Seeking Evidence for Basing the CS Theory Course on Non-decision Problems (Abstract Only)

Computational and complexity theory are core components of the computer science curriculum, and in the vast majority of cases they are taught using decision problems as the main paradigm. For experienced practitioners, decision problems are the best tool. But for undergraduates encountering the material for the first time, non-decision problems (such as optimization problems and search problems) may be preferable. This lightning talk will give a brief pointer to some new technical definitions and pedagogical strategies that have been used successfully for teaching the theory course using non-decision problems as the central concept. For example, instead of the familiar complexity classes P and NP, we can define analogous classes of non-decision problems, Poly and NPoly. The key question behind this lightning talk is to ask whether the new definitions and strategies are actually beneficial. Anecdotal evidence and certain theories of learning suggest the new approach should result in superior learning outcomes for students. We are seeking ideas, feedback, and collaborators interested in investigating this hypothesis and obtaining stronger evidence for it. To summarize, our central question is: how can we investigate whether students gain a superior grasp of computational and complexity theory when they are taught primarily using non-decision problems?