Download or read book Development of a Predictive Model for First year Student Retention at Mercyhurst College written by Marc Gattie and published by . This book was released on 2009 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Development of a Predictive Model for One year Freshman Retention Rate written by Huey W. Antley and published by . This book was released on 1998 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Strategically Using University Student Records to Build a Student Retention Model written by Emma J. Crabtree and published by . This book was released on 2019 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, Strategic Enrollment Management (SEM) initiatives have become integral in the programs that colleges and universities develop to recruit and retain students. With 73.0% of full-time freshmen at Wichita State University in 2017 returning for their second year of study in fall of 2018, there is a need to implement interventions designed to identify and provide services to students at-risk of not being retained. Based on research of academic attrition and models of student retention, a conceptual model of student retention was developed. Wichita State University has developed collaborations across multiple SEM offices to increase their capacity to strategically use student record data to create data-driven programs and policies. This study utilized this capacity to develop a predictive model of student retention for three of the university's main student populations: first-time-in-college students, transfer students, and returning adult students. The availability of student data for each population is impacted by university admissions and data monitoring practices, requiring the conceptual model to be tailored to each student group. Bivariate comparisons between the students who were retained and who were not retained in each population revealed significant differences between the groups, so a logistic regression was used to predict retention risk. The logistic regression equations for each population were able to predict student retention with at least 70% accuracy. Implications, limitations, and suggestions for future research will be discussed.
Download or read book Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling written by Kevin E. K. Chai and published by . This book was released on 2015 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist universities by proactively supporting and retaining these students as their situations and risk change over time. The study evaluated different models for predicting student attrition at four different time periods throughout a semester study period: pre-enrolment, enrolment, in-semester and end-of-semester models. A dataset of 23,291 students who enrolled in their first semester between 2011-2013 was extracted from various data sources. Three supervised machine learning techniques were tested to develop the predictive models: logistic regression, decision trees and random forests. The performance of these models were evaluated using the precision and recall metrics. The model achieved the best performance and user utility using logistic regression (67% precision, 29% recall). A web application was developed for users to visualise and interact with the model results to assist in the targeting of student intervention responses and programs. [For the full proceedings, see ED562093.].
Download or read book An Empirical Analysis of Factors that Influence the First Year to Second Year Retention of Students at One Large Hispanic Serving Institution hsi written by Steven Lamar Wilkerson and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this study was to identify how input and environmental factors impact first-to-second year retention of undergraduate students at a large Hispanic Serving Institution (HSI). An additional purpose of the study was to determine the usefulness of the Astin Typology as a predictive factor for student retention. The sample for the study was 1,296 first-year students enrolled at the University of Texas at San Antonio during the 2002, 2003, and 2004 academic years. Data used for the study included student responses to the Cooperative Institutional Research Program (CIRP): Freshman Survey (to identify each participant0́9s Astin type), gender, ethnicity, SAT scores, rank in high school class, first-generation status, financial need, first-semester residence, entry-college, semester credit hours attempted, academic course difficulty, participation in Supplemental Instruction, and enrollment in a first-year seminar course. Both descriptive and univariate statistics were used to describe the sample population, as well as the similarities and differences found to exist among the seven Astin types. Three separate logistic regression analyses organized by Astin0́9s I-E-O framework were conducted to develop a predictive model for retention from the first-to-second year of college. Subsequent analyses were conducted to identify the specific factors that were useful for predicting retention for each of the seven Astin types. The major findings of this study were: 0́Ø The most frequent Astin type identified within the sample population was Status Striver 0́Ø The model that included both Input and Environmental factors was the most accurate model for predicting retention 0́Ø Students who were classified as Hedonist, Status Striver, and Uncommitted were less likely to be retained at this institution when all other input and environmental factors were controlled. 0́Ø Environmental factors were most useful for predicting retention, in particular, semester credit hours attempted that had an inverse relationship with retention for all Astin types 0́Ø First-generation status, financial need, SAT score were not useful for the prediction of retention 0́Ø First-year seminar course enrollment and participation in Supplemental Instruction had a positive impact on retention This study provided evidence that the Astin typology is viable as a means of retention among college student populations.
Download or read book Predicting and Improving First Year Engineering Student Retention Through Lean Thinking and Quality Management Concepts written by Thomas Bereza and published by . This book was released on 2017 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: While the percentage of undergraduate engineering degrees awarded has increased over the past decade, it has been outpaced by the overall growth in bachelor degree attainment. With this, the amount of enrollment in engineering programs has increased, but still a significant number of engineering students choose to drop out or pursue other educational paths. Universities and policy makers are motivated to increase the retention of engineering students to graduation. This thesis explores the quantitative data that makes up a first year engineering student's profile. The data is used to develop an ordinal logistic regression model to predict 2nd year student retention. Ideas to improve retention are discussed with a focus of applying Lean Manufacturing techniques in conjunction with the proposed prediction model. Data from a college of engineering within a public land-grant research university is used to test for significance as indicators for freshman retention. Data used in this study is from 2010 and 2011 freshman engineering cohorts. Using collected student data, a prediction model is developed that assesses the probability of a first year engineering student either i) returning to engineering in their second year, ii) leaving engineering but remaining at the university, or iii) leaving the university altogether. Then, using concepts from lean manufacturing and quality management this prediction model is incorporated in a proposed engineering education quality system.This study creates a prediction model to identify students that are likely to be: retained in engineering, switch majors out of engineering, and drop out of the university. This prediction model is then incorporated into the proposed engineering education quality management system to assist with identifying; where and when students may not persist in engineering curriculum, and ideas to promote student persistence using the prediction results.
Download or read book Undergraduate Student Retention in Context written by Bradley C. Litchfield and published by . This book was released on 2013 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study examined the use of an institutionally-specific risk prediction model in the university's College of Education. Set in a large, urban, public university, the risk model predicted incoming students' first-semester GPAs, which, in turn, predicted the students' risk of attrition. Additionally, the study investigated advising practices within the College of Education via semi-structured interviews with the College's advising staff and a document analysis of students' advising notes in an attempt to find thematic links between undergraduate retention and usage of an advising center. Data were analyzed to determine the accuracy of the risk model in the College of Education. The results of this study are used to inform the College of Education's administration, faculty, and staff about the implications of risk prediction and to suggest potential treatments to increase retention rates. Furthermore, recommendations for future research are discussed for this study's institution and for the field of education.
Download or read book Predicting College Student Retention written by Eric L. Dey and published by . This book was released on 1989 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Factors Contributing to First Year Retention in Higher Education written by Sharon M. Young and published by . This book was released on 2016 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this study was to examine factors that may contribute to student retention in first year higher education students enrolled at a four-year public university in Texas. The study sought to answer the following research question: 1. To what degree are student factors--high school GPA, ethnicity, gender, first generation higher institution student status, SAT or ACT scores, socioeconomic status (SES), and financial aid eligibility (grants, loans, federal aid, scholarships)--related to first year retention at a college/university? This study used descriptive and logistic regression analyses from the Fall 2012-2013 school year data provided by the public university in Texas. Contributing factors (High School GPA, ethnicity, gender, first generation college student status, SAT or ACT scores, socioeconomic status (SES), and financial aid eligibility (grants, loans, federal aid, scholarships) were analyzed to determine the best predictor(s) of first year student retention for the Fall 2013-2014 school year at the public university. The study revealed that of the sample analyzed, 68.8% of the students were retained for the Fall 2013-2014 school year while the other 31.2% were not retained. The logistic regression revealed financial aid (grants, loans, scholarships) to be the most predictive variable for the retention of first year higher education students in 2013-2014.
Download or read book Survey of Best Practices in Student Retention written by Primary Research Group and published by . This book was released on 2016 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Mining a Peoplesoft Database to Assist in Developing Student Retention Interventions written by Greg Alan Đào Jonason and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Per a Bellwether Education Partners study (Aldeman, 2015, p. 8), "As of 2013, there were 29.1 million college dropouts versus 24.5 million Americans who dropped out with less than a high school diploma. In pure, raw numbers, college dropouts are a bigger problem than high school dropouts." Conceptually this study is framed within theories of student persistence/attainment and the Knowledge Discovery Process (KDP). This research study developed first time in college (FTIC) and transfer (TRAN) student graduation prediction models by using decision trees and support vector machine (SVM) classification algorithms and identified attributes of students who graduate and do not graduate. Data was collected from the University of Houston's data warehouse to provide detailed student academic records as the basis for quantitative analysis. The data set included male and female undergraduate students enrolled in the College of Education's Teaching & Learning Program from 2000-2012 at the University of Houston. These findings may contribute to improving student success and subsequent graduation rates in the College of Education and other colleges across the campus.
Download or read book First year Student Retention written by Derek A. Jackson and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigated the use of the MAP-Works[superscript]TM program that is designed to help retain first-year students by identifying the level of retention risk for each student early in their first semester and communicating this risk to key university faculty and staff. The participants for this study were all first semester freshman students enrolled during the academic years 2012 and 2013. This study sought to determine if the MAP-Works[superscript]TM program and resulting intervention were effective in predicting the retention of high-risk first semester freshman students to their second semester and second year. The data analysis for this study used quantitative data analysis methods. The first and second research questions asking which of the factors were significant in predicting retention were answered using independent samples t-tests. The third research question asking if the intervention was significant was answered using a 2x2 Chi-square test for independence. The fourth and final research question asked which of the factors contributed the most in predicting retention was answered using a direct (binary) logistic regression analysis. This study found for high-risk domestic students Cumulative GPA, Socio-Emotional, Test Anxiety, Peers, Homesickness: Distressed, Academic Integration, Social Integration and Environment were able to be associated significantly with retention from fall-to-spring semester. For international students GPA, Self-Efficacy and Self-Discipline were able to be associated significantly with retention. The study showed for fall-to-fall retention for domestic students that cumulative GPA, Socio-Emotional, Communication, Analytical, Social Integration and On-Campus Living Social were significant. The research found that the intervention conducted by their direct connects for high-risk domestic students was significant for fall-to-fall retention. The logistic regression analysis showed for domestic students that Cumulative GPA, Financial Means, Socio-Emotional, and ACT Composite score were significant for fall-to-fall retention. The strongest predictor of retention was Cumulative GPA followed by Socio-Emotional, Financial then ACT Composite score. The regression analysis for high-risk international students showed that Cumulative GPA, Gender, and Student Residence were significant for fall-to-fall retention. The strongest predictor of retention was cumulative GPA, Gender (Female) and Student Residence (Off Campus).
Download or read book Predicting Retention of First year College Students written by Jennifer Bebergal and published by . This book was released on 2003 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing an ADN Predictive Model of Student Retention written by Judith E. Miller and published by . This book was released on 2006 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Predictive Model for Freshman Retention written by Gary R. McDaniel and published by . This book was released on 2000 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Relationship Between First year Student Retention Noncognitive Risk Factors and Student Advising written by R. David Roos and published by . This book was released on 2012 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is well established that such student precollege cognitive measures as high school GPA and test scores (ACT, SAT) have a certain predictive value in student retention. While research is replete with evidence of the value of student advising in a college's retention strategy, there is a gap in the literature on the impact of using noncognitive survey information by advisors to better target student deficiencies. The primary goal of this study was to explore the relationship between retention and exposure to noncognitive risk factor information for students and advisors. One thousand fifty-four freshmen students enrolled in a first-year experience (FYE) course at Dixie State College were given the Student Strengths Inventory (SSI) survey that measures six different noncognitive risk factor variables. By using a regression discontinuity design, students were initially divided into two sample groups using an index score generated by combining the high school GPA and ACT (or equivalent) test score. Students who fell below the cutoff point were further subdivided by random sampling into three groups: (a) students who received their survey results with no further action, (b) students selected for general advisement, and (c) students selected for targeted advisement using the survey results. When comparing the retention rates from fall semester 2009 to fall semester 2010, the retention rates varied as predicted by the researcher; however, these differences in retention could not be attributed to the usage of the survey with one exception: when the treatment group was filtered only to include first-generation students, usage of the survey results was statistically significant in contributing to a 62% retention rate, the highest of any of the sample groups studied.
