Deprecating Misconceptions Through Context-Dependent Accounts of Productive Knowledge

Brian A. Danielak

This paper aims to expand our sense of what's possible in modeling cognition within computing education research. We argue that research approaches that privilege canonical knowledge do so at the expense of other productive knowledge and ways of knowing that students have. We explore applicable cognitive theory by showing how manifold models of cognition can be powerful frameworks for analysis in Computing Education (CEd). Finally, we conclude with an exploration of epistemological concerns, arguing that a fundamental concern for our research community should be paying attention to what counts as knowledge and knowing in computing learning environments.