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Louise Gwenneth Phillips; M. Obaidul Hamid; Vicente Reyes; Ian Hardy – Educational Review, 2024
We live in a data-driven world. The voluminous scale of data gathered can lead to diminished consciousness of ethics whilst economic interests are prioritised. Across recent decades education has come to be heavily data driven and datafied. We have witnessed the dehumanising and increased labour impacts of school datafication. In search for…
Descriptors: Foreign Countries, Educational Researchers, Data, Data Use
Alexandra M. Pierce; Melissa A. Collier-Meek; Thea R. Bucherbeam; Lisa M. H. Sanetti – Communique, 2024
Students cannot experience the full potential benefits of an intervention unless they are receiving the intervention. This is the second installment in a three-part series on intervention fidelity designed to highlight the importance of ensuring classroom supports are implemented as intended. This article provides guidance related to measuring and…
Descriptors: Data Use, Decision Making, Intervention, Fidelity
Tamara L. Shreiner – Teachers College Press, 2024
We are surrounded by data and data visualizations in our everyday lives. To help ensure that students can critically evaluate data--and use it to promote social justice--this book outlines principles and practices for teaching data literacy as part of social studies education. The author shows how social studies content and skills can enhance both…
Descriptors: Social Studies, Multiple Literacies, Teacher Competencies, Elementary Secondary Education
Region 1 Comprehensive Center, 2024
The Maine Department of Education (MDOE) wanted to better understand if their current educator workforce data collection could help them quantify supply and demand for educators in the state. They also wanted to understand if local school administrative units collected data that could inform future efforts to understand educator vacancies to…
Descriptors: School Administration, Teacher Characteristics, Labor Force, Data Collection
Laura M. Samulski-Peters – ProQuest LLC, 2024
One of the most significant issues in education, as defined by the U.S. Department of Education Office of Accountability (2018), is disproportionality in exclusionary discipline. Disproportionality is defined as the over- and under-representation of racial/ethnic minorities in relation to their overall enrollment (Ahram et al., 2011). Currently,…
Descriptors: Disproportionate Representation, Discipline, Data Use, Minority Group Students
John Hattie; Douglas Fisher; Nancy Frey; John Taylor Almarode – Corwin, 2024
It may seem obvious, but learning should never be implied or assumed. Learning must be explicit, evaluated and monitored; the impact of teaching on student learning should be visible. But how can we be sure? Armed with years of research that includes more than 2,100 meta-analyses, and 130,000 studies that include more than 300 million…
Descriptors: Evidence Based Practice, Data Collection, Data Use, Educational Quality
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Evelyn Goffin; Rianne Janssen; Jan Vanhoof – AERA Online Paper Repository, 2024
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.…
Descriptors: Data Use, Educational Research, Data Interpretation, Performance Based Assessment
Data Quality Campaign, 2024
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…
Descriptors: Early Childhood Education, Administrator Attitudes, Data Use, Decision Making
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Liu, Yi; Xu, TianWei; Xiao, Mengjin – International Journal of Information and Communication Technology Education, 2023
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…
Descriptors: Research Libraries, Data Collection, Data Analysis, Tables (Data)
Data Quality Campaign, 2023
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…
Descriptors: Educational Legislation, Data Analysis, Data Collection, Access to Information
James LaMar Bolden – ProQuest LLC, 2023
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…
Descriptors: Higher Education, Data Science, Administrators, Professional Personnel
Data Quality Campaign, 2021
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…
Descriptors: Data Use, Data Collection, Data Analysis, Bias
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Wilkerson, Michelle Hoda; Lanouette, Kathryn; Shareff, Rebecca L. – Mathematical Thinking and Learning: An International Journal, 2022
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…
Descriptors: Data Science, Information Literacy, Data Analysis, Secondary School Students
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Cintron, Dakota W.; Montrosse-Moorhead, Bianca – American Journal of Evaluation, 2022
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…
Descriptors: Evaluation Research, Evaluation Problems, Evaluation Methods, Models
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Marianne van Dijke-Droogers; Paul Drijvers; Arthur Bakker – Mathematics Education Research Journal, 2025
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,…
Descriptors: Statistical Inference, Learning Trajectories, Grade 9, High School Students
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