(By Michael Schrage)
“Understanding probabilities, statistics and analytics is increasingly vital to identifying and effectively managing high performing talent. As Moneyball and the rise of quants in professional sports worldwide attest, the ability to relentlessly improve the quality and specificity of performance analytics is key to success. Luck matters but so do the data-driven odds. This class requires small teams of students to compete against each other in at least two data-rich team sports. The student teams, with budgets and other constraints, have to assemble and field the best-performing teams they can and justify their investments and trade. Their grades are, indeed, dependent on their teams’ “on-the-virtual field/court” performance.“
School is back amid growing controversy and cynicism. The quality, validity and economic value of college degrees and MBAs have rarely been under such sustained assault. Employability of graduates has never been so dismal. Machines are clearly getting smarter at many of the things people traditionally do on the job. That means people need to become non-traditionally smarter at things machines are not quite yet ready to think about or do. And that means educators worldwide must revisit how they want to make their most important product — their students — more valuable.
Were I advising aspiring top-tier universities — or their students on what they should expect from their high-priced education — the following classes would represent excellent starting points for fundamental curricular reform.
Multimedia Editing. Increasingly, knowledge workers won’t simply be creating or generating information but assembling, reorganizing and prioritizing information from others. In other words, they’ll be editing. They will need to extract, abstract, synthesize and linearly present other people’s — and machine’s — work. Much of this information will be incomplete or inchoate. (Just ask my own editor.) The ability to write a sentence or video a sequence is not the same as editing them. The ability to immerse oneself in terabytes of data, identify (individually or collaboratively) what’s most important and restructure it in an accessible, meaningful and usable form for a variety of audiences will increasingly be an essential skill. What enterprise isn’t interested in graduates capable of transforming a petabyte of information into a slick 12-minute interactive multimedia presentation?
Scenarios. In addition to knowing how to create a compelling narrative out of reams of data, there will be a premium paid to those who can paint vivid pictures of possible tomorrows. Scenario planning is as essential for strategy formulation as it is for the design of next generation technologies and industries. Thinking in terms of scenarios forces people to rigorously examine fundamental assumptions and unexpected risks. Scenarios demand expository and analytic, as well as literary, skills. What serious employer doesn’t want to hire someone who can envision and articulate scenarios describing the future(s) of the enterprise?
Fantasy Sports Competition. Understanding probabilities, statistics and analytics is increasingly vital to identifying and effectively managing high performing talent. As Moneyball and the rise of quants in professional sports worldwide attest, the ability to relentlessly improve the quality and specificity of performance analytics is key to success. Luck matters but so do the data-driven odds. This class requires small teams of students to compete against each other in at least two data-rich team sports. The student teams, with budgets and other constraints, have to assemble and field the best-performing teams they can and justify their investments and trade. Their grades are, indeed, dependent on their teams’ “on-the-virtual field/court” performance. Credit given for the development of novel/innovative metrics for performance assessment (for example, attendance figures as a “team economics” variable). The goal is not creating teams or leagues of aspiring Nate Silvers but to assure that students come away with the statistical savvy not to be probabilistically buffaloed by Nate Silver wannabees.
Reverse Engineering. This class looks at what makes experiments, inventions and artifacts tick and then takes them apart and rebuilds them. In other words, this is a hands-on class where students gain knowledge and skill by seeking to replicate and recreate things that work. What makes Amazon’s web page work? What are the ingredients of a touchscreen? How does a mobile phone cam take a picture versus a video? What makes a prosthetic limb responsive? What makes a toaster toast? This is a class that puts things together by first deconstructing them. The goal is giving students a vocabulary and capability for interactively understanding the links between technology design and construction. Fixing, maintaining and/or repairing technologies is not the purpose; empowering students to identify and understand the fundamental physics, materials science and design technologies that combine into valuable outcomes is. Appreciating the essence of technology and the technology of essence is key. Grasping how difficult, challenging and important design integration can be — and why it must be managed well — is an essential takeaway.
Comparative Coding. Another blog on this site asks, “Should MBAs Learn to Code?” Alas, that’s exactly the wrong question. The better question is: What aspects of coding should MBAs (and university students) learn? When the world is filled with cascading style sheets, XML, Erlang, Python, Ruby-on-Rails, Objective C, C++, Java, SQL, etc, “coding” becomes a misnomer. The pedagogical challenge becomes what are the most important things people need to understand about the grammar, semantics and culture of computer languages? If fluency isn’t possible or practical, what is? Does value primarily come from the ability to code? Or from an ability to read, follow or grasp a program’s limits and strengths? Designing a coherent course that gives people who use software genuine and actionable insight into the languages underlying the apps and services they use remains one of the great educational challenges of the digital era. Perhaps “comparative coding” will evolve into a class not unlike “editing,” where the key to cognitive and conceptual success is the ability to identify the code that’s most important and effectively tweak it. A classroom experience that gives students confidence that they know why — and how — their software works (and evolves) as it does will be of inestimable value to the students themselves and the employers who hire them.
Cooking Science & Technology. Another hands-on course integrating fundamental scientific principles with real-world knowledge challenges students to transform their understanding of food. Everybody eats. But too many people think that food comes from supermarkets and that cooking simply means heating up food according to a recipe. Understanding the chemical and material properties of ingredients is, indeed, a science. Appreciating the role of technologies in every stage of the cultivation, preparation, presentation and preservation of those ingredients requires a genuine grasp of high-tech engineering. The role of local sourcing and global supply chains are integral to knowing why, how and how much food ends up on people’s plates. Controversial GMO foods challenge notions of what’s natural while moleculer gastronomy innovations transcend expectations about food tastes and textures. The ability to improvise is just as important as the willingness to follow a recipe to the gram. Planning and successfully executing a complex meal is an exercise in project management. Understanding how convection, radiation and microwave ovens work — and why — in relation to various ingredients represents the antithesis of perishable knowledge. The kitchen can and should be a laboratory for innovation. There are few better courses for combining scientific insights, raw materials, new technologies and customer satisfaction.
These classes strive to balance the transmission of knowledge in classroom environments with the cultivation of real-world skills. Coursework here demands an appreciation of how to collaborate; interact with more data and analytics; generate and communicate testable results; and improvise and innovate if things don’t go as planned. Students are not simply studying for tests; they’re testing their own ingenuity and intuition. Students achieving competence — let alone mastery — in their coursework would be both cognitively enriched and more economically desirable to potential employers. It wouldn’t hurt their ability to be entrepreneurial either.
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