Using Multilevel Logistic Regression Analysis to Predict Graduation Rates at Colleges and Universities PDF Download
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Author: Kenneth Wayne Lewis
Publisher:
ISBN:
Category : College athletes
Languages : en
Pages : 360
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Author: Kenneth Wayne Lewis
Publisher:
ISBN:
Category : College athletes
Languages : en
Pages : 360
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Book Description
Author: Dheeraj A. Raju
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 207
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Book Description
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: Ujwala Paladi
Publisher:
ISBN:
Category : College graduates
Languages : en
Pages : 190
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Book Description
The topic of this thesis is the analysis of factors influencing the retention of undergraduate level students at West Texas A&M University (WTAMU) with a special emphasis on STEM majors. The subjects of this study are first time college students, enrolled at WTAMU in Fall 2010, Fall 2011 and Fall 2012 that have a declared first major in a STEM field. Through Logistic regression analysis, factors influencing student retention and retention to graduation are identified. Identified factors that influence undergraduate student retention and graduation at WTAMU are math and science self-confidence, study habits, HSGPA, class percent, mothers' education, and senior year grades. Factors identified that influence retention to graduation of first year STEM majors at WTAMU are percentile of transfers, desire to finish, self-reported college prep activity, highest degree sought, sociability, distance from campus, HSGPA, class percent, Major code, fathers education, and work.
Author:
Publisher:
ISBN: 9781878477538
Category : College attendance
Languages : en
Pages : 55
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Book Description
"The report examines retention and degree attainment of 210,056 first-time, full-time students at 356 four-year non-profit institutions, using a combination of CIRP (Cooperative Institutional Research Program) Freshman Survey data and student graduation data from the National Student Clearinghouse"--Publisher's web site.
Author: Michael S. Sims
Publisher:
ISBN: 9780438633759
Category : Forecasting
Languages : en
Pages : 77
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Book Description
Abstract: As a result of the California State Universities having four-year graduation rates among freshman students below 20% over the last few years, the Graduation Initiative 2025 has been deployed. This initiative aims to increase the graduation rates to 40%, while eliminating opportunity and achievement gaps. A signicant impact of this is looking at the success of rst-time-freshmen (FTF) and the prediction of whether or not they will graduate in a timely fashion. To this end, a natural [classification] problem is identied: amongst the FTF cohort who will graduate in four years or less(class instance = 1), or more than four years (class instance = 0) including students who did not graduate. In this paper, using Area Under the Curve (AUC) as our models performance metric, we construct classication models that quickly identify students at risk of not graduating in a timely fashion. Furthermore, we will construct models cumulatively—term by term—where each successive model includes student data from matriculation to the end of a given term. Using this approach allows a University to nd an optimal time to deploy possible intervention programs. It should be noted that optimal in this paper means, having a model with high AUC as early into the students academic career as possible. This way, an at-risk student is identied early, and the value of the University intervening is optimized. In this paper we will compare a variety of classication algorithms such as Logistic Regression, Random Forest, and XGBoost to see which model yields the highest AUC. Also we provide insight on interpretation specically identifying the eect each covariate has on the response. This approach will be unique because not only will it be a means for identifying the problem, but also serve as part of the solution.
Author: Brenda Arndt Bailey
Publisher:
ISBN:
Category :
Languages : en
Pages : 470
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Author: Yale B. Quan (Graduate student)
Publisher:
ISBN:
Category : College graduates
Languages : en
Pages : 110
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Abstract: In 2015 the California State University system launched Graduation Initiative 2025 which aims to eliminate the equity gaps in degree completion and increase the average four-year graduation rate from 19% to 40% and the average six-year graduation rate from 57% to 70%. To support CSULB in meeting these goals, this study focuses on performing a multivariate statistical analysis to determine the effects of major change and various demographic and academic factors on timely graduation. The dataset was obtained from the Department of Institutional Research and Analytics and contained academic and demographic information on first-time freshmen accepted between 2009 and 2012. Due to high multicollinearity, dimensionality reduction was performed using Factor Analysis, and the data were analyzed using a combination of hypothesis testing, correlation analysis, multinomial logistic regression, and Fishers linear discriminant analysis.
Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1483312097
Category : Social Science
Languages : en
Pages : 489
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Book Description
Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.
Author: Fauziah binti Ahmad
Publisher:
ISBN:
Category : Graduate students
Languages : en
Pages : 74
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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 498
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