Retention in Engineering and Personality

Brown, Nina W.; Cross, E. J.

doi:10.1177/0013164493053003007

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…These two criteria are analogous to the criteria of academic performance and retention in the educational literature. Furthermore, variations of these constructs have been found to be predictive of college performance (e.g., Malloch & Michael, 1981;Wolfe & Johnson, 1995) and college retention (Brown & Cross, 1993) in the educational measurement literature. Thus, it seems likely that these personality constructs are useful predictors of college outcome criteria.…”

Section: Other Relevant Literaturesmentioning

confidence: 99%

Motivational and Skills, Social, and Self-Management Predictors of College Outcomes: Constructing the Student Readiness Inventory

Casillas

Robbins³

et al. 2005

Educational and Psychological Measurement

100

144

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The authors used a rational-empirical approach to construct the Student Readiness Inventory, measuring psychosocial and academic-related skill factors found to predict two important college outcomes, academic performance and retention, in a recent meta-analysis. The initial item pool was administered to 5,970 first-year college students and high school seniors to empirically validate and cross-validate the underlying factor structure. Ten first-order and 3 second-order factors were derived, partially resembling the original conceptual model. Future study is needed to explore the criterion and predictive validities of the factors constituting this inventory.

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Section: Other Relevant Literaturesmentioning

confidence: 99%

Motivational and Skills, Social, and Self-Management Predictors of College Outcomes: Constructing the Student Readiness Inventory

Casillas

Robbins³

et al. 2005

Educational and Psychological Measurement

100

144

View full text Add to dashboard Cite

show abstract

“…curricular activities or programs can provide more opportunities for students to feel more satisfied. The proposed SEM can be extended to accommodate individual students, such as how a student's success is affected by a student's attitude for study and the level of parental education [21][22][23].…”

mentioning

confidence: 99%

A Strategic Analysis of Korean Engineering Education Based on Two Satisfaction Scores

Lee

Yoon

Kim

et al. 2007

J of Engineering Edu

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Student avoidance of science and engineering fields is a significant problem that can hinder national competitiveness. In this study, a structural equation model is used to propose two scores to measure student satisfaction with Korean engineering education: the P‐score, a measure of the students' satisfaction with aspects of their personalities, and the M‐score, a measure of the students' satisfaction with aspects of their majors. Using these two scores to assess the students' satisfaction with engineering education in Korea, several strategic approaches to improve student satisfaction with Korean engineering education are proposed.

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“…Significant variables included in order: Math SAT, High School Class Rank, Ability to Analyze Data, Knowledge of Contemporary Issues, Ability for Life Long Learning, Plan to Attend Graduate School, Co-op Experience, Ability to Use Modern Engineering Tools, Knowledge of Mathematics, Study Abroad Experience, and Ability to Communicate Effectively. A model for graduating seniors from the previous year (2000) was comparable with a slightly smaller R-value (0.661) and also smaller R 2 and adjusted R2…”

mentioning

confidence: 89%

“…While a diverse number of systems have been successfully modeled, it is only recently that attention has turned to the engineering education system. To date, many of the empirical modeling applications in engineering education have focused on retention or performance [2,3,4,5,6,7,8]. At the University of Pittsburgh, we have developed logistic regression models to predict attrition and performance in our freshman engineering program using quantified measures of student attitudes [9].…”

Section: Background On Modeling In Engineering Educationmentioning

confidence: 99%

Modeling For Educational Enhancement And Assessment

Shuman¹,

Wolfe²,

Scalise³

et al.

2002 Annual Conference Proceedings

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Industrial engineering programs have typically adopted the new ABET accreditation criteria with more enthusiasm than other engineering programs, in part since the principles of continuous improvement and statistical measurement are commonly taught in most curriculums, and skills such as team work and data analysis are staples of modern IE curricula. However, such complementary skills should not limit the expertise that industrial engineers use to improve engineering programs. Mathematical models can be effective tools for both enhancing learning and assessment. This paper presents a number of modeling approaches that a team, consisting primarily of industrial engineers at the University of Pittsburgh has developed in conjunction with colleagues at the Colorado School of Mines over the course of several years to demonstrate the efficacy of this approach to ABET's requirement of continuous improvement. Using both logistic regression analysis and various neural network algorithms, we have employed empirical modeling to successfully improve retention in engineering, predict probation during the first year, and determine proper placement in math courses. We are also in the early stages of developing similar models to determine a student's intellectual development, determine student achievement based on students' attitudes towards engineering and themselves, as well as predict various EC 2000 outcomes based on students' attitudes. We describe each of theses models separately in this paper to emphasize the need for modeling as a viable tool for evaluation in engineering education.

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Retention in Engineering and Personality

Cited by 10 publications

References 6 publications

Motivational and Skills, Social, and Self-Management Predictors of College Outcomes: Constructing the Student Readiness Inventory

Motivational and Skills, Social, and Self-Management Predictors of College Outcomes: Constructing the Student Readiness Inventory

A Strategic Analysis of Korean Engineering Education Based on Two Satisfaction Scores

Modeling For Educational Enhancement And Assessment

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