Dr. Bairaktarova, in collaboration with social psychologists, a psychometrician, and an engineering education researcher has developed a new mechanical aptitude scale. The mechanical aptitude items were designed and tested across four phases in large samples of engineering and non-STEM students across four U.S. universities. An item analysis was conducted to screen questions not meeting established criteria for item difficulty and item discrimination. After which a one-factor confirmatory factor analysis was run with diagonalized weighted least squares. The current MAT scale consists of 17 multiple choice items narrowed down from a larger bank of 68 items covering topics related to mechanical insight, mechanical knowledge, shop geometry and measurement, and tool knowledge.

The ACE(D)_MAT is presented online with no cost to educators and researchers. The items are stored electronically and reflect three dimensional (3D), colored objects and tools common in engineering. The scale shows no evidence of gender or English-language proficiency bias. The scale reliably distinguishes between expert (engineering students) and novice (non-engineering students) test-takers and is strongly correlated with extant measures of mechanical aptitude. 

The development of MAT scale is published in International Journal of Engineering Education: https://dialnet.unirioja.es/servlet/articulo?codigo=7350196

For more information and use of ACE(D)_MAT, please contact Dr. Bairaktarova (dibairak@vt.edu).

Another project involves investigating psychological interventions that can be used in conjunction with teaching best practices to improve engineering students’ self-evaluations and their performance on critical skills such as spatial ability.

The progress towards this project is published in the Journal of Women and Minorities in Science and Engineering: https://10.1615/JWomenMinorScienEng.2022039497, and Journal of Engineering Education: https://doi-org.ezproxy.lib.vt.edu/10.1002/jee.20495

Bairaktarova, D. & Cohen, C (2024). Interactive spatial training animates sex differences in performance among first-year engineering students. Journal of Women and Minorities in Science and Engineering, 30(2). https://10.1615/JWomenMinorScienEng.2022039497

Hsing, H.-W., Bairaktarova, D., & Lau, N. (2023). Using eye gaze to reveal cognitive processes and strategies of engineering students when solving spatial rotation and mental cutting tasks. Journal of Engineering Education, 112(1), 125–146. https://doi-org.ezproxy.lib.vt.edu/10.1002/jee.20495