The Automaticity of Similarity-Based Reasoning
Edward E. Smith
University of Michigan
A recurring theme in Bill McKeachie's writings about teaching and learning has been the importance of understanding students' cognition (e.g., McKeachie, 1980a, 1980b). For example, one line of McKeachie's work has focused on students' cognitive structures of key classroom concepts, and how the form and evolution of such structures reflect on student performance ( Lin, McKeachie, Wernander, & Hedegard, 1970; Naveh-Benjamin, McKeachie, & Lin, 1989; Naveh-Benjamin, McKeachie, Lin, & Tucker, 1986). Other work on learning strategies (McKeachie, Pintrich, & Lin, 1985a) and learning to learn (McKeachie, Pintrich, & Lin, 1985b; Pintrich, McKeachie, & Lin, 1987) has emphasized the importance of teaching students key concepts of cognitive psychology along with the learning strategies that make use of these concepts. The importance of such considerations is underscored by the recent review of teaching and learning in the college classroom by McKeachie, Pintrich, Lin, Smith, and Sharma ( 1990); in this document, a considerable amount of space is devoted to student cognition, including such topics as knowledge structure, cognitive and metacognitive learning strategies, and thinking and problem solving. In this chapter we hope to contribute to such work on student cognition, focusing on issues that are relevant to probabilistic reasoning and to training such reasoning.