2026 ASEE Annual Conference & Exposition

A Performance Analysis of Two Engineering Majors in Probability Theory

Presented at Multidisciplinary Engineering Division (MULTI) Technical Session 6: Experiences in Multidisciplinary Robotics Education I

In a junior-level probability theory course that covers probabilistic models, discrete and continuous probability distributions, sampling distributions, and point and interval estimation, students are tasked with applying modeling techniques to address real-world engineering problems involving uncertainty. The course assumes proficiency in elementary algebra, as well as differential and integral calculus. Both Industrial Engineering (IE) and Polymer Engineering and Science (PES) students engage with the same learning modules and are evaluated through identical quizzes and exams. The aim of this study is to compare the performance of these two groups and assess their competencies in three key areas: understanding sample space and events, including Bayes' Theorem; identifying random variables and various discrete and continuous probability distributions; and solving and interpreting problems related to cumulative distribution functions and probability density functions. The results show the 2023 cohort demonstrated stronger performance in quizzes assessing a comprehensive understanding of problems related to sample spaces and counting techniques, including permutations and combinations. Additionally, the research highlights the importance of using real-world examples to bridge theoretical concepts with practical applications. This teaching approach significantly enhances students’ comprehension, particularly for those from diverse engineering disciplines.

Authors

Dr. Hsin-Li Chan Pennsylvania State University, Behrend College [biography]

Dr. Hsin-Li Chan is an Assistant Teaching Professor of Industrial Engineering at Penn State Behrend. She received the Ph.D. degree in Industrial Engineering from Clemson University and the M.S. in Applied Statistics from Syracuse University. Dr. Chan’s research interests include applied statistics, quality control in manufacturing process, and optimization.
Dr. Yuan-Han Huang Pennsylvania State University, Behrend College [biography]

Dr. Yuan-Han (Rick) Huang is the Chair of Industrial Engineering and Co-Director of the Master of Manufacturing Management (MMM) program at Penn State Behrend. He earned his B.S. in Industrial Engineering from I-Shou University, Taiwan; his M.B.A. in Industrial Management from the National Taiwan University of Science and Technology, Taiwan; and his M.S. in Industrial and Systems Engineering from the State University of New York (SUNY) at Buffalo. Dr. Huang received his Ph.D. in Industrial Engineering, with a concentration in Human Factors Engineering, from Clemson University in 2013.
Barukyah Shaparenko Pennsylvania State University, Behrend College [biography]

Dr. Barukyah Shaparenko is an Assistant Teaching Professor of Mechanical Engineering at Penn State Erie, The Behrend College. He received a B.S. in Mechanical Engineering from Penn State University in 2009 and a Ph.D. in Mechanical Engineering from the University of Pennsylvania in 2015. He has taught classes in mechanical engineering (thermodynamics, fluid mechanics, heat transfer, CFD, measurements, freshman engineering design), engineering mechanics (statics, strength of materials), computer science (MATLAB programming), biomedical engineering (measurements), and math (calculus I and II).

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