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Showing all 8 results Save | Export
Jo Boaler; Cathy Williams – Corwin, 2025
How can we prepare students for a world where data-driven decision-making shapes nearly every aspect of life? "Data Minds: How Today's Teachers Can Prepare Students for Tomorrow's World" helps K-8 educators infuse data literacy into everyday lessons across disciplines, without overwhelming existing curricula. Data literacy is an ability…
Descriptors: Data Use, Decision Making, Information Literacy, Data Analysis
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Ana Stojanov; Ben Kei Daniel – Education and Information Technologies, 2024
The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and…
Descriptors: Higher Education, Learning Analytics, Well Being, Decision Making
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Michael Moore; David Clingenpeel – College and University, 2024
Wake Forest University (WFU) is in the midst of a 28-month student information system (SIS) transition. The authors' offices are deeply involved in this process on a daily basis. As the university collects and analyzes data, discusses operational needs with campus colleagues, builds and configures tenants, tests and validates, and implements a new…
Descriptors: Registrars (School), Universities, Information Systems, Online Systems
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Leslie Dial; Juliann Sergi McBrayer; Antonio P. Gutierrez de Blume; Caitlin Criss; Mary Josephine Carney – Georgia Educational Researcher, 2025
School leadership in education is thinking, communicating, and modeling to maximize student growth and achievement. This quantitative research study explored how school leadership can strengthen the academic progress of students within Multi-Tiered Systems of Support (MTSS). When implementation of MTSS is guided by adaptive school leadership,…
Descriptors: Leadership, Role, Multi Tiered Systems of Support, Communication (Thought Transfer)
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Anders Kristian Munk; Anders Koed Madsen; Mathieu Jacomy – New Perspectives on Learning and Instruction, 2019
Data sprints have emerged as a popular way to involve stakeholders in datawork. In this chapter we discuss what it takes to turn a sprint into a productive situation of inquiry (in the sense of Dewey, 1938). We argue that sprint organizers must work actively to counteract an otherwise docile setting where the preference for agreement between…
Descriptors: Data Analysis, Risk, Research Methodology, Research Problems
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Gómez-Torres, Emilse – Statistics Education Research Journal, 2021
This paper describes the evolution of "recognition of need for data" and "strategical thinking", two types of thinking identified by Wild and Pfannkuch in their Framework for Statistical Thinking in Empirical Enquiry, as well as its relevance for math teacher professional development. The research was carried out with ten…
Descriptors: Thinking Skills, Mathematics Teachers, Secondary School Teachers, Data Analysis
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Kjelvik, Melissa K.; Schultheis, Elizabeth H. – CBE - Life Sciences Education, 2019
Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise…
Descriptors: Data Use, Scientific Research, Information Literacy, STEM Education
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use