435119 Effects of Variability in Instructional Methods on Student Performance and Learning in Mass and Energy Balances

Tuesday, November 10, 2015: 9:02 AM
Alpine East (Hilton Salt Lake City Center)
Elif Miskioglu, The Ohio State University, Columbus, OH and David W. Wood, Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, OH

As instructors, we often have high expectations of student performance and actual student results can sometimes be baffling. In an effort to better understand factors that influence student performance, we have been more closely examining our introductory course, CBE 2200: Process Fundamentals, more commonly known as mass and energy balances. There is a great range of performance among students in this course, with variation both within a class and by population (semester, section). We began our investigation of student performance in CBE 2200 by focusing on better understanding the student as a learner. Using learning styles (which describe how individuals receive, perceive, process and understand information) as a tool to better understand student problem solving, we have explored the correlations between learning styles, student problem solving strategies, problem type, student perceptions, and performance.

Problem type refers to the category (or categories) of learning styles from the Felder-Silverman model that the problem engages in presentation to the student or expected problem solution. For example, a problem that is presented in a step-wise fashion, often of the form involving several parts that build in complexity and lead to a final answer, is considered “sequential” in both presentation and solution. A problem that asks the student to simply calculate a final value without presenting the intermediary steps as part of the problem would be considered “global.” It is important to note that presentation and solution style do not always match, as a problem may describe a process in words and ask a student to draw a diagram, which would represent “verbal” presentation and “visual” solution.

Because we were examining student approaches towards specific exam problems, we began noticing stark differences in the types of problems that were being presented on exams (i.e., the learning styles engaged) by various faculty instructors. There were also large discrepancies in class performance (as measured by final grades before any adjustment) among sections taught by different faculty in the same semester. With these observations, our study became further focused on understanding the variability in sections taught by different faculty members, and identifying best practices in each. Through more thorough analysis of the syllabi, course goals and learning objectives, assigned homework problems, exam problems, lecture-styles, and administering a concept inventory to each section, we were able to re-evaluate the current approach to teaching CBE 2200 and the equivalency of the course when taught by different instructors.

This course is the foundation of the chemical engineering curriculum and impacts both student retention and future success. Better understanding of how instructional variance may affect student outcomes will allow us to re-design the course to provide a more homogeneous model that allows a better guarantee of achieving necessary learning objectives while allowing instructors to maintain a sense of independence and personal style in their teaching.

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