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The IEEE Education Society, the IEEE Computer Society, and the American Society for Engineering Education Educational Research and Methods Division (ASEE-ERM) sponsored the 48th Frontiers in Education (FIE) Conference in San Jose, CA, USA, held October 3–6, 2018. Claudio R. Brito, President of the IEEE Education Society, and Stephen T. Frezza, Chair of the FIE Steering Committee, presented awards sponsored by the Education Society and the FIE Conference. In addition, the IEEE Undergraduate Teaching Award, which is administered by the IEEE Awards Board and sponsored by the IEEE Education Society, was presented.

This paper describes a study to assess the demand of problems posed in undergraduate electrical engineering courses. This is part of a larger study to examine the gap between the demand of problems and the types of reasoning students use when attempting to solve them. The analysis uses a two-dimensional taxonomy table consisting of rows and columns that define cognitive processes and categories of knowledge, respectively. The cognitive process dimension (i.e., the rows of the table) contains six categories: remember, understand, apply, analyze, evaluate, and create. The knowledge dimension (i.e., the columns of the table) contains four categories: factual, conceptual, procedural, and metacognitive. Preliminary results suggest that the proposed two-dimension analysis can be successfully used to characterize the demand of electrical engineering problems.

The ability to solve problems is a critical skill in all undergraduate engineering curricula. Students' capacity for problem solving is complicated by the fact that higher level reasoning skills, including the capacity for abstraction, are not innate in a person until the mid-twenties or later. The results of an exploratory study that looked at students' episodes of reasoning when solving problems in a sophomore level electrical circuits course are presented. Students' problem solving attempts are analyzed using the representation mapping framework developed by Hahn and Chater that is based on store representations of knowledge and how they are applied. This framework distinguishes between similarity and rules-based cognitive processes, and accounts for memory-bank, rules-based, similarity-based and prototype types of reasoning. Students were asked to think aloud when solving specific problems selected by the course instructor. The interviews were recorded, transcribed, and analyzed in detail to identify the types of reasoning and the degree of abstraction in the students' problem solving attempts. This study demonstrated that representation mapping is useful framework for studying students' problem solving skills in electrical engineering.