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Denise Nadasen – Association of Public and Land-grant Universities, 2024
The Data Culture Framework is a high-level guide designed for institutional leaders who want to create and sustain an effective data culture on campus. The Framework offers a set of practices designed to help institutions of higher education create and maintain an effective data-informed community among institutional leaders, faculty, and staff.
Descriptors: Land Grant Universities, Data Collection, Data Use, College Faculty
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John Stamper; Steven Moore; Carolyn P. Rosé; Philip I. Pavlik Jr.; Kenneth Koedinger – Journal of Educational Data Mining, 2024
LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates…
Descriptors: Learning Analytics, Web Sites, Data Use, Educational Technology
Jason Delisle; Jason Cohn – Urban Institute, 2024
Data showing what students earn after attending higher education institutions have become increasingly available, bolstering calls from policymakers and advocates that government financial aid programs should be tied to those outcomes. Often overlooked, however, is that these data and policies usually reflect the earnings of only students who…
Descriptors: College Graduates, College Attendance, Dropouts, Data Collection
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Peter K. Dunn – International Journal of Mathematical Education in Science and Technology, 2024
The use of group work projects is common in introductory statistics courses, including projects where students collect their own data. However, the COVID-induced lockdown at the start of 2020 meant that data collection was compromised. In this study, we examine a situation where students were permitted to use artificial (made-up) data for their…
Descriptors: Student Projects, Undergraduate Students, Statistics Education, COVID-19
Claire Abbott; Michael DeArmond; Dan Goldhaber; Hannah Putman – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2025
This report examines how states can use their Statewide Longitudinal Data Systems (SLDS) to better understand their teacher supply problems and assess initiatives designed to address them. It emphasizes two key actions: connecting existing data and collecting new data on the prospective workforce. Using research and real-world examples from four…
Descriptors: Teacher Supply and Demand, Data Use, Longitudinal Studies, Data Collection
Data Quality Campaign, 2024
In 2024, state legislators introduced hundreds of bills that would affect data collection, access, and use across early education, K-12, postsecondary, and the workforce. As in 2023, legislators continued to introduce and enact legislation governing cross-agency data systems. These policies are the most important step toward making statewide…
Descriptors: State Legislation, Early Childhood Education, Elementary Secondary Education, Labor Force
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Rachel Elizabeth Fish; Kenneth Shores; João M. Souto Maior – Exceptional Children, 2026
This essay provides a two-pronged critical assessment of a subset of the literature on racial disproportionality in special education: that which aims to estimate racial disparities among otherwise similar children. This body of research has shown that Black students are less likely than "comparable" White students to receive special…
Descriptors: Disproportionate Representation, Race, Special Education, African American Students
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Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim – Interactive Learning Environments, 2023
Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…
Descriptors: Prediction, Student Behavior, Models, Academic Achievement
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Lee, Chia-An; Huang, Nen-Fu; Tzeng, Jian-Wei; Tsai, Pin-Han – IEEE Transactions on Learning Technologies, 2023
Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning…
Descriptors: Artificial Intelligence, MOOCs, Concept Mapping, Student Evaluation
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John N. Dyer – Journal of Instructional Pedagogies, 2023
Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of…
Descriptors: Data Analysis, Business, Business Administration Education, Information Literacy
Karen Dan Wang – ProQuest LLC, 2023
Digital learning environments are becoming increasingly ubiquitous as a wide range of EdTech products and services enter classrooms and households across the globe. One salient attribute of these environments is their capacity to generate large amounts of data as students interact with the technology. These data logs can help construct a detailed…
Descriptors: Educational Technology, Electronic Learning, Data Collection, Problem Solving
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Heffernan, Troy; Harpur, Paul – Assessment & Evaluation in Higher Education, 2023
Across the international higher education sector, existing studies highlight that student evaluations of courses and teaching are biased and prejudiced towards academics and can cause mental distress. Yet student evaluation data is often used as part of faculty hiring, firing, promotion, award and grant decisions. That a data source known to be…
Descriptors: Student Evaluation of Teacher Performance, Bias, School Policy, Universities
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Blackmon, Stephanie J. – Change: The Magazine of Higher Learning, 2023
Student privacy is a critical area of higher education that deserves greater focus, particularly as student data digitalization increases. Many colleges and universities use data literacy as a way to prepare students, sometimes from different disciplines, to work with others' data postgraduation. Data literacy can be an avenue for helping all…
Descriptors: Privacy, Data Collection, Data Use, Higher Education
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Bowers, Pam; Chen, Helen L.; O'Donnell, Ken; Parnell, Amelia – Change: The Magazine of Higher Learning, 2022
Traditional student information systems were designed primarily to collect and manage records of course enrollment and credit hours earned, as well as other data elements needed to monitor each student's progress to graduation. Now, institutions want to monitor and improve the quality and equity of students' learning experiences in courses and the…
Descriptors: Educational Practices, Data Collection, Data Use, School Policy
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Higgins, Linden E.; Smith, Julia M. – Research Evaluation, 2022
In 2015, a multidisciplinary group of academic researchers and extension personnel was awarded a US Department of Agriculture Coordinated Agricultural Project grant. The team's goal was to understand human decision-making in the context of potential livestock disease outbreaks, investigating the impediments to increased adoption of biosecurity…
Descriptors: Interdisciplinary Approach, Cooperation, Extension Agents, Researchers
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