Collaborative Research: Spatial Skills and Success in Introductory Computing

University Of North Carolina At Charlotte, Charlotte NC

Investigators

Abstract

Prior research studies have found that low spatial ability is a predictor of failure in several engineering disciplines. Furthermore, there is evidence that some students from underrepresented groups in engineering and computer science (e.g., women and African Americans and Hispanics), demonstrate statistically lower spatial abilities, but low spatial ability can be improved. Engineering students who have gone through interventions to enhance their spatial abilities have succeeded in great numbers in undergraduate engineering programs. While engineering and computer science are STEM disciplines that are often thought of as being related, it is still unknown whether spatial ability is also a factor in student success in computer science coursework. This project will explore whether spatial ability is a predictor of success in first-year college computing courses. If so, this project will study whether students with low spatial abilities can be trained to improve, and whether their performance in computing classes (and their retention) improves as a result. The short-term and long-term impacts of improving students' spatial abilities will be determined. This collaborative partnership includes faculty at the University of Nebraska-Lincoln, Texas Woman's University, and the University of North Carolina-Charlotte. Determining the 3D spatial skills components most important to succeed in introductory computing courses will enable the development of spatial skills interventions for computing students. The research team will determine the impact of spatial skills interventions on the performance and overall retention of computing students. In doing so, it will also explore whether providing spatial skills training also improves the experience of female and underrepresented minority students, thus increasing the likelihood that they will choose to take additional computing classes. This project will demonstrate a series of approaches that can improve students' spatial abilities. It is expected that the results of this work could be applied to other domains. For instance, many researchers within the algorithm visualization community have had limited successes with the creation and use of educational tools/visualizations. If there is a correlation between a student's low-spatial ability and the student's inability to learn from a particular algorithm visualization, this work will have provided this community with a means to improve the effectiveness and the impact of the tools/visualizations they are creating to improve student learning. Results of this project will be disseminated through conferences papers, workshops, and through a project's web site where all materials will be accessible to the public.