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• W2612088157 abstract "Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Predicting Academic Success for First Semester Engineering Students Using Personality Trait Indicators Abstract The dual factors of attracting and retaining talented students in the areas of science, technology, engineering and mathematics (STEM) are critical issues for building the technology work force. When students enter colleges/universities and declare an engineering major, retention becomes the primary focus. Retention of talented students is a significant issue in engineering programs and improvement of retention rates can be a powerful tool in increasing the number of engineering graduates needed for national and global competitiveness. A number of studies have examined predictors of success for entering freshman engineering students including SAT scores and high school performance. The goal of this present work is to identify other personality factors that are critical for retention. Knowing this information, timely and targeted intervention can be applied to improve student success. The area of internal motivation is often proposed as a success factor and generally studies have neglected this area due to the difficulty in measuring and evaluating. This study considers the results of the Big Five and locus of control tests given to a group of first semester engineering freshmen. Factors of these tests were evaluated as tools to measure student motivation to succeed. The levels of these traits were then employed in a multifactor linear regression model to predict overall grade point average for the first semester. The study found that two of the Big Five factors along with locus of control were significant prediction variables for first semester grade point average. Introduction Retention of engineering students is a continuing concern among university academic programs nationwide. In improving retention, engineering educators have spent significant effort in identifying relationships between various measures of success and prediction variables in the hope of identifying focused interventions to improve student success. A variety of multi-variable models have been developed to predict various measures of student success using a range factors. These studies examined the use of high school grade point averages (GPAs) and scores on standardized tests to predict student performance.1, 2, 3 In assessing the field of engineering in particular, Takahira, et al.4, found that the primary factors associated with persistence in an engineering statics course were GPA and SAT-math scores. Another study reported a positive effect of an entrepreneurship program on GPA and retention.5 Other models have been more complex. Student success and persistence were examined by French et al. using hierarchical linear regression.6 They examined both quantitative variables (SAT scores, high school rank, university cumulative grade point average) and qualitative variables (such as academic motivation and institutional integration). For measures of success they used junior and senior GPA, university enrollment and major enrollment over six and eight semesters. The study found that SAT scores, high school rank, and gender were significant predictors of GPA and that an orientation course was not a significant factor in predicting college success." @default.
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• W2612088157 date "2020-09-04" @default.
• W2612088157 modified "2023-09-27" @default.
• W2612088157 title "Predicting Academic Success For First Semester Engineering Students Using Personality Trait Indicators" @default.
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• W2612088157 doi "https://doi.org/10.18260/1-2--4404" @default.
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