This article reports on our experience of collecting language data from informants in video-conferencing settings under a research design originally developed with face-to-face interactions in mind. We had set out to investigate whether individual stylistic features persist in different modes of textual production and designed a complex set of…

In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the…

The National Youth in Transition Database (NYTD) snapshots offer a glimpse of National and State trends for young people who are receiving Independent Living Services through state child welfare programs. This Outcomes Data Snapshot shows statistical trends about who those young people are, their experiences and outcomes for those both in care and…

The multistage testing (MST) design has been gaining attention and popularity in educational assessments. For testing programs that have small test-taker samples, it is challenging to calibrate new items to replenish the item pool. In the current research, we used the item pools from an operational MST program to illustrate how research studies…

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,…

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…