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Improving Conceptual Understanding in Probability and Statistics

Abstract

Program as well as course assessments indicate that, while technical skills are generally good, a weakness remains in students’ ability to translate information and skill sets from one Carnegie unit (one course) to another. The inability to apply probability and/or statistical concepts in different problem contexts is particularly problematic for undergraduate students in industrial engineering. Despite the completion of six credit hours in probability and statistics, undergraduate students continue to have difficulty translating that information to industrial engineering applications in quality control, stochastic models, and work measurements. Historical data using the probability and statistics concepts inventory (Figure 1 below) indicates that, for most students, true conceptual understanding of probability basics remains elusive. This problem may be exacerbated somewhat due to a disconnect between the theory covered in the mathematics department and the applications needed in the industrial engineering program. In this paper we discuss some of the initial inroads towards improving conceptual understanding in the industrial program from a historical perspective and include examples of virtual experiments, technology enabled support modules, and collaborative learning activities. In addition to use of the concepts inventory, program assessments include use of Fundamentals of Engineering (FE) exam, embedded assessments in subsequent courses, and analysis of dwell time and module usage for online support. We conclude with the current status of the initiative and a vision for a collaborative learning approach to statistical concepts through classroom inversion.

Introduction

Statistics is an important element of the curriculum for students in a variety of majors including engineering, business, and the social sciences. Increasingly, elements of data analysis and probability are being emphasized in industry in a variety of disciplines and is becoming increasingly prevalent not only in accreditation requirements but in K-12 standards1. For the industrial engineering and the engineering management disciplines, a solid foundation of statistical reasoning is critical. While Fundamentals of Engineering (FE) analysis and course assessments indicate that, in general, student technical skills are good, a weakness remains in students’ abilities to translate information and skill sets from one Carnegie unit to another. This is particularly problematic for industrial engineering students who complete 6 credits in probability and statistics, but have difficulty translating that information to industrial engineering applications in simulation, quality control, stochastic models, work measurements, and human factors. Indeed, long term tracking over 6 years utilizing the Fundamentals of Engineering and a Concepts Inventory show little, if any, gains in statistical reasoning (see Figure 1 below).

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Jensen, D., & Kellogg, S. (2010, June), Improving Conceptual Understanding In Probability And Statistics Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. 10.18260/1-2--16816