Success factors for students transferring into undergraduate engineering degree programs

Anderson, Michael F.; Pérez, Lance C.; Jones, David; Zafft, Carmen R.

doi:10.1109/fie.2011.6142851

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…The receipt of a scholarship was largely connected with influencing the number of hours worked per week ; the larger the scholarship, the fewer the number of hours a student has to work per week. These are important factors for the engineering transfer student population as many of them are working 20 or more hours per week, a situation which is shown to negatively affect a student's academic performance (Anderson, Perez, Jones, & Zafft, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…Goals and commitments include intentions and external commitments. A key external commitment is the number of hours worked per week which is shown to be greater for engineering students (Anderson et al, 2011; Anderson‐Rowland, 2013a, 2013b). The goals and commitments of the institution are reflected in the institutional culture and academic advising, and include transfer specific academic advising and orientation sessions.…”

Section: Resultsmentioning

confidence: 99%

“…Engineering transfer students are found to be more academically involved with their engineering course, but because those courses have significant out‐of‐class expectations, their social integration is limited (Lee & Schneider, 2016). Additionally, working continues to be a necessity for this population, with many working 20 or more hours per week (Anderson et al, 2011; Anderson‐Rowland, 2013a, 2013b). Parental education, financial need, and hours worked per week are consistent with proxy variables for socioeconomic status (SES) (Estep, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Motivation was typically discussed through an intrinsic desire to problem solve or extrinsically as a perception of the prestige of engineering. Both intrinsic and extrinsic motivation were seen to increase student persistence (Allen & Zhang, 2016;Alting, Delale, & Barba, 2013;Anderson, Perez, Jones, & Zafft, 2011;Darrow, 2012;Mobley & Brawner, 2013;Zamudio, 2015). Additionally, Lee and Schneider (2016) posit that motivation is also driven by the student's degree aspirations.…”

Section: Goals and Commitmentsmentioning

confidence: 99%

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Systematic literature review of persistence of engineering transfer students

Smith

Aken

2020

J of Engineering Edu

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Background: Increasing the number of engineering graduates continues to be a national priority and influences the need to understand persistence of all student pathways. Few studies have focused on engineering transfer student pathways and their success post-transfer, suggesting a need for a more comprehensive analysis of existing research to advance this area. Purpose: A comprehensive literature review was conducted to identify the factors found in the literature that most significantly affect engineering transfer student persistence, discuss themes that span the factors, and identify the frameworks used in the literature to understand engineering transfer student persistence. Scope/Method: A systematic literature review method was used to capture a broad range of publications. A thematic synthesis approach was used to analyze the results of the final publication set. Results: The systematic review identified 76 publications in this research area, and analysis of these publications resulted in 33 unique factors and 3 overarching themes. Additionally, 13 persistence frameworks were identified as being used to understand engineering transfer student persistence. Conclusions: Findings from this work suggest that the primary methods institutions can use to increase persistence in engineering of this population are increasing pre-transfer academic requirements, increasing student integration, and providing an inclusive institutional culture. This review also supports the need for an updated persistence framework that considers the characteristics of engineering transfer students. Gaps in the literature were also identified and indicate a need to differentiate vertical and lateral transfer students as well as extend the review of post-transfer academics beyond the first post-transfer year.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Goals and Commitmentsmentioning

confidence: 99%

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Systematic literature review of persistence of engineering transfer students

Smith

Aken

2020

J of Engineering Edu

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Predicting GPA of University Students with Supervised Regression Machine Learning Models

Falát

Piscová

2022

Applied Sciences

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The paper deals with predicting grade point average (GPA) with supervised machine learning models. Based on the literature review, we divide the factors into three groups—psychological, sociological and study factors. Data from the questionnaire are evaluated using statistical analysis. We use confirmatory data analysis, where we compare the answers of men and women, university students coming from grammar schools versus students coming from secondary vocational schools and students divided according to the average grade. The differences between groups are tested with the Shapiro–Wilk and Mann–Whitney U-test. We identify the factors influencing the GPA through correlation analysis, where we use the Pearson test and the ANOVA. Based on the performed analysis, factors that show a statistically significant dependence with the GPA are identified. Subsequently, we implement supervised machine learning models. We create 10 prediction models using linear regression, decision trees and random forest. The models predict the GPA based on independent variables. Based on the MAPE metric on the five validation sets in cross-validation, the best generalization accuracy is achieved by a random forest model—its average MAPE is 11.13%. Therefore, we recommend the use of a random forest as a starting model for modeling student results.

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Success factors for students transferring into undergraduate engineering degree programs

Cited by 2 publications

References 5 publications

Systematic literature review of persistence of engineering transfer students

Systematic literature review of persistence of engineering transfer students

Predicting GPA of University Students with Supervised Regression Machine Learning Models

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