Building, operating a hybrid course.(ISSUES IN EDUCATION)

This month's column concludes a short series on the notion of a "hybrid" course. The first installment, "Your Next Teaching Vehicle may be a Hybrid" (OR/MS Today, June, 2007) discussed the pressures that OR/MS courses face in the MBA Core and the increasing need for a range of allied quantitative and analytic passengers to ride together in a single required course. One approach to doing more with less is a hybrid course, a deliberate fusion that "derives its power from the ideas and techniques of all the constituent courses," but with the ability to "switch back and forth seamlessly."

Next, in "Designing & Selling a Hybrid Course" (OR/MS Today, August, 2007), we addressed the main conceptual challenge of a hybrid: coherence. A "marriage of convenience" model, in which the only common thread among a grab-bag of tools is quantitative-ness, runs the risk of being seen as the MBA equivalent of wood shop: intellectually subservient to other courses, and peripheral to serious management practice. On the other hand, the "jackalope" model, in which two unrelated disciplines are tightly stitched together, but into a freakish whole, is equally unconvincing and perhaps even more awkward to staff.

We examined one hybridization, "Decision & Information Analysis," which we have used at Emory for more than 10 years. Here a central organizing question, "What should we do?" is decomposed into "Given what we believe, what should we do?" and "Given what we observe, what should we believe?" The first sub-question motivates not only decision analysis per se, but also a range of our OR/MS tools, which support rational action given a set of assumptions. The second motivates the description, inference and prediction topics of statistics, but as an integral component of a broader and more fundamental decision making process. Everything fits, and everything fits together.

Moving now from description to prescription, in the spirit of that course, we're faced with another question: "What should other schools do?"

Our DIA model has worked well given our own available resources, political terrain and elective ecosystem. Other schools might not get the same mileage from the same formulation. Nonetheless, our experience suggests some general lessons:

Build your chassis from local materials. Although our success has been based on decision and data analysis, there are other effective hybrids out there, built on modeling, spreadsheeting, problem-solving, decision support systems, etc. Consider your own faculty. If your research streams tend toward modeling, exploit that. If it's more empirical and data-driven, use that. Does your department or school have strong faculty in IS/IT or some spreadsheet ninjas? Invite them on board.

Keep weight and complexity down. We can't cover everything in the core, and we shouldn't try. Our field offers so much good material, and we're tempted to share our excitement--about all of it--the first chance we get. This urge can be heightened by a fear that for most students the core will also be the last chance. If we're sharing a ...