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Assessing Instructional Effectiveness and Understanding Factors that Contribute to Student Performance in an Engineering Statistics Course: An Exploratory Study

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2020 ASEE Virtual Annual Conference Content Access


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Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Industrial Engineering Division Technical Session 1

Tagged Division

Industrial Engineering

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Paper Authors


James Burns Western Michigan University Orcid 16x16

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Jim Burns, Ph.D.
Assistant Professor
Industrial and Entrepreneurial Engineering and Engineering Management Department
Bio: Jim Burns holds a Ph.D. in Industrial Engineering from Western Michigan University, and has more than 10 years industry experience in the manufacturing sector in a variety of roles including process engineering, operations management, and technical sales. His area of expertise centers on applying OR/MS and Simulation techniques to Supply Chain & Operations Management problems, and has also conducted research in the areas of Human Factors and Work Design for evaluating time and motion efficiencies of operations. Jim also holds an undergraduate IE degree and a Six Sigma Greenbelt. Prior to joining the faculty at Western Michigan, Jim was an Assistant Professor for the Industrial Engineering Technology program at Purdue Polytechnic Institute.

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Enas Aref Western Michigan University

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An Engineering professional with 10+ years of experience in manufacturing, inventory control, procurement, import and export. Earned Master’s Degree in Project Management, 2015,Keller Graduate School of Management, Wisconsin, USA.
Ph.D. Student in Industrial Engineering with research emphasis on Ergonomics and Human Factors, Western Michigan University
Instructor and Co-Instructor of several Engineering courses at the Graduate and Undergraduate levels.
Research areas: Ergonomics and Human factors, Usability Engineering, Engineering Education, Cyber-Physical Systems

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Mohammad Majd Western Michigan University

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Multi-disciplinary engineering courses present certain instructional challenges that stem directly from having students from many different programs in one classroom. Challenges include but are not limited to developing meaningful course materials that resonate across the disciplines and finding and applying the appropriate level of rigor for individual topics and for the course as a whole. These difficulties are compounded when the course involves large lecture sections taught by faculty and smaller lab-based sections that are taught by multiple teaching assistants. In such cases it can be difficult to assess the effectiveness both of instruction and student learning.

In this paper, we present the results of an effort to establish a methodology for assessing the quality of instruction and student learning in a multi-disciplinary engineering statistics course at a large, regional university. The introductory statistics course is offered through the Industrial Engineering department and serves approximately 25% of the college’s undergraduate student population. The lab-based course is comprised of two lecture sections (2 credit hours, ~100 students) and multiple lab sections (3 contact hours, ~25 students). Lecture sections are taught by faculty and focus on concepts, theory, and application. Lab sections are taught by graduate teaching assistants and focus on reinforcing lecture content and applying concepts with software. The objectives of the work are to: 1) develop a methodology to determine factors that contribute to variation in classroom performance such as students’ major and their knowledge of and sentiment toward statistics, and 2) to utilize those factors in developing a model to assess the quality of instruction and student learning across lecture and lab sessions.

Two semesters of performance data are analyzed in development of the regression-based statistical model. Factors explored during model development included major, class level, lab session characteristics (time of day, day of week), lecture section characteristics, lab instructor, measures of student engagement, and student sentiment toward statistics. The final model will serve as a basis for assessing instructional effectiveness as the course undergoes a major redesign between the Fall 2019 and Fall 2020 semesters.

Burns, J., & Aref, E., & Majd, M. (2020, June), Assessing Instructional Effectiveness and Understanding Factors that Contribute to Student Performance in an Engineering Statistics Course: An Exploratory Study Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34177

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