Assessment of Robust Learning with Educational Data Mining

Ryan S. Baker & Albert T. Corbett   |    Volume Nine  |    Email Article Download Article

Many university leaders and faculty have the goal of promoting learning that connects across domains and prepares students with skills for their whole lives. However, as assessment emerges in higher education, many assessments focus on knowledge and skills that are specific to a single domain. Reworking assessment in higher education to focus on more robust learning is an important step towards making assessment match the goals of the context where it is being applied. In particular, assessment should focus on whether learning is robust (Koedinger, Corbett, & Perfetti, 2012), whether learning occurs in a way that transfers, prepares students for future learning, and is retained over time; and also on skills and meta–competencies that generalize across domains. By doing so, we can measure the outcomes that we as educators want to create, and increase the chance that our assessments help us to improve the outcomes we wish to create. In this article, we discuss and compare both traditional test–based methods for assessing robust learning, and new ways of inferring robustness of learning while the learning itself is occurring, comparing the methods within the domain of college genetics.

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