Enhancing Student Graduation Rates by Mitigating Failure, Dropout, and Withdrawal in Introduction to Statistical Courses Using Statistical and Machine Learning - Research thesis

Aug 4, 2026

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Elevated rates of failure, dropout, and withdrawal (FDW) in introductory statistics courses hinder students’ timely college graduation. This thesis develops an actionable strategy for instructors by applying multiple machine learning algorithms to identify drivers of FDW and to predict student outcomes. Using three years of data from an introductory statistics course at a major U.S. university, numerous predictive classification models demonstrate the value of learning-based methods. The work consolidates statistical and machine learning insights and documents a complete pipeline covering data identification, integration, feature engineering, model development, and report generation to support broader adoption.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages85

Reading Time3 h 34 min

AccessFree to download

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University of Central Florida STARS Graduate Thesis and Dissertation 2023-2024 2024 Enhancing Student Graduation Rates by Mitigating Failure, Dropout, and Withdrawal in Introduction to Statistical Courses Using Statistical and Machine Learning Shahabeddin Abbaspour Tazehkand Find similar works at: [https://stars.library.ucf.edu/etd2023](https://stars.library.ucf.edu/etd2023) University of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu) This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Graduate Thesis and Dissertation 2023-2024 by an authorized administrator of STARS. For more information, please [contact STARS@ucf.edu](contact STARS@ucf.edu). 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](https://stars.library.ucf.edu/etd2023/329) ENHANCING STUDENT GRADUATION RATES BY MITIGATING FAILURE, DROUPOUT, AND WITHDRAWAL IN INTRODUCTION TO STATISTICAL COURSES USING STATISTICAL AND MACHINE LEARNING by SHAHABEDDIN ABBASPOUR TAZEHKAND B.Sc. Sharif University of Technology, 2012 M.Sc. Allameh Tabataba’i University, 2018 Ph.D. University of Central Florida, 2022 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Sciences in the School of Statistics and Data Science in the College of Sciences at the University of Central Florida Orlando, Florida Summer Term 2024 Major Professor: Morgan C. Wang © 2024 Shahabeddin Abbaspour Tazehkand 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. This thesis is dedicated to the Woman, Life, Freedom movement in Iran To Mahsa Amini, Kian Pirfalak, Nika Shakarami, Khodanur Lojei, Sarina Esmailzaeh, Abolfazl Adinezadeh, Hadis Najafi, Mohsen Shekari, Armita Geravand, Seyyed Mohammad Hosseini To those who lost their lives in the movement, their families and the survivors To Iranian schoolgirls who were poisoned in their schools and classrooms To Iranian students who cannot access education To Afghan female students who are being denied access to education under Islamic Theocracy To children we lose every day in meaningless wars and conflicts And to those children and students who don’t have a voice and those who strive to give them one ACKNOWLEDGEMENTS I’d like to express my heartfelt gratitude to Dr. Morgan C. Wang, my advisor,

FAQs

What problem does the thesis address in introductory statistics courses?

The thesis focuses on high rates of failure, dropout, and withdrawal (FDW), which prevent students from graduating on time.

How does the thesis support instructors to reduce FDW rates?

It builds and evaluates multiple predictive classification models using machine learning, then consolidates actionable insights derived from those models.

What does the analytical workflow include?

The thesis outlines a full framework covering data identification, integration, feature engineering, model development, and report generation.

Aug 4, 2026

Upload by Violet