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David Levy; Harvey J. Graff – Change: The Magazine of Higher Learning, 2024
Historically, it has been difficult for students and their families to compare how different colleges and programs improve students' chances of landing a desirable job and earning a good salary. The U.S. Department of Education's 2015 release of student earning data was a major step toward remedying this problem. The data offered by the College…
Descriptors: College Choice, Income, Outcomes of Education, Education Work Relationship
David Joyner, Editor; Benjamin Paaßen, Editor; Carrie Demmans Epp, Editor – International Educational Data Mining Society, 2024
The Georgia Institute of Technology is proud to host the seventeenth International Conference on Educational Data Mining (EDM) in Atlanta, Georgia, July 14-July 17, 2024. EDM is the annual flagship conference of the International Educational Data Mining Society. This year's theme is "New tools, new prospects, new risks--educational data…
Descriptors: Data Analysis, Pattern Recognition, Technology Uses in Education, Artificial Intelligence
Chenguang Pan; Zhou Zhang – International Educational Data Mining Society, 2024
There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…
Descriptors: High School Students, Dropouts, Dropout Characteristics, Artificial Intelligence
Humenberger, Hans – Teaching Statistics: An International Journal for Teachers, 2020
In this paper, we investigate an interesting question that came up when reading a problem in a school textbook: What happens to the variance of a dataset in the case of changing one single data point, and why? Some of the answers are not surprising but here we find the full answer and demonstrate the understanding of it suitable for school…
Descriptors: Problem Solving, Statistics, Data, Data Analysis
Meng-Ting Lo – ProQuest LLC, 2020
Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research…
Descriptors: Data Analysis, Models, Best Practices, Data Interpretation
Anna McDougall; Douglas McKee; George Orlov – Journal of Economic Education, 2025
While the field of economics lacks diversity, there is little consensus on the underlying causes of or most effective solutions to this problem. The authors of this article combine data from the Integrated Postsecondary Education Data System (IPEDS) with data from their own survey of U.S. economics departments to identify institution and…
Descriptors: Student Diversity, Economics Education, Departments, Undergraduate Students
Tahlia Lasczik; Alexandra Lasczik; Amy Cutter-Mackenzie-Knowles – International Journal of Art & Design Education, 2025
The rise in the number of young people disengaged from mainstream schooling is reaching critical proportions. This paper explores a child-framed participatory inquiry known as The Walking A/r/tography Project, which sought to challenge, empower and engage youth at risk in one Special Assistance Secondary School in Southeast Queensland through…
Descriptors: Foreign Countries, Secondary School Students, Student Projects, Student Research
Victoria L. Bernhardt – Eye on Education, 2025
With the 5th Edition of Data Analysis for Continuous School Improvement, best-selling Victoria Bernhardt has written the go-to-resource for data analysis in your school! By incorporating collaborative structures to implement, monitor, and evaluate the vision and continuous improvement plan, this book provides a framework to show learning…
Descriptors: Learning Analytics, Data Analysis, Educational Improvement, Evaluation Methods
Corrie Wilder; Shannon Calderone – Impacting Education: Journal on Transforming Professional Practice, 2025
This study explores the integration of generative artificial intelligence (AI) into qualitative research within a higher education context. Through a collaborative self-study, a doctoral candidate and their dissertation supervisor examined the application of Google's Gemini 1.5 to analyze interview data from a dissertation of practice (DiP)…
Descriptors: Technology Integration, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Robert M. Johnstone – Educational Considerations, 2025
This article explores utilizing a post-graduation success lens to help community college leaders frame the challenges of achieving equitable improvement for their students. Specifically, it posits that providing and exploring customized labor market data presented in an accessible format can help institutional leaders provide a "true…
Descriptors: Community College Students, College Graduates, Outcomes of Education, Success
Candace Walkington; Matthew Bernacki; Elizabeth Leyva; Brooke Istas – Journal for Research in Mathematics Education, 2025
Algebra has been identified as a gatekeeper to careers in STEM, but little research exists on how algebra appears for practitioners in the workplace. Surveys and interviews were conducted with 77 STEM practitioners from a variety of fields, examining how they reported using algebraic functions in their work. Survey and interview reports suggest…
Descriptors: Algebra, Mathematics, Computation, Mathematical Formulas
Xavier Ochoa; Xiaomeng Huang; Yuli Shao – Journal of Learning Analytics, 2025
Generative AI (GenAI) has the potential to revolutionize the analysis of educational data, significantly impacting learning analytics (LA). This study explores the capability of non-experts, including administrators, instructors, and students, to effectively use GenAI for descriptive LA tasks without requiring specialized knowledge in data…
Descriptors: Learning Analytics, Artificial Intelligence, Computer Software, Scores
Xiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis
Nathan Jones; Lindsey Kaler; Jessica Markham; Josefina Senese; Marcus A. Winters – Educational Researcher, 2025
Students with and without disabilities may be educated across various service delivery models (SDMs): general education, cotaught, pull-out, and self-contained. Still, evidence for their relative effectiveness at scale remains limited. Using longitudinal administrative data from Indiana, we measured the effect of different SDMs on test scores,…
Descriptors: Students with Disabilities, Teaching Methods, Students, Instructional Effectiveness
Krista Bixler; Marjorie Ceballos – Leadership and Policy in Schools, 2025
Instructional leadership is a complex dimension, which requires that principals possess expertise in goal setting, leading the instructional program, and creating the conditions for a successful school environment. Effective instructional leaders manage the instructional program by planning, coordinating, and evaluating the work of teachers and…
Descriptors: Principals, Instructional Leadership, Artificial Intelligence, Educational Technology

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