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…

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…

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…

Many departments seek to improve their equity, diversity, and inclusion using data-informed methods; however, not much is known about how closely department leadership aligns their review practices with appropriate theory. This dissertation seeks to explore four areas: 1) how are equity, diversity, and inclusion are being conceptualized (implied…

The last decades have seen an unprecedented growth in the availability and accessibility of data, highly influenced by the ubiquity of digital media and the internet. As society contends with data's increasing impact on the nature of knowledge, communication, and privacy, it faces a pressing need for citizens who are intelligent producers and…

Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research…

Missing values that fail to be appropriately accounted for may lead to reduced statistical power, biased estimators, reduced representativeness of the sample, and incorrect interpretations and conclusions (Gorelick, 2006). The current study provided an ontological perspective of data manipulation by explaining how statistical results can…

Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

My dissertation comprises three studies designed to explore nuances of data use in education that presently confound the relationship between variation in teachers' practice of data use and student outcomes: (1) the relationships between different data sources within and across instructional practices; (2) the dimensionality of teachers' practice…

Most White people have learned to ignore the way that the social construct of race benefits them or harms people of Color, myself included. In this way, people take for granted that race is not real, but rather the invention of White supremacists. Moreover, researchers who are responsible for generating knowledge far too often depend on…

While some engineering students develop proper critical thinking skills, most engineering students do not. Engineers must critically think to tackle various problems on the job, but many schools do not properly instill critical thinking in engineering students. With engineers working on multiple tasks in little time, a knowledge of critical…

Multivariate count data is ubiquitous in many areas of research including the physical, biological, and social sciences. These data are traditionally modeled with the Dirichlet Multinomial distribution (DM). A new, more flexible Dirichlet-Tree Multinomial (DTM) model is gaining in popularity. Here, we consider Bayesian DTM regression models. Our…

Institutional researchers (IRs) at higher education institutions fulfill a crucial role in identifying, processing, and disseminating data to administrators. Identifying effective ways for leaders to make sense of data is essential to advancing the most critical institutional issues, including achieving equitable student outcomes. However, leaders…

As online learning platforms and computerized testing become more common, an increasing amount of data are collected about users. These data include, but are not limited to, response time, keystroke logs, and raw text. The desire to observe these features of the response process reflect an underlying interest in the cognitive processes and…

Sexual misconduct incidents remain a major problem on college and university campuses. Despite its prevalence, higher learning institutions are still grappling with establishing the best approach for addressing or minimizing sexual misconduct practices on their campuses. While previous scholars have explored Title IX compliance, it is not clear…