We undertook a research project consisting of four interrelated studies, in order to shed light on ways educational professionals make sense and make use of educational data in general and school performance feedback in particular. Theoretical insights illuminate why a sensemaking perspective is an appropriate and valuable lens to study data use.…

A national poll from the Data Quality Campaign (DQC), conducted by The Harris Poll, surveyed early childhood administrators--educational or child care professionals in program director or general manager roles serving children from birth through age four--to find out how they are collecting, using, and reporting data. Early childhood…

Those asked to make valid decisions with data don't have the technical knowledge to understand nuance around data quality, assessment aims, and statistical limitations that influence how they should interpret the data. This reality is what Greg Thompson, Leslie Rutkowski, and David Rutkowski call the validity paradox. Educators can surmount this…

Historic data in analog (or print) format is a valuable resource that is utilized by scientists in many fields. This type of data may be found in various locations on university campuses including offices, labs, storage facilities, and archives. This study investigates whether biological data held in one institutional university archives could be…

Vision classification tasks, a fundamental and transformative aspect of deep learning and computer vision, play a pivotal role in our ability to understand the visual world. Deep learning techniques have revolutionized the field, enabling unprecedented accuracy and efficiency in vision classification. However, deep learning models, especially…

Collecting data for the purpose of decision making has become an integral part of the landscape of education in the United States over the past decade. Many educators are swamped with such an overwhelming amount of information that it can be difficult to sort and analyze, leaving them floundering under wave after wave of data. The SWIS facilitator…

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 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…

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…

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,…