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

Release Date

8-15-2024

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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