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Evaluation of Improvements in Visualization Test Scores Using Predictive Analytics

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Engineering Design Graphics Division Technical Session 2 - Design & Manufacturing Topics

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Engineering Design Graphics

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Paper Authors


Jorge Rodriguez P.E. Western Michigan University

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Professor in the Department of Engineering Design, Manufacturing, and Management Systems (EDMMS) at Western Michigan University's (WMU). Co-Director of the Center for Integrated Design (CID), and currently the college representative to the President’s University-wide Sustainability Committee at WMU. Received his Ph.D. in Mechanical Engineering-Design from University of Wisconsin-Madison and received an MBA from Rutgers University. His B.S. degree was in Mechanical and Electrical Engineering at Monterrey Tech (ITESM-Monterrey Campus). Teaches courses in CAD/CAE, Mechanical Design, Finite Element Method and Optimization. His interest are in the area of product development, topology optimization, additive manufacturing, sustainable design, and biomechanics.

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Diana Bairaktarova Virginia Tech

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Dr. Diana Bairaktarova is an Assistant Professor in the Department of Engineering Education at Virginia Tech. Through real-world engineering applications, Dr. Bairaktarova’s experiential learning research spans from engineering to psychology to learning sciences, as she uncovers how individual performance is influenced by aptitudes, spatial skills, personal interests and direct manipulation of mechanical objects.

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Spatial visualization skills have been long identified as critical competence for success in STEM disciplines, particularly in engineering and technology fields. Several initiatives to improve these skills have been implemented at various academic institutions. This study aims to apply data analytics (DA) to generate a predictive model for improvement of scores in a commonly used spatial visualization test. This model is based on pre- and post- scores by first-year engineering students, and the objective is to identify the factors that have the largest influence on the improvement of the scores. The generated predictive model provides information on dominant factors, i.e., specific questions in the test or demographics, that will help in establishing pedagogical activities aimed at improving spatial skills of students. The dataset used in this study is from a college of engineering’s incoming class, who are required to take the visualization test, and then are offered a one-credit course. Initial analyses are for initial validation of the generated model by comparing the results to previously generated ones. Three different definitions of improvement are used in this study, i.e., raw score, percentage, and tier, given that particular objectives might be different. Results from this study are in line with one observed in previous reports, with an overall test performance improvement, and more involved test question being more influential factors. Similarly, some of the results involving demographic factors follow in a limited fashion previously observed trends. This study shows that DA is a useful tool that will help in the search for specific objective information regarding the value of activities aimed at improving spatial visualization skills.

Rodriguez, J., & Bairaktarova, D. (2019, June), Evaluation of Improvements in Visualization Test Scores Using Predictive Analytics Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32765

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