Teacher turnover continues to be a significant problem in the United States. Teacher turnover is expensive because it costs money to continue recruiting, hiring, and training new teachers to replace those leaving (Carver-Thomas & Darling-Hammond, 2017). Most important though, teacher turnover hurts student achievement and success (Sorensen…

In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…

This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and…

This dissertation examines the effectiveness of machine learning algorithms and feature engineering techniques for analyzing process data and predicting test performance. The study compares three classification approaches and identifies item-specific process features that are highly predictive of student performance. The findings suggest that…

Foundations of statistics research aims to establish fundamental principles guiding inference about populations under uncertainty. It is concerned with the process of learning from observations, notions of uncertainty and induction, and satisfying inferential objectives. The growing interest in predictive methods in high-stakes fields like…

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…

The Vortex School District (VSD) utilized CASE benchmark assessments as their accountability tool to measure students' progress on Tennessee's standardized testing. The researchers sought to determine the predictive relationship between math scores on CASE benchmark assessments and math scores on state-mandated standardized tests within the VSD.…

The Vortex School District (VSD) utilized CASE benchmark assessments as their accountability tool to measure students' progress on Tennessee's standardized testing. The researchers sought to determine the predictive relationship between math scores on CASE benchmark assessments and math scores on state-mandated standardized tests within the VSD.…

The Vortex School District (VSD) utilized CASE benchmark assessments as their accountability tool to measure students' progress on Tennessee's standardized testing. The researchers sought to determine the predictive relationship between math scores on CASE benchmark assessments and math scores on state-mandated standardized tests within the VSD.…

The purpose of the study was to investigate if commonly collected data correlates with student success at week eight of the first term for first time, full time, students. This is an ex-post facto study of student data. A Spearman's rho test was used to analyze the data for correlational analysis to determine if variables contained within…

The purpose of this study was to further investigate the relationship between the instruments identified by Kose and developed by Maras et al. (2013), Astramovich (2016), and Dimmitt et al. (2007) to provide validation to the proposed taxonomy of program evaluation competencies for school counselors. Theoretically, these instruments should be…

Educational Data Mining (EDM) is an emerging field that aims to better understand students' behavior patterns and learning environments by employing statistical and machine learning methods to analyze large repositories of educational data. Analysis of variable data in the early stages of a course might be used to develop a comprehensive…

With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and…

Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…

Dialogue act (DA) classification plays an important role in understanding, interpreting and modeling dialogue. Dialogue acts (DAs) represent the intended meaning of an utterance, which is associated with the illocutionary force (or the speaker's intention), such as greetings, questions, requests, statements, and agreements. In natural language…