We report findings of this replication study, examining the internalizing subscale (SRSS-I4) of the revised version of the Student Risk Screening Scale for Internalizing and Externalizing behavior (SRSS-IE 9) and the internalizing subscale of the Teacher Report Form (TRF). Using the sample from 13 elementary schools across three U.S. states with…

Purpose: School principals are increasingly responsible for acting as instructional leaders, but research on data teams typically considers principals as secondary players responsible for ensuring that meetings occur but not necessarily for their quality. We investigated how elementary school principals in one district committed to data use…

The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…

This manuscript presents an exploratory mixed-methods case study examining the impact of artificial intelligence (AI) in the form of generative pretrained transformers (GPTs) and large language models on special education administrative practices in one school district in the Northeast United States. AI holds tremendous potential to positively…

We report the results of a convergent validity study examining the externalizing subscale (SRSS-E5, five items) of the adapted Student Risk Screening Scale for Internalizing and Externalizing (SRSS-IE 9) with the externalizing subscale of the Teacher Report Form (TRF) with two samples of K-12 students. Results of logistic regression and receiver…

Non-academic factors such as school climate, grit, and growth mindset are receiving much attention in recent education policy and practice. Within this context, this study (N = 425) describes the distribution of U.S. in-service teachers' access to and use of 10 categories of non-academic data. Findings indicate that in-service teachers vary widely…

Children require access to high-quality statistics education to develop the skills to participate in a technological and data-reliant workforce. This study consisted of a five-lesson integrated STEM unit designed to develop the statistical literacy of 62 6th-grade (11-12 years old) emerging bilingual (EB) learners. Learning was situated in the…

Background: New technology and increased collaboration have revolutionized how scientists work with data. This creates a need to identify new aspects of working with scientific data that are important for K-12 students to learn. Purpose: To address this need, we conducted a study with practicing scientists and K-12 science teachers. The purpose of…

There is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing,…

Following a high-quality early care and pre-K experience, the kindergarten-through-third-grade years set the foundation upon which future learning builds; and strengthening this continuum creates opportunities for later success. Key components of a quality experience in K-3 include school readiness and transitions, kindergarten requirements,…

A gap in practice at CES Elementary School (pseudonym) was the lack of data driven instructional decision making that contributed to the problem of low school scores on the State of Texas Assessments of Academic Readiness (STAAR). Low STAAR scores have negatively impacted student, teacher, and administrator retention. Unsuccessfully, data chats…

In order to study the manifestation of data sense and identify ways of thinking about variability in authentically realistic problems in a group of Chilean fifth-grade students, a lesson plan was designed and implemented, within the framework of statistical literacy and using the "lesson study" modality, in which students were urged to…

The purpose of this pilot study was to engage pre-service elementary teachers in a semester-long project to deepen their quantitative reasoning skills when working with real-world data. Over three semesters, all pre-service elementary teachers enrolled in mathematics content courses focusing on K-8 mathematics topics had to collect, analyze,…

The new research results on the online- testing method in the Latvian education system for a learning process assessment are presented. Data mining is a very important field in education because it helps to analyse the data gathered in various researches and to implement the changes in the education system according to the learning methods of…

Determining whether a state's young people are on track for a life of opportunity is a difficult task for governors and state leaders. States can be both awash in data and unable to easily access and use that data to inform policy. State longitudinal data systems that meaningfully connect workforce, higher education, K-12, and early childhood…