In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Decision-making, a key factor of organizational performance, is based on information retrieved from processing raw data. As businesses and consumers are shifting toward digital channels, more and more data is being generated through digital services and electronic devices. Big Data is argued to have significant benefits to businesses, and yet data…

Data-based decision-making has been a frequently used policy and practice intervention used in schools to help inform the decision-making processes of educational practitioners, with the aim of improving student outcomes. Interim benchmark assessments are designed by commercial test developers to support educators in this framework. In fact, the…

Mixed methods research collects, analyzes, and integrates rigorous qualitative and quantitative methods to gain a deeper understanding of a phenomenon than would be gained by using either method alone. Integration is one of the key features of mixed methods research and consists of mixing qualitative and quantitative research in a systematic way…

School districts have recognized the undeniable power of using data to enhance performance. As a result, teachers are now required to use data to prepare for critical state assessments and to improve district levels. These assessments are crucial in determining the school's rating by the state and can even result in a state takeover if scores are…

This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding…

This dissertation address two significant challenges in the causal inference workflow for Big Observational Data. The first is designing Big Observational Data with high-dimensional and heterogeneous covariates. The second is performing uncertainty quantification for estimates of causal estimands that are obtained from the application of black box…

This qualitative case study sought to discover how school-level data teams can intentionally use effective data practices to identify and implement high-leverage interventions that support all students, including Black and Latinx boys, in attaining the necessary academic requirements for high school graduation. The researcher analyzed data from…

Institutional researchers use skills from their diverse backgrounds to collect, analyze and report data about their institutions to stakeholders representing various interests and levels of data literacy. However, there is little research into how these professionals process data and none into what aspects are important to institutional…

The purpose of this quantitative causal-comparative study is an empirical examination of the differences in business intelligence capability and the value and competitive advantage of mid-level higher education academia professionals from community colleges, four-year public, and four-year private institutions within the United States.…

Data collection and analyzation practices for English language development services are scarcely found in research, but needed in the subgroup of minority students commonly known as English language learners (Wiseman & Bell, 2021). Wiseman and Bell (2021) identified ELLs as one of the most under-documented student subgroups in the American…

Practical work involving laboratory experiments is agreed upon to be an essential component of secondary science education. Practical work encompasses a broad range of activity types. The different forms of practical work are not equal in terms of cognitive demand and learning benefit. Inquiry-based investigations provide experience of cognitive…

This paper seeks to empirically validate a sector agonistic instrument that measures the perceived critical success factors in data governance. Twelve constructs (Leadership and Management Commitment; Leadership and Management Alignment; Executive Sponsorship; Robust Data Governance Strategy; Change Management; Training and Education; Governance…

All chemistry students must develop competency in analyzing and making sense of data. However, there are many difficulties that chemistry students may experience while analyzing data. Many students may not use relevant prior knowledge to aid in making sense of data, or they may not form conclusions using all the data provided. Additionally, prior…

There has been a great deal of news, discussion, and outcry regarding the lack of diversity in science, technology, engineering, and math (STEM) related fields, be it industry or academia. On many comparative lists regarding STEM students and workers, African American males are an unacceptably low percentage of the population. The problem is quite…