to advance our educational methods and ensure that our practices result in the attainment of learning outcomes, we rely heavily on teacher feedback through course assessments. These evaluations may enable us to pinpoint teaching techniques that assure a higher level of student conceptual understanding with improved performance. Unfortunately, however, given the nature of the course assessment, this input can be inherently biased and without a true scientific foundation. A number of researchers examining the educational challenges of the assessment process have come to this conclusion. As part of the assessment process for meeting student learning outcomes, the Department of Mathematical Sciences at the United States Military Academy performed a study in 2017 with its foundation course, Engineering Mathematics – the intent was to identify alternative measures of assessment for student performance. A preliminary investigation of trends was conducted to examine the varying degrees of student confidence in acquiring conceptual knowledge of vector calculus, the application of ordinary differential equations, and partial differential equations. A pre- and post-assessment given with graded events enabled the faculty to classify the students into one of several groups and make inferences as to their ability to achieve specific objectives. This work provides the insights of that study and its potential for identifying trends in learning, specifically with regard to an engineering mathematics program.

Goethals, P. L., & Hood, K. (2018, June), Classifying the Engineering Mathematics Student: An Investigation of Trends in Learning Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30193

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