June 15, 2019
June 15, 2019
October 19, 2019
Electrical and Computer
Engineering Electromagnetics is a challenging junior-level course containing many concepts and formulae, and is a core course in many Electrical Engineering programs. A traditional way to teach this class is via didactic instruction, i.e., lecture mode. The instructor often introduces the concepts and then works examples for the students. While the process of working examples may be helpful to some students, the question arose as to whether or not actively engaging the students would improve their understanding of the material. To address this hypothesis, raw exam scores were examined for a total of six semesters. In the first three semesters, only direct instruction was used, whereas the next three semesters used direct instruction for the first half of the class period, working no examples, and then having students collaboratively solve examples and problems for the second half of the class, while being mentored by the faculty as needed. Students were strongly encouraged to work together in teams and to discuss the material while the instructor circulated to give guided practice. Solutions became visible 20 minutes before the end of the class period via the learning management system so that students could check their level of understanding. These in-class exercises, in the form of written problem sets, were worth one and one-half letter grades. Pre and post student knowledge was measured by comparing raw scores on four exams for each semester: three exams and a final. Each exam covered the same topics, for example, the first exam was concerned with transmission lines, the second exam tested for knowledge of electrostatics and magnetostatics, and so forth. While the exams differed from semester to semester by changing values, boundary conditions, or solving for a particular variable, the exams were substantially similar in content, number of questions, and number of concepts tested. While student grades were determined by scaling the exam scores, the extent of their content mastery was performed by comparing raw scores. Subsequent analysis revealed that there was an improvement both in average raw score and in standard deviation of score. For example, the average raw score in Spring of 2017 (pre active engagement) was 53.5% with a standard deviation of 17% but in Spring of 2018 (post active engagement) the average raw score improved to 60.1% and the standard deviation decreased to 7.9%.
Compeau, C. R., & Talley, A., & Tran, P. Q. (2019, June), Active Learning in Electrical Engineering: Measuring the Difference Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32030
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