Robust knowledge serves as a common instructional target in academic settings. Past research identifying characteristics of experts' knowledge across many domains can help clarify the features of robust knowledge as well as ways of assessing it. We review the expertise literature and identify three key features of robust knowledge (deep, connected, and coherent) and four means of assessing these features (perception, memory, problem solving, and transfer). Focusing on the domains of math and science learning, we examine how four instructional techniques--practice, worked examples, analogical comparison, and self-explanation--can promote key features of robust knowledge and how those features can be assessed. We conclude by discussing the implications of this framework for theory and practice.