Rodriguez, J., & Bairaktarova, D. (2022, August), Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41240

\bibitem{asee_peer_41240}
  Rodriguez, J., \& Bairaktarova, D. (2022, August), \emph{Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41240

Jorge Rodriguez and Diana Bairaktarova.  "Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41240 Internet.  18 Apr, 2024

\bibitem{asee_peer_41240}
  Jorge Rodriguez and Diana Bairaktarova.
  "Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41240 Internet.  18 Apr, 2024

@INPROCEEDINGS{asee_peer_41240
author = "Jorge Rodriguez and Diana Bairaktarova"
title = "Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41240}
number = {10.18260/1-2--41240}
}

TY  - CPAPER
AB  - A trend at academic institution in the USA is the use of visualization skills as potential indicator of success in technical programs. In this study the objective is to search for specific trends that predict improvements in scores in a standardized visualization test for two different cohort groups. The trend could be of specific question number(s), or as specific question group. The grouping is based on the number and type of manipulations (rotations) required to reach the orientation required. In order to identify those possible trends, predictive analytics techniques are utilized, thus requiring the development of a validated predictive model that serves to identify potential trends. Predictive analytics has been used extensively in business environments and there have been applications in an academic setting, but its application on pedagogical approaches is something novel with high potential.

The technique followed in this study for the creation of predictive models is decision trees. A dataset containing answers to the standard PSVT-R questions by two cohort groups of students is used. The dataset has answers by students to the test at the start of the intervention (pre-), and at the end of the intervention (post-). The initiative in this case is a semester long course offered to first-year engineering students to improve their spatial visualization skills. Some conclusions on the type of problems (i.e., question numbers) that are better predictors of test score are obtained. Results from this study will serve in the identification of material offered during the pedagogical interventions that is more important due to its effect on test scores. Additionally, on the modeling side, results from the predictive model for each subset (pre- and post-) are consolidated with previous results/ thus helping in building a more robust prediction model.
AU  - Jorge Rodriguez
AU  - Diana Bairaktarova
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Comparison of Spatial Visualization Skills Scores for Entry-Level Cohorts
UR  - https://peer.asee.org/41240
DO  - 10.18260/1-2--41240
ER  -