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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: 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: Krista D. Mattern
Publisher:
ISBN:
Category :
Languages : en
Pages : 28
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Book Description
Research has consistently shown that traditional admission measures--SATʼ scores and high school grade point average (HSGPA)--are valid predictors of early college performance such as first-year grades; however, their usefulness to predict later college outcomes has been questioned, especially for the SAT. This study builds on previous research showing that both SAT scores and HSGPA are predictive of a more distal measure of college success--college graduation within four years. Moreover, each measure provided unique information to the prediction of graduation, indicating the utility of using both measures in the admission process to elect applicants who are most likely be successful. Finally, the relationships between SAT and HSGPA with four-year graduation rates by institutional control and selectivity (i.e., undergraduate admittance rate) were also investigated. The findings demonstrate the usefulness of traditional admission measures for predicting long-term college outcomes.
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: Dmitri Rogulkin
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
The second year of college can be as important as the first year but far fewer studies have been conducted on second-year students. About 12% of students leave California State University - Fresno during or after their second year. In this report, we examined second year students to find the differences between those who graduate and those who leave. A decision tree data mining model was used to identify factors that predict students' graduation. A clustering model was used to classify 5 groups of students by multiple characteristics including their graduation rate (which ranges from 99% for one group to 14% for another). The findings showed that staying on track, which was defined as reaching sophomore level no later than the end of the third semester, and cumulative GPA after the first year were the most influential factors in predicting six-year graduation. Potential interventions that may benefit certain groups were suggested. (Contains 5 notes, 3 tables, and 1 figure.).
Author: Elaine Marie Allensworth
Publisher:
ISBN: 9780972603560
Category : High school attendance
Languages : en
Pages : 26
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Book Description
The First Year Of High School Is A Critical Transition Period For Students, Those Who Succeed In Their First Year Are More Likely To Continue To Do well in The Following Years And Eventually Graduate. Because A Successful Transition Into High School Is So Important, In 1999 The Consortion Developed An Indicator To Gauge Whether Students Make Sufficient Progress In Their Freshman Year Of High School To Be On-Track To Graduate Within Four Years. The Evidence Presented Here Suggests That the On-Track Indicator Can Be A Valuable Tool For Parents, Schools, And The School System As They Work To Improve Students Likelihood Of Graduating.
Author: Karen L. Webber
Publisher: Johns Hopkins University Press
ISBN: 1421439034
Category : Education
Languages : en
Pages : 337
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Book Description
Webber, Henry Y. Zheng, Ying Zhou
Author: Richard Whitmire
Publisher:
ISBN: 9780578438511
Category :
Languages : en
Pages :
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Book Description
Author: Linda M. Creighton
Publisher:
ISBN: 9781109888881
Category : College attendance
Languages : en
Pages : 149
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Book Description
Keywords. Graduation rates, Student factors, and Institutional factors.
Author: Brynn L. Munro
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author's request; PREDICTING GRADUATION RATES OF FIRST-GENERATION COLLEGE STUDENTS by BRYNN MUNRO (Under the Direction of Juliann Sergi McBrayer) ABSTRACT In the 2015-2016 academic year, 56% of college students fit the federal government’s definition of first-generation college student status, meaning that neither parent has earned a baccalaureate degree. The success of this student population is crucial for institutional success. There are few studies which follow first-generation college students and continuing-generation college students longitudinally over six years at the same institution to examine graduation outcomes. This study utilized archival data at an access institution in the Southeastern United States in a causal comparative study using binary logistic regression analysis to determine if first-generation college student status, gender, socioeconomic status, and academic preparedness are predictors for six-year graduation rates. Findings from this quantitative study determined that gender, socioeconomic status, and academic preparedness were significant predictors for graduation within six years of matriculation at the institution. While findings from this study do not entirely align with prior research, a future qualitative study may provide context for the student experience and what factors influenced student success. These findings are intended to help administrators understand their student population and implement intervention strategies to increase graduation outcomes
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
