Application of Data Analytics Approach to Spatial Visualization Test Results

Jorge Rodriguez; Luis Genaro Rodriguez

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: EDGD: Assessment & Student Learning
Tagged Division: Engineering Design Graphics
Page Count: 6
DOI: 10.18260/1-2--29807
Permanent URL: https://peer.asee.org/29807
Download Count: 424

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Paper Authors

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Jorge Rodriguez P.E. Western Michigan University

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Faculty member 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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Luis Genaro Rodriguez University of Wisconsin, Waukesha

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Faculty member in the Engineering Department at the University of Wisconsin - Waukesha. Received his Ph.D. in Mechanical Engineering - Design from the University of Wisconsin - Madison. His B.S. degree is in Mechanical and Electrical Engineering from ITESM - Monterrey. Teaches courses in the areas of Engineering Graphics, Mechanics and Numerical Methods. His interest are in the area of pedagogical methodology in CAD/CAE and Mechanics.

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Abstract

The field of data analytics has received substantial attention in the past few years due to a global trend of collecting and analyzing information/data. Most of the attention and applications relate to consumers’ behavior, but the applicability of data analytics has extended to processes and market analyses. Data analytics is considered a generic term used to refer to a set of quantitative and qualitative approaches that are applied to provide the basis for decision-making. The specific objective being pursued with such the use of data analytics approaches could be: increase in productivity, additional business profit, or expected performance or behavior by consumers.

Spatial visualization is a skill that has been linked to the abilities to do engineering and technology work. There are several reports that have provided a relationship between spatial visualization skills of students and their performance in engineering courses, particularly for engineering graphics and design courses. Similarly, there are reports that indicate the value in improving visualization skills when looking at the performance in learning in engineering courses, specifically for female students.

This study pertains the application of a data analytics approach to spatial visualization scores with the objective of obtaining some predictive factors. The data utilized in this study is from the scores of the Purdue Spatial Visualization Tests with Rotations (PSVT:R), which was administered to a group of first-year students taking a course in engineering graphics. Besides their responses to the test, their demographic data was collected together with background academic information all these parameters are used in the data analytics approach applied. The objective of the study is not to prove a specific hypothesis, but to obtain results from a predictive analytic approach that is followed, so that, specific trends are identified and specific interventions could be defined to address any specific behavior or factor. The software used in this study is RapidMiner, and different subsets of data are utilized in the machine learning phase, thus resulting in more robust predictive conclusions.

Citation
Format

Rodriguez, J., & Rodriguez, L. G. (2018, June), Application of Data Analytics Approach to Spatial Visualization Test Results Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29807

TY - CPAPER
AB - The field of data analytics has received substantial attention in the past few years due to a global trend of collecting and analyzing information/data. Most of the attention and applications relate to consumers’ behavior, but the applicability of data analytics has extended to processes and market analyses. Data analytics is considered a generic term used to refer to a set of quantitative and qualitative approaches that are applied to provide the basis for decision-making. The specific objective being pursued with such the use of data analytics approaches could be: increase in productivity, additional business profit, or expected performance or behavior by consumers.

Spatial visualization is a skill that has been linked to the abilities to do engineering and technology work. There are several reports that have provided a relationship between spatial visualization skills of students and their performance in engineering courses, particularly for engineering graphics and design courses. Similarly, there are reports that indicate the value in improving visualization skills when looking at the performance in learning in engineering courses, specifically for female students.

This study pertains the application of a data analytics approach to spatial visualization scores with the objective of obtaining some predictive factors. The data utilized in this study is from the scores of the Purdue Spatial Visualization Tests with Rotations (PSVT:R), which was administered to a group of first-year students taking a course in engineering graphics. Besides their responses to the test, their demographic data was collected together with background academic information all these parameters are used in the data analytics approach applied. The objective of the study is not to prove a specific hypothesis, but to obtain results from a predictive analytic approach that is followed, so that, specific trends are identified and specific interventions could be defined to address any specific behavior or factor. The software used in this study is RapidMiner, and different subsets of data are utilized in the machine learning phase, thus resulting in more robust predictive conclusions.

AU - Jorge Rodriguez P.E.
AU - Luis Genaro Rodriguez
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Application of Data Analytics Approach to Spatial Visualization Test Results
UR - https://peer.asee.org/29807
DO - 10.18260/1-2--29807
ER -