Understanding Factors Contributing To Retention In Engineering: A Structural Equation Modeling (Sem) Approach (2020)

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract 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.