Admission Standards for Graduate Study in Management

Gayle, John B.; Jones, Troy

doi:10.1111/j.1540-5915.1973.tb00566.x

Cited by 31 publications

(6 citation statements)

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“…Regardless of the methodology used, the most important variables uncovered by these investigations were the applicant's GMAT score and undergraduate GPA as minimum admission requirements into a graduate business program [Benson, 1983;Carver and King, 1994;Deckro and Woundedberg, 1977;Gayle and Jones, 1973;Graham, 1991;Paolillo, 1982;Remus and Wong, 1982;and Sobol, 1984]. However, when GMATs and GPAs were combined with other variables, these models explained only 8 to 21 percent of the variation and were not as useful as predictors of success.…”

Section: Literature Reviewmentioning

confidence: 97%

Predicting MBA no-shows and graduation success with discriminate analysis

Clayton

Cate

2004

International Advances in Economic Research

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Section: Literature Reviewmentioning

confidence: 97%

Predicting MBA no-shows and graduation success with discriminate analysis

Clayton

Cate

2004

International Advances in Economic Research

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“…At the undergraduate university level, the bulk of this research concludes that high school grade point average (GPA), scholastic assessment test (SAT) or achievement test scores, and high school class rank predict academic success with “unsettling accuracy,” though these predictors lose their efficacy when college entrance is delayed by several years; then age is a better predictor of success (Merante, 1983). Similarly, at the graduate level, undergraduate GPA, graduate management admission test (GMAT) scores, letters of reference, and work experience have all been used to predict academic success as measured by the first year average or completed GPA or some combination thereof (Deckro & Wounden berg, 1997; Fisher & Resnick, 1990; Gayle & Jones, 1973; Graham, 1991; Wilson & Hardgrave, 1995). Other studies have shown the same measures to be reliable predictors in the context of individual courses, such as accounting (Morgan, Cornick, & Kauder, 1985).…”

Section: Review Of the Literaturementioning

confidence: 99%

Student Hits in an Internet‐Supported Course: How Can Instructors Use Them and What Do They Mean?

Dan¹

2003

Decision Sci J Innov Edu

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The world of education is changing as Web-based technology and courseware are increasingly used for delivery of course material. In this environment, instructors may need new measures for determining student involvement, and ultimately student performance. This study examines whether hits to a Web site have any value for predicting student performance in a traditional course supported by Web activities. Total Hits at the end of the semester was used as one measure. Hit Consistency, determined by assigning a 0 when no hits occurred between class meetings and by assigning a 1 when one or more hits occurred between class meetings, was another. Hit Consistency was significantly correlated with course average (r = .37, p < .001) for 108 students in two course sections. Hit Consistency started to show a significant relationship with course average by the third week (or class). Total Hits was not found to significantly correlate with course average (r = .08, p > .05) at the end of the semester or during any week. These results suggest that students who consistently access a Web site will perform better than those who do not. When Hit Consistency and Total Hits were entered as independent variables into a stepwise regression with course average as the dependent variable, the model was enhanced by the addition of Total Hits after Hit Consistency was entered (R = .43, p < .001). Hierarchical regression analysis in which cumulative grade point average was entered as the first controlling variable suggested that online access may go beyond the predictive value of achievement alone for predicting course performance with Hit Consistency appearing to be the dominant causal variable.

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“…Many of the prior studies involving graduate student performance have used linear regression models to estimate student academic performance based on their pre-admission record of achievements. Gayle and Jones (1973) and Baird (1975) found a significant positive relationship between Graduate Records Examination (GRE) scores and graduate grade point average (GPA) in graduate students. Studies by Lee (2010) used a neural network based approach for predicting the learning effect in design students with an average accuracy of 93.54%.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Prospective Mathematics Teachers’ Academic Success in Entering Graduate Education by Using Back-propagation Neural Network

Bahadır

2016

Journal of Education and Training Studies

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The purpose of this study is to examine a neural network based approach to predict achievement in graduate education for Elementary Mathematics prospective teachers. With the help of this study, it can be possible to make an effective prediction regarding the students' achievement in graduate education with Artificial Neural Networks (ANN). Two different neural networks are used for an effective prediction of the first network in which some core courses are taken by prospective Mathematics teachers in their first two years, including General Mathematics, Pure Mathematics, Analysis I, Analysis II, Geometry, Linear Algebra-I. The scores received from the above courses are used as an input for the back-propagation neural network (BPNN). Additionally, the scores of vocational core courses taken by third year students, including Analysis3, Special Teaching Methods 2, Elementary Number Theory, Algebra, Problem Solving, are used as the output of the BPNN. The second network uses the scores of all courses which are previously mentioned and uses them as an input of BPNN and also uses ALES (Academic Personnel and Postgraduate Education Entrance Exam) score. They are used as an output of the BPNN. Besides, the correlation analysis is conducted by using the average graduation and ALES scores components. Analytical results demonstrate that the BPNN model offers relatively accurate predictions for the student success in graduate education with a high average of accuracy (Neural Network1 is 77.125% and Neural Network2 is 68.5%). Another finding is that there is no significant correlation between the graduate average scores of candidates who qualified for a graduate education and their ALES scores. These results indicate that BPNN is a suitable tool to predict the academic success of all education majors. Student career advisors can use the ANN model to identify the students who have particular potential for graduate education, and this prediction model can help these students adjust their own teaching strategies, and provide guidance and support for their careers.

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Admission Standards for Graduate Study in Management

Cited by 31 publications

References 0 publications

Predicting MBA no-shows and graduation success with discriminate analysis

Predicting MBA no-shows and graduation success with discriminate analysis

Student Hits in an Internet‐Supported Course: How Can Instructors Use Them and What Do They Mean?

Prediction of Prospective Mathematics Teachers’ Academic Success in Entering Graduate Education by Using Back-propagation Neural Network

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