Mixed methods research collects, analyzes, and integrates rigorous qualitative and quantitative methods to gain a deeper understanding of a phenomenon than would be gained by using either method alone. Integration is one of the key features of mixed methods research and consists of mixing qualitative and quantitative research in a systematic way…

School districts have recognized the undeniable power of using data to enhance performance. As a result, teachers are now required to use data to prepare for critical state assessments and to improve district levels. These assessments are crucial in determining the school's rating by the state and can even result in a state takeover if scores are…

In order to better grasp the needs of library users and provide them with more accurate knowledge services, combining the characteristics of university libraries, this article applies library small data to personalized recommendation and proposes a small data fusion algorithm model for library personalized recommendation. This model combines the…

Each year, state legislators introduce hundreds of bills that generate new data collections, analyses, and resources, playing a crucial role in how people access and use data. Notably, in 2023 legislators introduced and enacted bills governing cross-agency data systems--the most important step that states must take to make robust access to data…

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 preparation (also called "wrangling" or "cleaning") -- the evaluation and manipulation of data prior to formal analysis -- is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular…

Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim…

Data reflects a series of decisions made by people--and those decisions affect the story that data tells, what it captures, and how it can and should be used to inform decision-making. Because of this, mistrust in data is often the result of incomplete information and a lack of context. This resource breaks down what it means to build trust in…

In our data-driven society, it is essential for students to become statistically literate. A core domain within Statistical Literacy is Statistical Inference, the ability to draw inferences from sample data. Acquiring and applying inferences is difficult for students and, therefore, usually not included in the pre-10th-grade curriculum. However,…

Purpose: Higher education institutions increasingly emphasize data analytics education, yet curricula based solely on competency-based frameworks may overlook industry's process-driven approach. This study examines the process deficit in data analytics education and its impact on workforce readiness. It explores strategies to better align…

This Forum Guide, developed by the National Forum on Education Statistics, provides best practices for collecting, managing, and using educator workforce data. It offers education agencies practical guidance on building high-quality data systems that inform teacher recruitment, retention, and professional development efforts.

The 2023 Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC), provides a comprehensive overview of adults' literacy, numeracy and adaptive problem-solving skills -- skills that are fundamental for personal, economic and societal development. These key information processing skills…

Use and analysis of, and reflection on, qualitative and quantitative data helps illuminate the journey of professional learning for educators and students. This article describes methods to provide reflective space for administrative teams to listen to stakeholders' stories and consider additional support they can put in place to help their staff…

Evaluation data helps inform decision-makers about the time, human capital, and funding required for professional learning to be effective. Evaluation data also guides program improvement and sets leaders' expectations for ongoing monitoring and accountability. The complexities of the educational systems in which professional learning happens mean…

This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its…