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Hadis Anahideh; Nazanin Nezami; Abolfazl Asudeh – Grantee Submission, 2025
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness.…
Descriptors: Correlation, Measurement Techniques, Guidelines, Semantics
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Achmad Bisri; Supardi; Yayu Heryatun; Hunainah; Annisa Navira – Journal of Education and Learning (EduLearn), 2025
In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This…
Descriptors: Data Analysis, Models, Academic Achievement, Evaluation Methods
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Katerina Guba; Angelika Tsivinskaya – Studies in Higher Education, 2025
This paper explores the regulators' perspective and demonstrates how legitimacy deficits of private universities outweigh performance results in decisions regarding university inspections. We examined the period when the regulator had an urgent claim on Russian universities, particularly during the campaign to 'clean the system of higher…
Descriptors: Private Sector, Private Education, Universities, Private Colleges
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Joanna Clifton-Sprigg; Jonathan James – British Educational Research Journal, 2025
Using newly released detailed data on absence from school, we find a 'Friday effect'--children are much less likely to attend schools in England on Fridays. We use daily level data across the whole of England and find that this pattern holds for different schools and for different types of absence, including illness-related authorised and…
Descriptors: Foreign Countries, Attendance Patterns, Student Behavior, Attendance
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Joseph Chiarelli; Melissa A. St. Hilaire; Brandi L. Baldock; Jimmy Franco; Stephen Theberge; Anthony L. Fernandez – Journal of Chemical Education, 2025
There is a growing need for chemistry students to be able to handle and manipulate large datasets and analyze them in an efficient and accessible way. This creates the need to develop course materials that introduce these topics early in the undergraduate curriculum. To address this growing need, this activity introduced RStudio to students…
Descriptors: Chemistry, Science Instruction, College Science, Undergraduate Students
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Eric Richardson; Jean Gordon; Richard Ginnetti; Rachel Carroll; Randyl Cochran; Laura Morris; Valerena Candy; Margaret Brown – Journal of Education Human Resources, 2025
Beyond informing human resources (HR) policies and practices, information gleaned from predictive analytics, visualized via dashboards, can increase awareness and prompt employee and management actions based on identified variables often related to intent to leave and employee wellness. While considerable research, relevant measurements, and tools…
Descriptors: Higher Education, Employee Attitudes, Labor Turnover, Occupational Mobility
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Catherine Ferguson – Issues in Educational Research, 2025
The use of artificial intelligence (AI) in higher education has mostly focused on issues associated with teaching and assessment. In this paper I used AI to support the analysis of data which consisted of public comments on a newspaper article. This small, low risk research was chosen to demonstrate the potential use of AI and how it may support…
Descriptors: Artificial Intelligence, Data Analysis, Technology Uses in Education, Higher Education
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Federico Colecchia; Fabrizio Ceschin; David Harrison – International Journal of Technology and Design Education, 2025
It has been acknowledged that global challenges are in the way of delivering responsible innovation, as reflected in the Sustainable Development Goals -- a set of strategic objectives formulated by the United Nations General Assembly, to promote environmentally, societally, and economically-sustainable development. Design higher education has an…
Descriptors: Technology Uses in Education, Design, Higher Education, Sustainability
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Nicole Barnes; Helenrose Fives; Coby V. Meyers; Tonya R. Moon – Journal of Educational Administration, 2025
Purpose: School principals are increasingly responsible for acting as instructional leaders, but research on data teams typically considers principals as secondary players responsible for ensuring that meetings occur but not necessarily for their quality. We investigated how elementary school principals in one district committed to data use…
Descriptors: Elementary Schools, Rural Areas, School Districts, Principals
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Ridwan Whitehead; Andy Nguyen; Sanna Järvelä – Journal of Learning Analytics, 2025
Incorporating non-verbal data streams is essential to understanding the dynamics of interaction within collaborative learning environments in which a variety of verbal and non-verbal modes of communication intersect. However, the complexity of non-verbal data -- especially gathered in the wild from collaborative learning contexts -- demands…
Descriptors: Case Studies, Nonverbal Communication, Video Technology, Data Analysis
Thomas R. Guskey, Editor; Cassandra Erkens, Contributor; Katie White, Contributor; Mandy Stalets, Contributor; Garnet Hillman, Contributor; Tim Brown, Contributor; Tom Hierck, Contributor; Tom Schimmer, Contributor; Sharon V. Kramer, Contributor; Sarah Schuhl, Contributor; Anthony R. Reibel, Contributor; Joellen Killion, Contributor – Solution Tree, 2025
In "The Teacher as Assessment Leader, Second Edition," editor Thomas R. Guskey and expert contributors offer research-backed strategies for re-envisioning assessment to enhance student learning and teacher instruction. The authors provide actionable steps, practical examples, and strategies for utilizing formative assessments. These…
Descriptors: Teacher Leadership, Formative Evaluation, Data Analysis, Evidence Based Practice
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Ye, Dan – TechTrends: Linking Research and Practice to Improve Learning, 2022
This article introduces the evolution of themes and ideas related to the history, theory, and practice of learning analytics within the learning, design, and technology field through four eras. This review provides researchers with a fundamental understanding of the origin of learning analytics from a historical perspective and distinguishes…
Descriptors: Learning Analytics, Educational History, Theory Practice Relationship, Ethics
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Schuman, Carrie; Stofer, Kathryn A.; Anthony, Lisa; Neff, Hannah; Chang, Peter; Soni, Nikita; Darrow, Alice; Luc, Annie; Morales, Amanda; Alexandre, Jeremy; Kirkland, Brittani – Research in Science Education, 2022
Conceptualizing and understanding global, physical systems like Earth's ocean is challenging. Data visualizations on touch-based technology allow learners to explore systems and facilitate embodied experiences, promoting deeper understanding. We investigated how direct manipulation of data visualizations on a touchscreen table affords meaningful…
Descriptors: Oceanography, Data Analysis, Visual Aids, Technology Uses in Education
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Zakharova, Irina; Jarke, Juliane – Learning, Media and Technology, 2022
Schools, teaching, and learning environments have long been understood and idealised as places of care. Within feminist studies and Science and Technology Studies (STS), scholars have proposed a shift to include non-humans in what we propose to conceive as care arrangements. Drawing on Tronto's feminist ethics of care [1993. "Moral…
Descriptors: Educational Technology, Caring, Foreign Countries, Information Systems
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McHenry, William K. – Journal of Information Systems Education, 2022
Students now have readily available and powerful tools to access, manipulate, combine, and visualize data. Acquiring data and visual literacy requires more than knowledge of how to use these tools. Students need to engage with assignments that challenge them to make relatively complex visualizations, interpret them, and explain why these…
Descriptors: Visual Aids, Scoring Rubrics, Feedback (Response), Data Interpretation
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