Determining Academic, Background, and Financial Predictors of Community College First Year Retention using Data Mining Techniques

Even with extensive retention research dating from the 1960s, community colleges still struggle to identify the reasons why students do not return to college. Data mining has allowed these retention models to evolve to identify new patterns among student populations and variables. The purpose of this study was to create a predictive model for student retention using background, academic, and financial factors serving as a guide for other community colleges to use when investigating institutional retention. Four different data mining models (neural networks, random forest trees, support vector machines, and logistic regression) identified significant factors for retention. The models were compared to identify if one outperformed the others on five different evaluation metrics. The number of credit hours was consistently the most important variable in retention. In addition, the interactions between the number of credit hours, GPA, and financial aid variables were significant in student retention in their first year. The interaction between GPA, financial aid variables, and the number of remedial hours was also crucial for the first-year retention. There were no consistent variables among the retention models that can predict students' nonretention in the first year of their college career. Many background predictors (age, gender, race, or ethnicity) were not significant in predicting retained or nonretained students. The comparison of the retention models found the random forest model had the best performance for accurately classifying the nonretained and retained students overall and the retained students individually. Keywords: Retention, Community College, Data Mining, Academic Factors, Background Factors, Financial FactorsChapter I: INTRODUCTION 1 -- Statement of the Problem 6 -- Purpose of the Study 6 -- Research Questions 7 -- Research Methodology 7 -- Significance of the Study 10 -- Theoretical Basis of the Study 11 -- Limitations of the Study 13 -- Definition of Terms 14 -- Organization of the Study 17 -- Chapter II: LITERATURE REVIEW 19 -- Community College Populations 20 -- Community College Enrollment Trends 22 -- Community College Funding 23 -- Community College Retention 24 -- Bean and Metzner’s Retention Model 25 -- Importance of Individualized Retention Models 27 -- Retention Variables 28 -- Background Variables 28 -- Age 29 -- Gender 31 -- Race or Ethnicity 33 -- High School GPA 36 -- Academic Factors 37 -- College GPA 38 -- Online Courses 40 -- Remedial Courses 44 -- Number of Courses Completed 47 -- Financial Aid Factors 50 -- Amount of Financial Aid Awarded 52 -- Amount of Financial Aid Paid 53 -- FASFA Completion 55 -- Introduction of Data Science and Big Data 59 -- Data Mining 61 -- Educational Data Mining 62 -- Classifiers 63 -- Cross Validation Methods 64 -- Decision Trees and Random Forest Trees 65 -- Support Vector Machines (SVM) 68 -- Neural Network 72 -- Logistic Regression 74 -- Interpretation of Binary Classifier Models 76 -- Evaluation Metrics for Comparing Classifier Models 77 -- Accuracy, Sensitivity, and Specificity 78 -- F1-Scores 78 -- Receiver Operating Characteristic (ROC) Curves 78 -- Validation of Evaluation Metrics 80 -- Summary 80 -- Chapter III: METHODOLOGY 83 -- Research Design 83 -- Participants 85 -- Instrumentation 87 -- Data Collection 88 -- Data Analysis 88 -- Inferential Statistics 91 -- Random Forest 92 -- Supported Vector Machine (SVM) 93 -- Neural Network 93 -- Logistic Regression 93 -- Summary 95 -- Chapter IV: RESULTS 97 -- Demographic Characteristics for Individual Cohorts 98 -- Descriptive Statistics for Students 100 -- Correlation Coefficients for Students 102 -- Categorical Variable Analysis of Combined Cohorts 104 -- Missing Data Analysis of Combined Cohorts 105 -- Cross Validation Method 106 -- Outliers and Normality of Combined Cohorts 106 -- Outlier Capping, Transformation, and Normalization 108 -- Research Question 1 111 -- Random Forest 112 -- Support Vector Machine with Polynomial Kernel 118 -- Support Vector Machine with Radial Kernel 125 -- Neural Network 132 -- Logistic Regression 139 -- Comparison of Variable Importance 150 -- Research Question 2 151 -- Random Forest 153 -- Support Vector Machine with Polynomial Kernel 155 -- Support Vector Machine with Radial Kernel 157 -- Neural Network 159 -- Logistic Regression 161 -- Overall Model Comparison with ROC Curves 163 -- Inferential Tests for Model Comparison 165 -- Summary 173 -- Chapter V: SUMMARY, DISCUSSION, and CONCLUSIONS 176 -- Overview of the Study 177 -- Related Literature 177 -- Classification Models 178 -- Individual and Sector-based Models 178 -- Predictive Factors 178 -- Methodology 180 -- Participants 181 -- Variables Studied 181 -- Background Factors 181 -- Academic Factors 182 -- Financial Factors 183 -- Procedures 183 -- Summary of Findings 184 -- Research Question 1 184 -- Research Question 2 189 -- Discussion of Findings 191 -- Research Question 1 191 -- Research Question 2 193 -- Limitations of the Study 194 -- Implications for Future Research 197 -- Conclusions 198 -- REFERENCES 201 -- APPENDIX A: R Code for Modeling Building and Variable Importance 230 -- APPENDIX B: R Code for Inferential Statistics Tests 259 -- APPENDIX C: Institutional Review Board Protocol Exemption Report 264 -- APPENDIX D: Data Sharing Agreement 266Brockmeier, Lantry L.Bochenko, Michael J.Kim, DaesangEd.D.Education in Leadershi

Introduction to Human Development (GHC)

This Grants Collection for Introduction to Human Development was created under a Round Nine ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/psychology-collections/1023/thumbnail.jp

Elementary Statistics (GHC)

This Grants Collection for Elementary Statistics was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/mathematics-collections/1039/thumbnail.jp

FEA testing the pre-flight Ariel primary mirror

Ariel (Atmospheric Remote-sensing Infrared Exoplanet Large-survey) is an ESA M class mission aimed at the study of exoplanets. The satellite will orbit in the lagrangian point L2 and will survey a sample of 1000 exoplanets simultaneously in visible and infrared wavelengths. The challenging scientific goal of Ariel implies unprecedented engineering efforts to satisfy the severe requirements coming from the science in terms of accuracy. The most important specification – an all-Aluminum telescope – requires very accurate design of the primary mirror (M1), a novel, off-set paraboloid honeycomb mirror with ribs, edge, and reflective surface. To validate such a mirror, some tests were carried out on a prototype – namely Pathfinder Telescope Mirror (PTM) – built specifically for this purpose. These tests, carried out at the Centre Spatial de Liège in Belgium – revealed an unexpected deformation of the reflecting surface exceeding a peek-to-valley of 1µm. Consequently, the test had to be re-run, to identify systematic errors and correct the setting for future tests on the final prototype M1. To avoid the very expensive procedure of developing a new prototype and testing it both at room and cryogenic temperatures, it was decided to carry out some numerical simulations. These analyses allowed first to recognize and understand the reasoning behind the faults occurred during the testing phase, and later to apply the obtained knowledge to a new M1 design to set a defined guideline for future testing campaigns

Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

OpenStax Introductory Statistics Ancillary Materials

These ancillary materials were created for use in Elementary Statistics courses as a result of a Round 15 Affordable Learning Georgia Mini-Grant

Introduction to Human Development (GHC) (Open Course)

This open course for Introduction to Human Development is an adaptation of PsychologyWiki materials and was created under a Round Nine Textbook Transformation Grant. Authors\u27 Description: In our transformation of PSYC 2103 Human Development we decided to divide the content into three units. Unit 1: Overview, History and Biological Beginnings Unit 2: Early Childhood to Adolescence Unit 3: Young Adulthood to Death Each unit includes: Learning objectives Things to consider: questions students should be thinking about while engaging with the content PowerPoint Presentation Readings from a variety of open text books Activities Supplemental readings and videos If you have questions or would like access to the question/test bank please contact either Elizabeth Dose, [email protected] Katie Bridges, [email protected]

Elementary Statistics (GHC) (Open Course)

This open course for Elementary Statistics was created through a Round Ten Textbook Transformation Grant: https://oer.galileo.usg.edu/mathematics-collections/39/ The open course contains ancillary materials for OpenStax Introductory Statistics: https://openstax.org/details/books/introductory-statistics Included in the course are introductions to each lesson, lecture slides, videos, and problem questions. Topics include: Types of Data Sampling Techniques Qualitative Data Frequency Distributions Descriptive Statistics Variation and Position Confidence Intervals Hypothesis Testing Chi-Square Goodness of Fit Linear Regression Variance ANOV

Open Mathematics in Action Pilot

Mathematics faculty from USG institutions implemented a set of low-cost and open educational resources and affordable materials in one or more of the math courses College Algebra (MATH 1111), Trigonometry (MATH 1112), Precalculus (MATH 1113), and Probability and Statistics (MATH 2112), supported by previous Textbook Transformation Grants project participants and co-leaders Dr. German Vargas and Dr. Victor Vega along with Affordable Learning Georgia staff. These Project Leads have been working with CCGA faculty to address the high cost of mathematics required materials through the adoption of open textbooks and low-cost online homework systems. Their initial work was supported by a Round Two ALG Textbook Transformation Grant. Through Open Mathematics in Action, Affordable Learning Georgia and USG faculty expanded on the transformational success of the College of Coastal Georgia’s Mathematics Department by providing faculty from five institutions with the support, resources, and training needed to implement these OER in these mathematics course sections.https://oer.galileo.usg.edu/mathematics-collections/1050/thumbnail.jp