Asee peer logo

Students Use Statistics to Justify Senior Project Selection

Download Paper |

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

Permanent URL

https://peer.asee.org/22493

Download Count

19

Request a correction

Paper Authors

biography

Murray Teitell DeVry University, Long Beach

visit author page

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.

visit author page

author page

William S. Sullivan DeVry University, Long Beach

Download Paper |

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.

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. https://peer.asee.org/22493

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2013 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015