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Dissertations / Theses on the topic 'Graduation prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Tranchita, Anthony Phillip. "Predictors of Graduation and Rearrest in a Contemporary Juvenile Drug Court Program." DigitalCommons@USU, 2004. https://digitalcommons.usu.edu/etd/6210.

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Research on the efficacy of drug courts for substance-abusing criminal adult offenders has generally found reduced recidivism rates, and both actual and potential cost savings to the public. However , outcome research on juvenile drug courts has been limited. Furthermore , little research has examined variables that may be predictive of outcome in this population. This study reports graduation and rearrest rates for a sample of juvenile drug court participants in Salt Lake City, Utah. Also, this research assessed whether demographics, prior arrest history, attendance at drug education classes, serving detention time, or a preprogram measure of degree of substance abuse (SAS SI-A) help predict several important outcomes (i.e., graduation from the drug court program and number of rearrests per year after leaving drug court). The graduation rate in this sample was fairly high (84.2%). However, the rearrest rate was also relatively high, with slightly over 50% with an arrest for any offense, and 38. 7% with a drug-elated arrest during follow-up (average follow-up time 4.3 years). Serving detention and not attending prevention class predicted lower rates of program graduation, while younger age, male gender, not graduating drug court, non-Caucasian status, and past adjudication predicted higher rates of recidivism (rearrest).

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2

Crumrine, David A. "Effective graduation proficiency assessment parents' perception of high-stakes vs. multiple assessment as a predictor of future success /." Open access to IUP's electronic theses and dissertations, 2008. http://hdl.handle.net/2069/140.

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3

Misigaro, Edwin Nitunga Morreau Lanny E. "Factors influencing Tanzanian students to leave school prior to grade seven graduation." Normal, Ill. Illinois State University, 1993. http://wwwlib.umi.com/cr/ilstu/fullcit?p9323738.

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Thesis (Ed. D.)--Illinois State University, 1993.
Title from title page screen, viewed February 15, 2006. Dissertation Committee: Lanny Morreau (chair), Ming-Gon John Lian, Paul Baker, Keith Stearns. Includes bibliographical references (leaves 134-152) and abstract. Also available in print.

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4

Marshall, David T. "Testing the Ability of Two Series of Models to Predict High School Graduation Status." VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/4756.

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The purpose of this study was to create and test two series of predictive models aimed at projecting high school graduation status. Secondary data were obtained in partnership with an urban school district. All of the predictor variables included in the models tested in this study were academic and nonacademic variables that were found to be significant predictors of high school graduation in previous empirical work. In the first series of models tested, individual academic and nonacademic variables were tested together along with school-level variables. Eighth and ninth grade variables were tested separately to avoid multicollinearity issues. The second series of models tested included similar individual-level academic and nonacademic variables, along with community-level predictors to analyze their ability to predict high school graduation status. Logistic regression and multilevel logistic regression analyses were conducted to analyze the data. The model including community-level predictors yielded a pseudo R-squared value of .40, approximating that 40% of the variance was explained by the predictors in the model. Most of the individual predictors included in the models yielded findings similar to those found in previous literature on high school graduation status projection; however, this was not true for all of the predictor variables included. These differences highlight the tension that can exist between generalizability and local specificity. Significant findings from studies utilizing large nationally-representative longitudinal datasets and other large data sources do not always generalize to settings with samples that differ demographically. This study represents a first step in a line of research aimed at developing a better understanding of high school graduation status, particularly in challenging school contexts.

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5

Campos, Lisa D. "An investigation of cognitive and non-cognitive variables that affect student-athlete graduation and retention." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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6

Kotzè, M., and L. Griessel. "The prediction of the academic performance of MBA students by means of specific aptitudes And competencies." Journal for New Generation Sciences, Vol 6, Issue 2: Central University of Technology, Free State, Bloemfontein, 2008. http://hdl.handle.net/11462/505.

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Published Article
The Council on Higher Education (CHE) (2004) states that graduation rates across all provider types of MBA qualifications in South Africa are not very high. Various studies have reported that, in order to address poor throughput rates, one of the important aspects that needs to be addressed, is the criteria used to select students. The purpose of this study was to identify valid predictors and measures of the academic performance of MBA students. Multiple regression analysis was used to determine the significance of different competencies and aptitudes in predicting academic success. The sample consisted of 135 MBA students from a South African School of Management. The results show that certain aptitudes and competencies, namely numerical aptitude, personal motivation, verbal aptitude, and resilience, contributed statistically significant to academic success.

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7

Mills, Bradley Scott. "Predicting Graduation| An Examination of the Variables that Predict Graduation for Students with Emotional Disabilities." Thesis, North Carolina State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10708320.

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Students with Emotional Disabilities (ED) graduate from high school at rates far below their peers. The completed study utilized archival data from former students’ special education folders and from a nondisabled comparison group to examine variables that had previously been studied in relation to graduation (e.g., repeating ninth grade, extracurricular participation) along with variables identified from the folders of the former students. The descriptive quantitative study identified variables that predicted graduation for individuals with ED and the differences between the variables for individuals with ED and the nondisabled group. The results indicated that GPA and extracurricular participation positively predicted graduation while the number of years spent in 9^th grade negatively predicts graduation for both groups. Specifically for students with ED, student attendance at special education meetings was statistically significant for predicting graduation.

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8

Sims, Michael S. "Predicting Four-Year Graduation| A Sequential Modeling Approach." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10841337.

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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 classication 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.

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9

Sandusky, Sue Ann. "Predicting Student Veteran Persistence." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1585070424571773.

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10

McNeill, Donald B. "An analysis of factors predicting graduation at United States Marine Corps Officer Candidates School." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FMcNeill.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002.
Thesis advisor(s): Samuel E. Buttrey, Lyn R. Whitaker. Includes bibliographical references (p. 69-72). Also available online.

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11

Pacheco, Amanda Celeste. "Cooperative Education as a Predictor of Baccalaureate Degree Completion." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2110.

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Baccalaureate degree completion statistics are surprisingly low. National four-year graduation rates hover around 38%, and six-year graduation rates have remained steady at approximately 63% (Berkner, He, & Cataldi, 2002). At the University of Central Florida, as at many public research institutions, the numbers are even lower. Literature has emerged, however, which suggests that students who participate in cooperative education programs may experience increased motivation to continue the formal education process (Avenoso & Totoro, 1994; Schambach & Dirks, 2002; Somers, 1986). This study investigated the effect of co-op participation on undergraduate degree completion in the context of several risk factors for attrition. The population for this study was the cohort of full-time, bachelor's degree-seeking undergraduate students who entered the University of Central Florida as first-time-in-college (FTIC) students in the fall semester of 1999. Group One (Co-op Students) consisted of full-time FTIC students who successfully participated in the University of Central Florida Cooperative Education program and Group Two (Non-Participants) included full-time FTIC students with at least 20 credit hours completed and consistent grade point averages of at least 2.5 who did not participate in the University of Central Florida Cooperative Education program. The additional parameters on the Non-Participant group were included to control for any potential differences between the two groups due to increased requirements for participation in the co-op program. The two groups arrived at the University of Central Florida with nearly identical high school grade point averages and standardized test scores, and also were remarkably similar in age, ethnic composition, and college at entry. Results indicated that students who graduated within four years seemed to do so regardless of co-op participation, but for those who took longer, there was a correlation between co-op and degree completion. There was also some evidence to suggest that internships are associated with degree completion as well. Further, some of the known risk factors for attrition (lower high school grade point average, male gender, and non-White/non-Asian ethnicity) may be mitigated by the student's participation in their institution's co-op program, though additional research in this area is suggested.
Ed.D.
Department of Educational Research, Technology and Leadership
Education
Educational Leadership EdD

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12

Ousley, Chris. "A Geographic-Information-Systems-Based Approach to Analysis of Characteristics Predicting Student Persistence and Graduation." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/194256.

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This study sought to provide empirical evidence regarding the use of spatial analysis in enrollment management to predict persistence and graduation. The research utilized data from the 2000 U.S. Census and applicant records from The University of Arizona to study the spatial distributions of enrollments. Based on the initial results, stepwise logistic regression was used to identify spatially associated student and neighborhood characteristics predicting persistence and graduation.The findings of this research indicate spatial analysis can be used as a valuable resource for enrollment management. Using a theoretical framework of the forms of capital and social reproduction, cultural and social capital characteristics were found to influence persistence at statistically significant levels. Most notably, the social capital proxy of neighborhood education levels, and the cultural capital proxies of the number of standardized tests a student has taken, and when the application for admission is submitted all significantly influenced a student's probability to persistence and graduate. When disaggregating by race and ethnicity, resident Hispanic students from highly Hispanic neighborhoods were found to persist at higher levels in the first year of college attendance. Also, resident Native Americans were found to have a higher probability to persist when evidencing cultural capital characteristics. Since spatially based student and neighborhood characteristics can be quantified and mapped, target populations can be identified and subsequently recruited, resulting in retention-focused admissions.

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13

Wong, Chin Han. "An analysis of factors predicting graduation of students at Defense Language Institute Foreign Language Center." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FWong.pdf.

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14

PEREIRA, FRANCISCO COIMBRA CARNEIRO. "PREDICTIVE MODELS FOR STUDENT ATTRITION IN PRIVATE GRADUATION: AN APPLICATION OF MACHINE LEARNING TO RELATIONSHIP MARKETING MANAGEMENT." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32553@1.

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Perdendo em média mais de 20 por cento da base de alunos todo semestre, a evasão de alunos no ensino superior privado representa um desafio para a gestão dessas instituições. Diferentes abordagens são utilizadas para combater este problema. Para a gestão de marketing de retenção, a identificação dos alunos é o primeiro passo necessário para aplicar uma estratégia de interação personalizada. Nesse sentido, este trabalho apresenta uma metodologia quantitativa para classificação de risco de evasão de alunos ativos. Baseado em dados históricos de alunos que evadiram ou se formaram, modelos gerados por algoritmos de machine learning foram calculados e comparados e, na sequência, utilizados para classificar alunos ativos. Por fim, estimou-se o lifetime value desses alunos para auxiliar na definição de estratégias de retenção.
Losing more than 20 percent of its students each semester, the student attrition in private graduation courses challenges its institutions management. Different approaches to address this problem have been used. To retention marketing management the identification of students is the first necessary step to apply a personalized interaction strategy. In this sense, this work uses a quantitative methodology to classify its students by risk of attrition. Based in historic data of former students of an institution, models were generated by machine learning algorithms and its results compared. Then they were used to classify active students in the educational institution. Afterwards, their lifetime value were estimated in order to help in the definition of retention strategies.

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15

Frei, Autumn Michelle. "Predicting Successful Drug Court Graduation: Exploring Demographic and Psychosocial Factors among Medication-Assisted Drug Court Treatment Clients." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5022.

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The purpose of this study was to examine the influence of psychosocial factors on drug court graduation among a medication assisted treatment drug court population. The extant research identifies drug courts as effective in reducing recidivism and relapse rates; however meta-analyses of the drug court literature reveal that there is little explanation as to why drug courts are effectual and especially for whom. This study examined trauma, mental health, and social support to determine predictive psychosocial factors of drug court participants while controlling for certain demographics. The analyses showed that social support was predictive, but failed to identify trauma or mental health correlates. Policy implications are discussed.

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16

Knight, Melissa. "Accelerated Online and Hybrid RN-to-BSN Programs: A Predictive Retention Algorithm." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6345.

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Predicting retention and time to graduation within accelerated online and a hybrid RN-to-BSN programs are significant elements in leveraging the pipeline of qualified RNs with BSN degrees, but the literature lacks significant accounts of retention and time to graduation outcomes within these programs and predictive algorithm developments to offset high attrition rates. The purpose of this study was to quantitatively examine the relationships between pre-entry attributes, academic integration, and institutional characteristics on retention and time to graduation within accelerated online RN-to-BSN programs in order to begin developing a global predictive retention algorithm. This study was guided by Tinto's theories of integration and student departure (1975, 1984, 1993) and Rovai's composite persistence model. Retrospective datasets from 390 student academic records were obtained. Findings of this study revealed pre-entry GPA, number of education credits, enrollment status, 1st and 2nd course grades and GPA index scores, failed course type, size and geographic region, admission GPA standards, prerequisite criteria, academic support and retention methods were statistically significant predictors of retention and timely graduation (p <.05). A decision tree model was performed in SPSS modeler to compare multiple regression and binary logistic regression results, yielding a 96% accuracy rate on retention predictions and a 46 % on timely graduation predictions. Recommendations for future research are to examine other variables that may be associated with retention and time to graduation for results can be used to redevelop accurate predictive retention models. Having accurate predictive retention models will affect positive social change because RN-to-BSN students that successfully complete a BSN degree will impact the quality and safety of patient care.

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17

Geltz, Rebecca L. "Using Data Mining to Model Student Success." Youngstown State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709.

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18

Owen, John Alexander. "The multivariable relationship among student characteristics at medical school interview, admissions committee members' predictions of career plans, and student career plans at graduation /." Diss., 2000. http://wwwlib.umi.com/dissertations/fullcit/9424477.

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