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Multivariate Analysis Of Student Performance In Large Engineering Economy Classes

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1997 Annual Conference


Milwaukee, Wisconsin

Publication Date

June 15, 1997

Start Date

June 15, 1997

End Date

June 18, 1997



Page Count


Page Numbers

2.302.1 - 2.302.9



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Paper Authors

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Shamil F. Daghestani

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William G. Sullivan

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 1239

Multivariate Analysis of Student Performance in Large Engineering Economy Classes

William G. Sullivan, Shamil F. Daghestani Department of Industrial and Systems Engineering Virginia Tech Blacksburg, VA 24061-0118


Based on multivariate data collected over four years, linear regression equations are developed and used to assess student learning in large sections of engineering economy taught at Virginia Tech. In each year (1993, 1994, 1995 and 1996), more than 350 students in the fall semester voluntarily participated in this research. This paper presents the principal findings of the study and demonstrates the use of multivariate linear regression for evaluating student performance (learning) in engineering economy.


The aim of this paper is to describe statistical results from the application of multiple linear regression to student records and performance data collected during the fall semesters of 1993, 1994, 1995 and 1996 at Virginia Tech. Over 1,400 students in large sections (~200 students each) of engineering economy (ISE 2014) voluntarily participated in this research to develop linear regression equations for predicting learning in the course. These predictions can then be used to offer individual tutoring to students who are found to be “at risk.”

A valid surrogate measure of learning is assumed to be the final weighted score in this 2-hour sophomore level course. In this study the final weighted score is the dependent variable, which is determined as follows.

Final weighted score = 0.20 (homework/quiz score) + 0.25 (test 1 score) + 0.25 (test 2 score) + 0.30 (final examination score)

The independent variables include gender (GNDR), academic level (LVL), grade point average (QCA), SAT math score (MATH), SAT verbal score (VERB) and high school class standing (HS Rank %). Further delineations regarding particular engineering major, morning (AM) versus afternoon (PM) section and instructor were also made in the student records database.

Daghestani, S. F., & Sullivan, W. G. (1997, June), Multivariate Analysis Of Student Performance In Large Engineering Economy Classes Paper presented at 1997 Annual Conference, Milwaukee, Wisconsin. 10.18260/1-2--6701

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