Advertisement

Predictive Model for Improvement of Spatial Visualization Skills

  • Jorge RodriguezEmail author
  • Luis G. Rodriguez-Velazquez
Conference paper
  • 20 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1135)

Abstract

Improvement of spatial visualization skills is an objective that has been pursued at several academic institutions because it has been reported its positive relationship with STEM disciplines. Spatial visualization has been identified as an important competence for successful studies in the STEM fields. Several academic institutions have implemented a policy to have first-year students tested on such competence, and initiatives to improve visualization skills have been implemented mainly at those academic institutions. A standardized visualization test, which is accepted as indicator of spatial visualization skills, is utilized as an indicator to evaluate students. In this study the goal is to identify the most influential factors in the administered standardized test that will help in predicting test performance and score improvement, thus indicating adequate academic intervention. In this study the main factors are the specific question numbers in the standardized test (i.e., PSVT:R), and the academic intervention is material covered in a CAD course that focuses on improving spatial visualization skills.

Keywords

Predictive model Spatial visualization First-year students 

References

  1. 1.
    Strong, S., Smith, R.: Spatial visualization: fundamentals and trends in engineering graphics. J. Ind. Technol. 18(1), 1–6 (2002)Google Scholar
  2. 2.
    Yue, J.: Spatial visualization by realistic 3D views. Eng. Des. Graph. J. 72(1), 28–38 (2008). winterGoogle Scholar
  3. 3.
    Prieto, G., Velasco, A.D.: Does spatial visualization ability improve after studying technical drawing? Qual. Quant. Res. Note 44(5), 1015–1024 (2010)CrossRefGoogle Scholar
  4. 4.
    Sorby, S.A.: Developing 3-D spatial visualization skills. Eng. Des. Graph. J. 63(1), 21–32 (1999)Google Scholar
  5. 5.
    Sorby, S.A.: Assessment of a new and improved course for the development of 3-D spatial skills. Eng. Des. Graph. J. 69(3), 6–13 (2005)Google Scholar
  6. 6.
    Kozhevnikov, M., Thornton, R.: Real-Time data display, spatial visualization ability, and learning force and motion concepts. J. Sci. Educ. Technol. 15(1), 111 (2006)CrossRefGoogle Scholar
  7. 7.
    Titus, S., Horsman, E.: Characterizing and improving spatial visualization skills. J. Geosci. Educ. 57(4), 242–254 (2009)CrossRefGoogle Scholar
  8. 8.
    Guay, R.: Purdue Spatial Visualization Test – Visualization of Rotations. IN, Purdue Research Foundation, West Lafayette (1977)Google Scholar
  9. 9.
    Big Data Analytics: What it is and why it matters (2017). https://www.sas.com/en_us/insights/ analytics/big-data-analytics.html
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Rodriguez, J., Rodriguez-Velazquez, L.G.: Comparison of spatial visualization skills in two approaches to entry-level graphic courses. In: Proceedings of ASEE Annual Conference (2016)Google Scholar
  14. 14.
    Rodriguez, J, Rodriguez, L.G.: Comparison of spatial visualization skills in courses with either graphics or solid modeling content. In: Proceedings of ASEE-EDGD Mid-year Conference (2016)Google Scholar
  15. 15.
    Rodriguez, J., Rodriguez-Velazquez, L.G.: Application of data analytics approach to spatial visualization test results. In: Proceedings of ASEE Annual Conference (2018)Google Scholar
  16. 16.
    Rodriguez, J., Bairaktarova, D.: Evaluation of improvements in visualization test scores using predictive analytics. In: Proceedings of ASEE Annual Conference (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Western Michigan UniversityKalamazooUSA
  2. 2.University of WisconsinWaukeshaUSA

Personalised recommendations