Keywords
Identifying At-Risk Students, Machine Learning, XGBoost, Statistics, Undergraduate
Abstract
The elevated rates of failure, dropout, and withdrawal (FDW) in introductory statistics courses pose a significant barrier to students' timely graduation from college. Identifying actionable strategies to support instructors in facilitating student success by reducing FDW rates is paramount. This thesis undertakes a comprehensive approach, leveraging various machine learning algorithms to address this pressing issue. Drawing from three years of data from an introductory statistics course at one of the largest universities in the USA, this study examines the problem in depth. Numerous predictive classification models have been developed, showcasing the efficacy of machine learning techniques in this context. Actionable insights gleaned from these statistical and machine learning models have been consolidated, offering valuable guidance for instructors. Moreover, the complete analytical framework, encompassing data identification, integration, feature engineering, model development, and report generation, is meticulously outlined. By sharing this methodology, the aim is to empower researchers in the field to extend these approaches to similarly critical courses, fostering a more supportive learning environment. Ultimately, this endeavor seeks to enhance student retention and success, thereby contributing to the broader goal of promoting timely graduation from college.
Completion Date
2024
Semester
Summer
Committee Chair
DR. Wang, Morgan C.
Degree
Master of Science (M.S.)
College
College of Sciences
Department
Statistics and Data Science
Degree Program
Statistics - Data Science
Format
application/pdf
Identifier
DP0028534
URL
https://purls.library.ucf.edu/go/DP0028534
Language
English
Release Date
8-15-2024
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Campus Location
Orlando (Main) Campus
STARS Citation
Abbaspour Tazehkand, Shahabeddin, "Enhancing Student Graduation Rates by Mitigating Failure, Dropout, and Withdrawal in Introduction to Statistical Courses Using Statistical and Machine Learning" (2024). Graduate Thesis and Dissertation 2023-2024. 329.
https://stars.library.ucf.edu/etd2023/329
Accessibility Status
Meets minimum standards for ETDs/HUTs