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Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression
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Mason, Cindi; Twomey, Janet; Wright, David; Whitman, Lawrence – Research in Higher Education, 2018
As the need for engineers continues to increase, a growing focus has been placed on recruiting students into the field of engineering and retaining the students who select engineering as their field of study. As a result of this concentration on student retention, numerous studies have been conducted to identify, understand, and confirm…
Descriptors: Student Attrition, Engineering, Probability, Comparative Analysis
The Interrelation of First-Year College Students' Critical Thinking Disposition, Perceived Academic Control, and Academic Achievement
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Stupnisky, Robert H.; Renaud, Robert D.; Daniels, Lia M.; Haynes, Tara L.; Perry, Raymond P. – Research in Higher Education, 2008
While a great deal of research has examined students' critical thinking skills, less is known about students' tendencies to use these skills. Specifically, little is known about what factors contribute to students developing a disposition to think critically or what impact this disposition has on college students' academic achievement. Perceived…
Descriptors: College Freshmen, Academic Achievement, Critical Thinking, Thinking Skills
Justice and Truth in Grades and Their Averages.
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Vickers, John M. – Research in Higher Education, 2000
Examines structural features of grade point averages (GPAs) and offers examples showing that GPAs cannot consistently determine class rank since class rank is sometimes permuted with arbitrary change of scale. Notes relativistic efforts to resolve inconsistencies are insufficient. Discusses the function of GPAs as predictors of academic…
Descriptors: Class Rank, Comparative Analysis, Grade Point Average, Higher Education
Artificial Neural Networks: A New Approach to Predicting Application Behavior.
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Gonzalez, Julie M. Byers; DesJardins, Stephen L. – Research in Higher Education, 2002
Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)
Descriptors: Artificial Intelligence, College Applicants, College Choice, Comparative Analysis
Statistical Alternatives for Studying College Student Retention: A Comparative Analysis of Logit, Probit, and Linear Regression.
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Dey, Eric L.; Astin, Alexander W. – Research in Higher Education, 1993
This study explored how logit, probit, and more traditional linear regression techniques compare in predicting college student retention, using data provided by registrars from a national sample of colleges and universities. Results showed little practical difference among the techniques, despite theoretical advantages offered by the first two.…
Descriptors: Academic Persistence, Comparative Analysis, Higher Education, Institutional Research
Fun with Numbers: Alternative Models for Predicting Salary Levels.
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Johnson, Catherine B.; And Others – Research in Higher Education, 1987
The increasing concern with equity issues in higher education, along with litigation, has prompted institutions to undertake salary prediction studies. Four models were compared: (1) entering all variables, (2) excluding rank and tenure, (3) using predicted rank and tenure, and (4) using only "objective" variables. (Author/MLW)
Descriptors: Bias, College Faculty, Comparable Worth, Comparative Analysis
Predicting Student Progression: The Influence of Race and Other Student and Institutional Characteristics on College Student Performance.
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Gosman, Erica J.; And Others – Research in Higher Education, 1983
In a study of college student retention and progression, significant differences were found between black and white students in terms of their attrition rates, overall progression rates, and tendency to follow the prescribed progression pattern. When other student and institutional characteristics are statistically controlled, racial differences…
Descriptors: Academic Achievement, Academic Persistence, Blacks, College Students