Students Use Statistics to Justify Senior Project Selection

Murray Teitell; William S. Sullivan

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Conference: 2013 ASEE Annual Conference & Exposition
Location: Atlanta, Georgia
Publication Date: June 23, 2013
Start Date: June 23, 2013
End Date: June 26, 2013
ISSN: 2153-5965
Conference Session: Mathematics Division Technical Session 4
Tagged Division: Mathematics
Page Count: 15
Page Numbers: 23.1108.1 - 23.1108.15
DOI: 10.18260/1-2--22493
Permanent URL: https://peer.asee.org/22493
Download Count: 420

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Paper Authors

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Murray Teitell DeVry University, Long Beach

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Murray Teitell is a Professor at DeVry University, Long Beach, California. He teaches courses in mathematics, science and technology. His research interests are algorithms, solutions of equations and statistics as they relate to education, engineering and design. He is Program Chair-Elect of the Mathematics Division of ASEE.

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William S. Sullivan DeVry University, Long Beach

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Abstract

Students Use Statistics to Justify Senior Project SelectionAbstract Engineering and technology curriculums typically include senior projects as theculmination of a student’s degree program. Students encounter difficulty during theselection process of the project due to uninformed decisions, lack of a structure, methodor model, or an insufficient understanding of the resources needed to complete theproject. The authors address this issue by introducing the utilization of a statisticalmodel in a senior project course.In the course, students work in teams and propose, design, test and build their seniorproject over the course of two semesters. Previously, teams proposed a set of projectsand then settled on a final selection after consultation among the members and theprofessor. In order to make the selection process more structured, the use of metrics wasimplemented to evaluate alternate project concepts. Students were given a spreadsheettemplate model for the metrics and instructed to adapt the model for their measurements.The metrics were based on parameters that measure man hours estimated, team skillscompatibility, resource availability, team interest correlation and team mission statementand public need alignment. For example, the resource metric measured the availabilityand the relative cost of all major components, hardware, software, and tools. For each metric, the student team specified how it was to be measured. This processincluded the identification of what data was available that could be measured. Data setsutilized included relative cost of the proposed components, sales figures of similarproducts, and consumer satisfaction with similar products. Using a standardizedmaximum overall score for the proposed project, each metric had a weighting assignedby the team. For each data set collected, a data error term was computed and the meanand standard deviation of the overall data error was calculated. The “total alternativeproject score” was computed from the individual metric scores. The results of the utilization of the statistical model indicate an increase in studentsuccess, preparedness, and overall achievement of the outcomes of their degree program.

Citation
Format

Teitell, M., & Sullivan, W. S. (2013, June), Students Use Statistics to Justify Senior Project Selection Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22493

TY - CPAPER
AB - Students Use Statistics to Justify Senior Project SelectionAbstract Engineering and technology curriculums typically include senior projects as theculmination of a student’s degree program. Students encounter difficulty during theselection process of the project due to uninformed decisions, lack of a structure, methodor model, or an insufficient understanding of the resources needed to complete theproject. The authors address this issue by introducing the utilization of a statisticalmodel in a senior project course.In the course, students work in teams and propose, design, test and build their seniorproject over the course of two semesters. Previously, teams proposed a set of projectsand then settled on a final selection after consultation among the members and theprofessor. In order to make the selection process more structured, the use of metrics wasimplemented to evaluate alternate project concepts. Students were given a spreadsheettemplate model for the metrics and instructed to adapt the model for their measurements.The metrics were based on parameters that measure man hours estimated, team skillscompatibility, resource availability, team interest correlation and team mission statementand public need alignment. For example, the resource metric measured the availabilityand the relative cost of all major components, hardware, software, and tools. For each metric, the student team specified how it was to be measured. This processincluded the identification of what data was available that could be measured. Data setsutilized included relative cost of the proposed components, sales figures of similarproducts, and consumer satisfaction with similar products. Using a standardizedmaximum overall score for the proposed project, each metric had a weighting assignedby the team. For each data set collected, a data error term was computed and the meanand standard deviation of the overall data error was calculated. The “total alternativeproject score” was computed from the individual metric scores. The results of the utilization of the statistical model indicate an increase in studentsuccess, preparedness, and overall achievement of the outcomes of their degree program.
AU - Murray Teitell
AU - William S. Sullivan
CY - Atlanta, Georgia
DA - 2013/06/23
PB - ASEE Conferences
TI - Students Use Statistics to Justify Senior Project Selection
UR - https://peer.asee.org/22493
DO - 10.18260/1-2--22493
ER -