Machine learning (ML) techniques have been successfully applied to a wide array of applications. This dissertation aims to take application data handling into account when developing ML-based solutions for real-world problems through a holistic framework. To demonstrate the generality of our framework, we consider two real-world applications: spam…

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…

The last decade witnessed a spectacular rise in the volume of available textual data. With this new abundance came the question of how to analyze it. In the social sciences, scholars mostly resorted to two well-established approaches, human annotation on sampled data on the one hand (either performed by the researcher, or outsourced to…

Data-based decision making (DBDM) is a process of using student data to inform instructional decisions and intensify intervention for students whose data indicate inadequate academic and behavioral progress. Data teams, an important structure for DBDM, are a collaborative group of school faculty who meet to systematically analyze student data,…

This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test…

While the co-production of knowledge through community-engaged research is intended to be a reciprocally beneficial process, academic institutions have often devalued community expertise by treating community organizations as subjects rather than co-creators of knowledge. Drawing from Black Feminist Epistemology, this ethnographic study examines…

Fieldwork is an important component of data collection in environmental sciences and other related disciplines. Sensitive to the ways in which field based environmental sciences (FBES) research is often unsafe and lacks inclusivity, we explore findings from a mixed methods study that identified barriers to inclusion and overlooked risks to safety…

This case study examines the critical role school leaders play in teacher data use. Aligned with previous literature, we find that educators perceive high levels of support for data use and prefer formative data; however, observations showed a data use method unlike previously described inquiry cycles. From these findings we propose a new data use…

Managerial accounting has traditionally played an important role in analyzing data, estimating performance, and offering suggestions. Modern management accountants face evolving expectations, such as contributing strategically to long-term goals and communicating information using visualizations. We specifically focus on how managerial accounting…

This study examines the potential to use keystroke logs to examine differences between authentic writing and transcribed essay writing. Transcribed writing produced within writing platforms where copy and paste functions are disabled indicates that students are likely copying texts from the internet or from generative artificial intelligence (AI)…

Data--their collection, analysis and use--have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research…

This study seeks to understand the big-data analytics readiness of four-year public and private higher education institutions (HEIs) in North Carolina. The higher education landscape has been experiencing unprecedented challenges including declines in enrollment, graduation rates, and student retention rates. Coupled with cuts in funding at the…

Across continental Europe, educational research samples are often divided by 'migrant background', a binary variable criticized for masking participant heterogeneity and reinforcing exclusionary norms of belonging. This study endorses more meaningful, representative, and precise research by offering four guiding questions for selecting relevant,…

This brief explores data science methods that workforce programs can use to predict participant success. With access to vast amounts of data on their programs, workforce training providers can leverage their management information systems (MIS) to understand and improve their programs' outcomes. By predicting which participants are at greater risk…

As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational…