Freshmen to graduate students in higher education learn about conducting research within the legal and policy frameworks. In many cases, learners may have to address “hard problems,” those that are inherently complex and challenging, open-ended (and often without known solutions or with multiple complex solutions), and requiring professional collaboration, innovation and creativity, and deep knowledge. How should those who design such learning provide optimized learner feedback in the context of learning with hard problems at every step of the learning process? How can feedback be designed for contexts in which the most effective or efficient solutions are not known?
TopIntroduction
Like kind learning environments, a kind world is based on repeating patterns…The current world is not so kind; it requires thinking that cannot fall back on previous experience.
-- David Epstein in Range: Why Generalists Triumph in a Specialized World (2019, p. 104)
Despite more than a century of work, research on learning and memory has provided designers of classroom curricula or computer-aided instruction systems with surprisingly few bits of concrete guidance on how to speed learning and retard forgetting.
-- Harold Pashler, Nicholas J. Cepeda, John T. Wixted, and Doug Rohrer (2005, p. 3)
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A popular definition of a “hard problem” is that it is one that is inherently difficult and complex, open-ended and without known solutions, and requiring cross-disciplinary skill sets and innovative thinking and deep knowledge. According to “computational complexity theory,” hard problems are inherently difficult and require “significant resources” to solve (“Computational complexity theory,” Sept. 17, 2019). Also known as “wicked problems,” hard problems exist in domains where “the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both” (Epstein, 2019, p. 21). Such problems may be long-standing ones in a particular learning discipline, or it may be of such scope that complexity and challenge are inherent.
In formal higher education learning, “hard problems” are used in various forms: case studies, simulations, problem-based learning, project-based learning, research writing, and others. These problems may be actual or simulated, or some combination. They may be applied or theoretical and conceptual.
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What features should a next-generation space station have, and how can these be effectively engineered and designed?
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How should a diplomatic negotiating team encourage a dictator to move towards giving up nuclear weapons?
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How can societies be encouraged to engage in various activities in promotion of environmentalism?
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What sort of support interventions should be offered to a person trying to eat healthily and exercise regularly?
Hard problems may share some similarities with so-called “productive learning,” which require that learners produce something (“design, fabrication, orchestrating, or simply teaching another”) (Okita & Schwartz, 2013, p. 407), and with so-called “generative learning,” in which something new is created and added to a learner’s schema of a particular issue.
Because such hard problems play an important role in formal accredited learning, it is important to explore two main areas:
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How to design “hard problems” for effective learning, and then,
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How to provide effective feedback (guidance and support) for the learning with “hard problems”.
The design of “learning through the study of hard problems” involves at minimum the selection of known details, the presentation and framing of the challenges, the definition of learner roles in approaching issues, the provisioning of some of the necessary tools, and guidance and support (or to be “corrective” and “positive” in the provision of information) in a context of uncertainty. How learning is designed can affect how feedback on the learning work is received and applied or not (Dysthe, Autumn 2011).