Data science, as a discipline can be used in any area. However, in order to utilize data science techniques, data scientist must be taught domain knowledge, referred to as a partner discipline, in the area with which the techniques are to be utilized. Using a quantitative analysis of publicly available information and survey methodology, this…

The research is a descriptive correlational study that investigates new methods for the creation of innovation for institutions. The research addresses the need for organizations to generate a novel form of innovation for their competitiveness and survival. These novel methods in the generation of innovation include using an organization's big…

In this dissertation, I engage in three analytic cuts to think about/with a relational ontological orientation to data and data literacies/science education. The analysis focuses on the following question: What possibilities for teaching and learning about data are made possible when we attune to the relational, noisy, liminal, and material…

As data becomes an integral part of our lives, the general public faces the increasing need to actively engage with data to participate in daily activities, support personal goals, and understand social issues. Formal data science training, however, remains out of reach for most people and does not cater to their diverse needs related to data.…

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…

The field of Data Science has seen rapid growth over the past two decades, with a high demand for people with skills in data analytics, programming, statistics, and ability to visualize, predict from, and otherwise make sense of data. Alongside the rise of various artificial intelligence (AI) and machine learning (ML) applications, we have also…

Statistics & data science are growing, rapidly evolving, and increasingly important for an informed citizenry in a data-saturated world. In this dissertation, I address two central questions: (1) who is taking statistics? and (2) what are statistics courses teaching? I estimate that 920,000 US students take statistics in high school each year,…

Data science programming presents many challenges for programmers entering the field. Roughly, data science programming can be broken up into several activities: data wrangling, analysis, modeling, or visualization. Data wrangling is an important first step that involves cleaning and shaping tabular data--or dataframes--into a form amenable for…

This dissertation explores technological affordances in blended learning, their influence on the flexibility of statistics and data science curricula, and students' satisfaction with learning. While blended learning is often perceived as a flexible learning approach, its correlation with flexibility lacks substantial evidence in existing…

Data science encompasses the most prominent collection of methods for creating scientific knowledge in the 21st century. Currently, data scientists must navigate a wide-ranging and often incoherent ecosystem of tools, in addition to organizing sociotechnical interactions with colleagues across many fields of expertise. This predicament motivates…

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 research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of…

Regional comprehensive universities offer accessible and diverse undergraduate educational programs, while grappling with funding cuts and affordability. The study's first research question underscores the enduring importance of factors such as student characteristics, pre-college characteristics, and financial situations. The findings highlight…

The emerging field of data science has brought attention to how we teach statistics and data science (Bargagliotti et al., 2020; Franklin et al., 2007) and prepare the next generation of statistics and data science teachers (Franklin et al., 2013). To realize the full potential of statistics and data science, researchers have also called for using…

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…