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Author: Donald Wilson Irvine
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
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Author: Donald Wilson Irvine
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
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Author: Tara Laron Young
Publisher:
ISBN:
Category : African American universities and colleges
Languages : en
Pages : 115
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The purpose of this logistic regression study is to review the pre-admission factors through the lenses of multiple retention constructs and graduation rates at a Christian, Historically Black College or University (HBCU). A binary logistic regression is used to analyze the odds of graduation based on a set of pre-admission factors of first-time freshmen, as predictor variables. In particular, the predictor variables of interest are eligibility of academic support based on academic scholarships, gender, international status, and type of high school attended. The outcome variable of interest is graduation. This study is important because it contributes to the scholarship in the study of Christian HBCUs and the understanding of how preadmission factors may affect graduation. This study addresses the problem by using regression relationships to guide supportive programs that reinforce retention, persistence, and completion of students based on pre-admission factors, as reflected in the work of Tinto, Astin and other theorists. The number of participants used for this regression analysis supports adequate statistical power for a medium effect size. This study took place at a Christian HBCU in north Alabama with data collected from the admissions office for the freshmen class of 2011, where N=364. The results of this study suggest that students that attend a private high school have high odds of completing a post-secondary degree at a Christian HBCU and makes recommendations to support the retention and recruitment of the targeted population. The implications for further research could include a variety of replication studies with additional preadmission factors, longitudinal, mixed methods, or qualitative studies reviewing persistence, completion, and yearly graduation rates as they relate to the preadmission factors.
Author: Richard Paul Brandt
Publisher:
ISBN:
Category :
Languages : en
Pages : 256
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Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 824
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Author: Library of Congress. Division of Documents
Publisher:
ISBN:
Category : State government publications
Languages : en
Pages : 562
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Author: Library of Congress. Exchange and Gift Division
Publisher:
ISBN:
Category : State government publications
Languages : en
Pages : 978
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June and Dec. issues contain listings of periodicals.
Author: Robert G. Cope
Publisher: New York ; Toronto : Wiley
ISBN:
Category : Education
Languages : en
Pages : 214
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Author: Dheeraj A. Raju
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 207
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Predictive modeling using data mining methods for early identification of students at risk can be very beneficial in improving student graduation rates. The data driven decision planning using data mining techniques is an innovative methodology that can be utilized by universities. The goal of this research study was to compare data mining techniques in assessing student graduation rates at The University of Alabama. Data analyses were performed using two different datasets. The first dataset included pre-college variables and the second dataset included pre-college variables along with college (end of first semester) variables. Both pre-college and college datasets after performing a 10-fold cross-validation indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The misclassification rate indicates the error in predicting the actual number who graduated. The model misclassification rates for the college dataset were around 7% lower than the model misclassification rates for the pre-college dataset. The decision tree model was chosen as the best data mining model based on its advantages over the other data mining models due to ease of interpretation and handling of missing data. Although pre-college variables provide good information about student graduation, adding first semester information to pre-college variables provided better prediction of student graduation. The decision tree model for the college dataset indicated first semester GPA, status, earned hours, and high school GPA as the most important variables. Of the 22,099 students who were full-time, first time entering freshmen from 1995 to 2005, 7,293 did not graduate (33%). Of the 7,293 who did not graduate, 2,845 students (39%) had first semester GPA
Author: Aboul Ella Hassanien
Publisher: Springer Nature
ISBN: 3030311295
Category : Technology & Engineering
Languages : en
Pages : 1093
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This book presents the proceedings of the 5th International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI2019), which took place in Cairo, Egypt, from October 26 to 28, 2019. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: machine learning and applications, swarm optimization and applications, robotic and control systems, sentiment analysis, e-learning and social media education, machine and deep learning algorithms, recognition and image processing, intelligent systems and applications, mobile computing and networking, cyber-physical systems and security, smart grids and renewable energy, and micro-grid and power systems.
Author: United States. Office of Education
Publisher:
ISBN:
Category : Agricultural colleges
Languages : en
Pages : 1130
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