Austin, Texas
June 14, 2009
June 14, 2009
June 17, 2009
2153-5965
Educational Research and Methods
11
14.1295.1 - 14.1295.11
10.18260/1-2--5128
https://peer.asee.org/5128
479
Mark Urban-Lurain is the Director of Instructional Technology Research & Development in the Division of Science and Mathematics Education at Michigan State University. Dr. Urban-Lurain's research interests are in theories of cognition, their impact on instructional design and applying these to the use of instructional technology. He is also interested in the role of technology in educational improvement and reform.
Jon Sticklen is the Director of the Applied Engineering Sciences major, College of Engineering, Michigan State University. Dr. Sticklen also serves as the College Coordinator for engineering education research, and is an Associate Professor in the Computer Science and Engineering Department, MSU. Dr. Sticklen has lead a laboratory in knowledge-based systems focused on task specific approaches to problem solving. More recently, Dr. Sticklen has pursued engineering education research focused on early engineering; his current research is supported by NSF/DUE and NSF/ CISE.
Daina Briedis is an Associate Professor in the Department of Chemical Engineering and Materials Science at Michigan State University. Dr. Briedis has been involved in several areas of education research including student retention, curriculum redesign, and the use of technology in the classroom. She is a co- PI on two NSF grants in the areas of integration of computation in engineering curricula and in developing comprehensive strategies to retain early engineering students. She is active nationally and internationally in engineering accreditation and is a Fellow of ABET.
Thomas F. Wolff is Associate Professor of Civil Engineering and Associate Dean of Engineering for Undergraduate Studies at Michigan State University. He has taught undergraduate and graduate courses in geotechnical engineering and reliability analysis. His research and consulting has focused on the design and evaluation of dams, levees and hydraulic structures, and he has been involved in several studies related to the failure of New Orleans levees in hurricane Katrina. As Associate Dean, he oversees curriculum, advising, career planning, study abroad, early engineering and other related initiatives.
Understanding Factors Contributing to Retention in Engineering: A Structural Equation Modeling (SEM) Approach Introduction
Retention of early engineering students is a nation-wide concern that will affect the strength of the future engineering workforce and, hence, the role of the United States as a dominant world player in engineering and technology1. Michigan State University (MSU) and Lansing Community College (LCC) were recently awarded a five-year NSF STEP grant (STEM Talent Expansion Program) to increase retention by 10% over current levels at our large, research- intensive institution. The project is titled Engaging Early Engineering Students to Expand Numbers of Degree Recipients (EEES).
The major research challenge in this project is to understand the interactions among the various components of the project. Our engineering curricula are not lock-step, so students may elect to participate in various programs and the interactions among the interventions may vary by student and the choices they make. These challenges make traditional statistical techniques difficult to use.
Structural Equation Modeling (SEM) is a multivariate procedure that supports hypothesis-testing of causal models in observational studies without the need for random assignment of participants to treatment and control groups. This paper outlines our project, introduces SEM and the models we are evaluating and discusses the data collection, management and analysis we are implementing to track the various components of the project. The methods are appropriate for other in situ studies of educational interventions.
Overview of EEES
EEES targets two groups of students who are at-risk for leaving engineering: 1) students who are academically capable of completing an engineering degree but perceive the education environment of early engineering as being unsupportive and not engaging 2-4; and 2) students who struggle with core prerequisite courses, mainly calculus and physics. Analysis of our past student retention patterns show that grades in these core courses are the best predictors of future admission to Engineering in the junior year. The goal of the EEES project will be achieved through the synergistic deployment of four components designed to involve engineering faculty in rethinking the structure of the introductory courses.
EEES has four components as shown in Figure 1. They are: 1) Connector faculty; 2) Peer Assisted Learning (PAL); 3) Course cross linkages; and (4) Diagnostic (DX) Driven Early Intervention. Details of these components of the EEES project are available in other papers5, 6.
EEES targets students in four key technical courses taken by early engineering students to prepare them for upper level disciplinary courses: pre-calculus algebra and trigonometry (MTH 116), calculus 1 (MTH 132), physics 1 (PHY 183) and computation-based problem solving (EGR 102). The four EEES components are shown in the bubbles in Figure 1. The dark boxes show groups of faculty or students who are part of the implementation of EEES. The dark bubble shows the target group: early engineering students.
Urban-Lurain, M., & Sticklen, J., & Briedis, D., & Buch, N., & Wolff, T. (2009, June), Understanding Factors Contributing To Retention In Engineering: A Structural Equation Modeling (Sem) Approach Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--5128
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