Since 2013, DaSy has tracked the status of Part C and Part B 619 state data systems to understand states' technical assistance needs, provide stakeholders with a national picture of what capacities state data systems have, and to track how those capacities are changing over time. Most recently, in spring of 2021, DaSy collected comprehensive…

Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D…

This article discusses how each of the papers in this special issue explored some combination of subject, audience, and data scientist perspectives with an eye toward helping students situate their relationship to data. Specifically, within the data scientist perspective, the papers examined a variety of ways in which students can relate to data…

Coding has typically been understood as an engineering practice, where the meaning of code has discrete boundaries as a technology that does precisely what it says. Multidisciplinary code studies reframed this technological perspective by positing code as the latest form of writing, where code's meaning is always partial and dependent on…

Data visualizations have proliferated throughout the COVID-19 pandemic to communicate information about the crisis and influence policy development and individual decision-making. In invoking exponential growth, mathematical modelling, statistical analysis, and the like, these data visualizations invite opportunities for mathematics teaching and…

Grounded within current reform recommendations and built upon Giere's views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the "Evidence-Explanation (EE) Continuum." The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis…

Language research has come to rely heavily on large-scale, web-based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long-standing challenges in corpus-based language research,…

The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education. There is still a need, however, to more deeply conceptualize what a field of Data Science Education might entail in terms of scope, responsibility, and execution.…

Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry…

Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…

Intelligent assessment, the core of any AI-based educational technology, is defined as embedded, stealth and ubiquitous assessment which uses intelligent techniques to diagnose the current cognitive level, monitor dynamic progress, predict success and update students' profiling continuously. It also uses various technologies, such as learning…

Data-based individualization (DBI) is a process of collecting and analyzing data on students' response to intervention and then making intervention adaptations accordingly. Although this process can lead to better student outcomes, very few teachers are trained in the components of DBI, particularly in relation to behavior. Improving practice…

Trial-based functional analysis (TBFA) is an accurate and ecologically valid assessment of challenging behavior. Further, there is evidence to suggest that individuals with minimal exposure to behavior analytic assessment methodology (e.g., parents, teachers) can quickly be trained to conduct TBFAs in naturalistic settings (e.g., schools, homes).…

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research…

School attendance/absenteeism (SA/A) is a crucial indicator of health and development in youth but educational policies and health-based practices in this area rely heavily on a simple metric of physical presence or absence in a school setting. SA/A data suffer from problems of quality (reliability, construct validity, data integrity) and utility…