Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Engineering Design Graphics
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.
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. https://peer.asee.org/29807
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