Designing the effective OR/MS classroom

Matthew J. Drake

Any time that I design a course that is either completely new or new for me, I first try to understand the course’s position in the overall curriculum. This helps me to establish the appropriate focus and objectives for the course. It is also important to understand the students’ objectives in the course and the program as a whole. I would teach a course for Ph.D. students significantly differently than I would teach one for undergraduates or MBA students.

I teach in both an undergraduate and an MBA program at a medium-sized university with class sizes of approximately 25-35 students. The courses I teach are primarily quantitative modeling courses for supply chain management majors. While I want them to understand how to develop and solve the models and tools that I present, my first goal is that they understand when to apply each model. That is always the first step in any form of analysis. Considering a decision scenario and knowing which type of model or form of analysis to apply is not necessarily innate. For analytical tools and techniques to be useful on the job, students must be able to recognize the situations to which they apply.

I also want the students in my courses to gain the confidence in their analytical abilities as well as their mastery of available software tools. Most of us have heard the common refrain that a student “has never been good at math.” While there certainly is a spectrum of mathematical competence that students possess, I have found that the vast majority of my students possess the analytical capabilities that my courses require. Some just need to gain the confidence in these abilities.

But no matter how capable someone is, including professors, we all reach the upper limit of our analytical abilities. I tell my students that their managers will not tolerate responses such as “I don’t know how to do that,” or “That’s too difficult for me.” They want answers and recommendations. This is where technology comes in. For example, when we are determining the optimal shipment size in a transportation mode selection exercise, I show the students the analytical way to minimize the total cost function using calculus. Whenever I start talking about derivatives, utter panic starts to enter some of their faces. To calm their fears, I ask them what they would do if they did not remember how to solve for the optimal size using calculus. They usually do not have an answer for that question, so I tell them that we can use the technology that is available to us (in this case, Excel).

In a manner that would probably horrify their high school calculus teachers, I show the students how to solve for the optimal shipment size using brute force, otherwise known as complete enumeration. While that method would be untenable to implement by hand, it can be done in a matter of seconds in Excel no matter how large the domain of values may be. The key here is that students gain the confidence that they can use the tools available to them to perform the analysis rather than throwing their hands up in the air and surrendering.

As far as class instruction goes, my courses are largely still lecture-based on the surface when I present new material. However, I do try to turn the class into active problem-solving sessions wherever possible to keep the students engaged. Whenever I present example problems to the students, I sometimes get feedback that I go too quickly for some of them to keep up with me. This is especially true when I am solving the problem on a spreadsheet. This is a dynamic that I consistently struggle with because going slower would mean sacrificing some other material in the course. As a compromise, I always post the Excel files that I build during class afterwards on our course management system website so that students can download the files and compare their notes to mine.

I also try to use at least a few cases in each course. In my experience, students enjoy and appreciate considering the real-world decision scenarios that cases offer. This is especially true for math and engineering students because they often do not get this experience in their other courses.

In conclusion, I have several additional thoughts and recommendations for designing effective courses:

  • Be understanding and flexible with deadlines and attendance, especially with part-time students. We all might like to think that our courses should be the most important considerations in students’ lives, but they have many other concerns and responsibilities. I always accept late assignments with a point deduction to be fair to other students.
  • Don’t be afraid to say, “I don’t know.” No one person can be an expert in everything. Professors often need to teach courses containing material on which they are not experts. If students ask questions to which I do not know the answer, I tell them that I do not have an answer off the top of my head. I then try to follow up during a later class after I have had the chance to research the issue. Students seem to appreciate this honesty.
  • Students appreciate rapid feedback to their questions and to their work on assignments. I try to return all graded assignments within a week or 10 days at the most, and I reply to emails as soon as I can.

Matt Drake ( is the Harry W. Witt Faculty Fellow in Supply Chain Management and associate professor in the Palumbo-Donahue School of Business at Duquesne University in Pittsburgh. This article is based on a presentation Drake gave at a recent Teaching Effectiveness Colloquium in Philadelphia that was organized by Eric Huggins from Fort Lewis College.