June 14, 2015
June 14, 2015
June 17, 2015
26.1697.1 - 26.1697.11
Engineers are commonly described as problem solvers. Arguably, the best problem solversconsist of the most versatile information-gatherers and processors. Learning styles describe howindividuals gather and process information. The Felder-Silverman learning styles model consistsof eight learning styles dimensions, with two opposing preferences in each dimension(active/reflective, sensing/intuitive, visual/verbal, sequential/global) that categorize individualsbased on how they best process, perceive, receive, and understand information. It is important tonote that these descriptions of learning styles are preferences, and not absolute characteristics,and the Felder-Silverman model accounts for “balanced,” “moderate,” and “strong” preferenceswithin each dimension. However, because learning styles describe the cognitive processesinvolved in problem solving, it may be argued that individuals with more balanced learningstyles will be better problem solvers. Originally, our study focused on the correlation betweenstudent learning styles, self-efficacy, attitudes/perceptions, and performance in an introductoryundergraduate chemical engineering course at a large Midwestern university, in an effort tobetter understand our student population and provide a basis for curricular development. Wewere interested in understanding whether there were observable differences in studentperformance and self-efficacy that correlated with student learning style, specifically onproblems that exhibited a learning style bias in presentation or solution. We categorized thelearning styles engaged by exam problems in their presentation and their solution and ranstatistical analyses for correlation with performance and self-efficacy. While we discoveredseveral instances where students of one learning style preference outperformed orunderperformed compared to students of another learning style preference on specific problems,we made an even more interesting observation in categorizing the learning styles exploited byexam problems of different instructors. The study has been conducted in two separate sections ofthe same course, offered the same semester, by different instructors. There was a largeobservable difference in the learning styles utilized in exam problems by instructor. Someconcepts and equations used were the same, but the ways in which students were asked toprocess and problem solve were drastically different. One section had exams that contained onlya few problems, each of a highly sequential and numeric nature. Intuitive problems were nearlynon-existent. The other section’s exams had five or six questions, and nearly each questionutilized multiple learning styles in presentation and solution methods. We are currently repeatingthe study with two more sections of the same course, again with two separate instructors, andobservations are similar (results will be available by full submission date). We are adding anend-of-term general chemical engineering self-efficacy and concept inventory in an attempt tobetter understand whether the courses may be considered equivalent despite these observabledifferences in teaching style. The study will also be conducted in Spring 2015 with two moresections, again two separate instructors. These results will be available by June 2015. We believethese differences in student experience may show significant impact on student understanding ofthe introductory course material, as well as correlate with self-efficacy and future outcomes.After multiple semesters of evaluation are complete, we will propose a new course model thatensures a more consistent experience in this course, and hopefully a better foundation for allstudents in the material.
Miskioglu, E. (2015, June), Variability in Instruction of Introductory Chemical Engineering Course: Does it affect our students? Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.25033
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