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,…

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…

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…

The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…

Due to educational policy, data use in schools is a widespread method of school improvement, yet results remain inconsistent. Across the literature, the school leader is a key determinant of the effectiveness of data use. This study sought to shed light on the role of the school leader in data use through a mixed methods case study. The study…

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…

Educational data mining focuses on exploring increasingly large-scale data from educational settings, such as Learning Management Systems (LMS), and developing computational methods to understand students' behaviors and learning settings better. There has been a multitude of research dedicated to studying the student learning process, leading to…

Since the introduction of the specific learning disability (SLD) classification in federal special education legislation, there has been considerable ambiguity and controversy about the criteria that should be used in the determination of SLD (Kavale & Flanagan, 2007; Zumeta et al., 2014). Various models have been introduced to identify…

Research conducted at scientifically significant sites produces an abundance of important and highly valuable data. Yet, though sites are logical points for coordinating the curation of these data, their unique needs have been under supported. Previous studies have shown that two principal stakeholder groups -- scientific researchers and local…

This three-article dissertation considers the pedagogical practices for developing statistically literate students and teaching data-driven decision-making with the goal of preparing students for civic engagement and improving student achievement. The first article discusses a critical review of the literature on data-driven decision-making…