Sectionality or Why Section Determines Grades: an Exploration of Engineering Core Course Section Grades using a Hierarchical Linear Model and the Multiple-Institution Database for Investigating Engineering Longitudinal Development

George D. Ricco; Noah Salzman; Russell Andrew Long; Matthew W. Ohland

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Conference: 2012 ASEE Annual Conference & Exposition
Location: San Antonio, Texas
Publication Date: June 10, 2012
Start Date: June 10, 2012
End Date: June 13, 2012
ISSN: 2153-5965
Conference Session: Research Informing Teaching Practice I
Tagged Division: Educational Research and Methods
Page Count: 14
Page Numbers: 25.1146.1 - 25.1146.14
DOI: 10.18260/1-2--21903
Permanent URL: https://peer.asee.org/21903
Download Count: 364

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Paper Authors

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George D. Ricco Purdue University, West Lafayette

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George D. Ricco is a doctoral student in Purdue University’s School of Engineering Education. He previously received an M.S. in earth and planetary sciences studying geospatial imaging and a M.S. in physics studying high-pressure, high-temperature FT-IR spectroscopy in heavy water, both from the University of California, Santa Cruz. He has a B.S.E. in engineering physics with a concentration in electrical engineering from Case Western Reserve University.
His academic interests include longitudinal analysis, visualization, semantics, team formation, gender issues, existential phenomenology, and Lagomorph physiology.

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Noah Salzman Purdue University, West Lafayette

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Noah Salzman is a graduate student in engineering education and mechanical engineering at Purdue University. He received his B.S. in engineering from Swarthmore College, and his M.Ed. in secondary science education from University of Massachusetts, Amherst. He has work experience as both an engineer and taught science, technology, engineering, and mathematics at the high school level. His research focuses on the intersection of pre-college and undergraduate engineering programs.

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Russell Andrew Long Purdue University

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Matthew W. Ohland Purdue University, West Lafayette orcid.org/0000-0003-4052-1452

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Matthew W. Ohland is Associate Professor of engineering education at Purdue University. He has degrees from Swarthmore College, Rensselaer Polytechnic Institute, and the University of Florida. His research on the longitudinal study of engineering students, team assignment, peer evaluation, and active and collaborative teaching methods has been supported by more than $11.6 million from the National Science Foundation and the Sloan Foundation and his team received the William Elgin Wickenden Award for the Best Paper in the Journal of Engineering Education in 2008 and multiple conference Best Paper awards. Ohland is Past Chair of ASEE’s Educational Research and Methods division and an At-large member the Administrative Committee of the IEEE Education Society. He was the 2002-2006 President of Tau Beta Pi.

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Abstract

Exploration of the Multiple‐Institution Database for Investigating Engineering Longitudinal Development through Hierarchal Linear Models (HLMs) The MIDFIELD database includes complete student records of twelve institutions offering engineering degrees in the United States for an extended number of years. These institutions enroll more than twelve percent of the nation’s total engineering enrollment. While researchers have explored MIDFIELD using regression analysis, thus far only single‐level methodologies have been used. Hierarchical Linear Models, (sometimes called multi‐level models, nested models, or generalized mixed models,) provide a unique interpretive tool to probe a database such as MIDFIELD. Unlike ANOVA analysis variants, HLMs allow for robust analysis of incomplete data sets. For instance, in the case of a database of student grades, ANOVA methods require complete (or duplicate) recording of grades at each interval used for analysis. In other words, if one student has a missing grade for one semester, then that student cannot be used in the analysis. HLMs do not require completeness for strict convergence. More importantly, HLMs retain the nested structure of the data itself through the analysis. In our study, we first prepare a segment of MIDFIELD focusing first year courses endemic to most core curricula for our analysis: introductory physics; calculus; and chemistry. Then, we discuss the construction of a generalized HLM in the form of a cluster observations model. We use such a method to glean an understanding of the variation of section grade distribution in these required, first ‐year courses through the construction of intercorrelation coefficients (ICCs) and through discussion of standard HLM regression coefficients. The work performed here leads to a new MIDFIELD discussion of what grade distributions are observed at each of the partner institutions. We will be able to determine what effect the percentage of engineers enrolled in these first‐year courses has on section grade distribution. The effect of course size on grade distribution can also be studied. Further work involving relating section grade to more fundamental outcomes in MIDFIELD such as graduation rates will be included if it can fit while leaving a paper of reasonable scope..

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Ricco, G. D., & Salzman, N., & Long, R. A., & Ohland, M. W. (2012, June), Sectionality or Why Section Determines Grades: an Exploration of Engineering Core Course Section Grades using a Hierarchical Linear Model and the Multiple-Institution Database for Investigating Engineering Longitudinal Development Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21903

TY  - CPAPER
AB  - Exploration of the Multiple‐Institution Database for Investigating Engineering Longitudinal Development                                 through Hierarchal Linear Models (HLMs)          The MIDFIELD database includes complete student records of twelve institutions offering engineering degrees in the United States for an extended number of years. These institutions enroll  more than twelve percent of the nation’s total engineering enrollment.  While researchers have explored MIDFIELD using regression analysis, thus far only single‐level methodologies have been used.  Hierarchical Linear Models, (sometimes called multi‐level models, nested models, or generalized mixed models,) provide a unique interpretive tool to probe a database such as MIDFIELD.  Unlike ANOVA analysis variants, HLMs allow for robust analysis of incomplete data sets.  For instance, in the case of a database of student grades, ANOVA methods require complete (or duplicate) recording of grades at each interval used for analysis. In other words, if one student has a missing grade for one semester, then that student cannot be used in the analysis.  HLMs do not require completeness for strict convergence. More importantly, HLMs retain the nested structure of the data itself through the analysis.          In our study, we first prepare a segment of MIDFIELD focusing first year courses endemic to most core curricula for our analysis: introductory physics; calculus; and chemistry.  Then, we discuss the construction of a generalized HLM in the form of a cluster observations model.  We use such a method to glean an understanding of the variation of section grade distribution in these required, first ‐year courses through the construction of intercorrelation coefficients (ICCs) and through discussion of standard HLM regression coefficients. The work performed here leads to a new MIDFIELD discussion of what grade distributions are observed at each of the partner institutions.  We will be able to determine what effect the percentage of engineers enrolled in these first‐year courses has on section grade distribution. The effect of course size on grade distribution can also be studied.  Further work involving relating section grade to more fundamental outcomes in MIDFIELD such as graduation rates will be included if it can fit while leaving a paper of reasonable scope..   
AU  - George D. Ricco
AU  - Noah Salzman
AU  - Russell Andrew Long
AU  - Matthew W. Ohland
CY  - San Antonio, Texas
DA  - 2012/06/10
PB  - ASEE Conferences
TI  - Sectionality or Why Section Determines Grades: an Exploration of Engineering Core Course Section Grades using a Hierarchical Linear Model and the Multiple-Institution Database for Investigating Engineering Longitudinal Development
UR  - https://peer.asee.org/21903
DO  - 10.18260/1-2--21903
ER  -