Identifying Factors Influencing Engineering Student Graduation: A Longitudinal and Cross‐Institutional Study

Abstract

Pre‐existing factors are quantitatively evaluated as to their impact on engineering student success. This study uses a database of all engineering students at nine institutions from 1987 through 2002 (a total of 87,167 engineering students) and focuses on graduation in any of the engineering disciplines. We report graduation rate as a function of years since matriculation, and determine the typical time‐to‐graduation. A multiple logistic regression model is fitted to each institution's data to explore the relationship between graduation and demographic and academic characteristics. A pooled model is fitted to six institutions where a complete data set was available. High school GPA, gender, ethnicity, quantitative SAT scores, verbal SAT scores, and citizenship had significant impact on graduation. While HSGPA, SATQ were significant for all models tested, the significance of other predictors varied among institutions. These studies add to the existing body of research about factors affecting the success of engineering students.

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

“…Nichols et al made use of ordinal regression for analyzing the data obtained from 187questionnaires returned by distance education students; the results showed that among four sets of attributions (namely computer usage attitudes, learning motivation, perception of satisfaction and previous academic performance), only previous academic performance exhibited a strong correlation with dropout [21]. Research by Zhang et al used multivariate regression to analyze the data of 57,549 students from nine universities: results showed that demographic attributions such as gender, ethnicity and nationality attribution are significantly correlated to dropping out [22]. Another logistic regression study conducted by Doherty analyzed the data of 10,466 students in the educational institution information system; results showed that the students' demographic attributions, learning methods, and curriculum interaction were correlated to dropping out [23].…”

Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence

“…Consequently, a clear focus has been directed towards early identification and diagnosis of at-risk students, and a variety of studies using statistical methods, data ISSN 1929-7750 (online). The Journal of Learning Analytics works under a Creative Commons License, Attribution -NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) mining, and machine learning techniques can be found in recent literature (Thammasiri, Delen, Meesad, & Kasap, 2013;DeBerard, Spielmans, & Julka, 2004;Zhang, Anderson, Ohland, & Thorndyke, 2004;Burtner, 2005;Yu, DiGangi, Jannasch-Pennell, Lo, & Kaprolet, 2007;Mendez, Buskirk, Lohr, & Haag, 2008;Li, Swaminathan, & Tang, 2009;Lin, Imbrie, & Reid, 2009;Delen, 2010;Zhang, Oussena, Clark, & Hyensook, 2010).…”

Step-Outs to Stars: Engineering Retention Framework

“…Viewing other factors through the lens of this framework might help explain some of the inconsistencies found in the previous research on engineering retention (see Zhang et al 46 for an example). Examples of factors that could be investigated with respect to the framework include: ethnicity, self-esteem, social integration into engineering, what influenced students to study engineering, time spent on classwork and studying, student participation in high school activities related to engineering, when students became interested in engineering, and beliefs on effort and intelligence.…”