In this evidence-based practice paper, the relationship between assessment design guided by learning style preferences and student performance in a programming course is investigated. One of the National Academy of Engineering’s 14 Grand Challenges for Engineering is to tailor and differentiate instruction to improve the reliability of learning. A manner in which this differentiation may be accomplished is through attention to the various preferences and styles by which students learn. As such, the purpose of this paper is to present evidence on the effect of formative assessment design on student performance, and whether this effect varies by student learning style. The results from this study can be used by engineering educators to either diversify or personalize their assessment style.
This work is grounded in the Felder-Soloman learning style model, a model that was developed within engineering education and has been validated and widely used within the field. This model categorizes learning styles along four distinct dimensions: perception (sensing versus intuitive), input (visual versus verbal), processing (active versus reflective), and understanding (sequential versus global). Along each of these dimensions, students are categorized as having a mild, moderate, or strong preference in each of these four learning style scales.
This study takes place in a mid-size, public university in the western United States. The sample for this study includes mechanical engineering undergraduate students across four sections of a required programming course in MATLAB, taught by the same instructor. These students were provided the Index of Learning Styles at the beginning of the semester. Students were administered a weekly quiz to assess their ability to write code, but construction of this assessment varies by section to favor different preferences of one of the four Felder-Soloman learning style dimensions. Performance on these quizzes is objectively scored using a standardized rubric. General linear modeling is used to determine if quiz scores differ by quiz construction condition, and if learning style preference interacts with quiz condition to predict performance on each assessment. Findings portray a complex relationship between quiz construction, learning style preference, and assessment performance.
Shanon Reckinger joined the department of Mechanical and Industrial Engineering at Montana State University in Fall 2015. She received her PhD in Mechanical Engineering at the University of Colorado Boulder in August of 2011. Before her position at MSU, she was a Clare Boothe Luce Professor at Fairfield University in the department of Mechanical Engineering for four years. Her research interests include ocean modeling, computational fluid dynamics, fluid dynamics, and numerical methods. Shanon has taught courses in thermodynamics, numerical methods (graduate), fluid dynamics, gas dynamics (graduate), computational fluid dynamics (undergrad/graduate), fundamentals of engineering, and introduction to programming in MATLAB.
Bryce E. Hughes is an Assistant Professor in Adult and Higher Education at Montana State University, and holds a Ph.D. in Higher Education and Organizational Change from the University of California, Los Angeles, as well as an M.A. in Student Development Administration from Seattle University and a B.S. in General Engineering from Gonzaga University. His research interests include teaching and learning in engineering, STEM education policy, and diversity and equity in STEM.
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