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Kamila Misiejuk; Sonsoles López-Pernas; Rogers Kaliisa; Mohammed Saqr – Journal of Learning Analytics, 2025
Generative artificial intelligence (GenAI) has opened new possibilities for designing learning analytics (LA) tools, gaining new insights about student learning processes and their environment, and supporting teachers in assessing and monitoring students. This systematic literature review maps the empirical research of 41 papers utilizing GenAI…
Descriptors: Literature Reviews, Artificial Intelligence, Learning Analytics, Data Collection
Zhi Li; Wenxiang Zhang – Education and Information Technologies, 2025
In the swiftly changing realm of education, technology serves as a key instrument in transforming the methods of teaching, learning experiences, and educational outcomes. Legal and governance issues, integral to maintaining order and justice in societies, are equally pertinent in the realm of education. The digital age introduces concerns like…
Descriptors: Educational Technology, Technology Uses in Education, Technological Advancement, Legal Responsibility
Francis Huang; Brian Keller – Large-scale Assessments in Education, 2025
Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…
Descriptors: Research Problems, Data Analysis, Statistical Bias, International Assessment
Mathilde Léon; Shoba S. Meera; Anne-Caroline Fiévet; Alejandrina Cristia – Research Ethics, 2024
The last decade has seen a rise in big data approaches, including in the humanities, whereby large quantities of data are collected and analysed. In this paper, we discuss long-form audio recordings that result from individuals wearing a recording device for many hours. Linguists, psychologists and anthropologists can use them, for example, to…
Descriptors: Foreign Countries, Developing Nations, Data Collection, Audio Equipment
Tsai, Tiffany; Tosh, Katie – RAND Corporation, 2020
Teachers' use of student data to inform instruction is commonly accepted as sound educational practice, and this data use is only likely to grow as more data, as well as more-complex data, become increasingly available to educators. However, numerous studies reveal inconsistent data use among teachers and an overall lack of the preparation and…
Descriptors: Data Analysis, Data Use, Database Management Systems, Personnel Data
Grantee Submission, 2022
Systems for learning and producing knowledge, such as career and technical education (CTE), often reproduce inequities unless an equity-focused lens is used when designing, implementing, and evaluating programs. This framework presents guidance for conducting CTE research with an intentional focus on equity. Developed by the CTE Research Network's…
Descriptors: Equal Education, Vocational Education, Educational Research, Program Administration
National Forum on Education Statistics, 2023
This guide is designed for use by school, district, and state education agency staff to improve the effectiveness of efforts to collect and use discipline data, including reporting accurate and timely data to the federal government. It explains the importance of collecting discipline data, identifies key considerations for agencies implementing…
Descriptors: Discipline, Data Collection, Data Analysis, School Districts
Alturki, Sarah; Hulpu?, Ioana; Stuckenschmidt, Heiner – Technology, Knowledge and Learning, 2022
The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…
Descriptors: Grade Prediction, Academic Achievement, Data Use, Dropout Rate
Johnson, Sara K. – New Directions for Child and Adolescent Development, 2021
Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and…
Descriptors: Profiles, Child Development, Developmental Psychology, Models
Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
Ferguson, Sarah L.; Moore, E. Whitney G.; Hull, Darrell M. – International Journal of Behavioral Development, 2020
The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models,…
Descriptors: Statistical Analysis, Computer Software, Data Analysis, Goodness of Fit
Hollands, Fiona M.; Pratt-Williams, Jaunelle; Shand, Robert – Grantee Submission, 2021
The purpose of these guidelines is to support the execution of cost analysis and cost-effectiveness analysis of educational programs. The steps involved in conducting a cost analysis are presented in four stages with explicit examples used throughout: designing your cost analysis, collecting cost data using the ingredients method, analyzing cost…
Descriptors: Cost Effectiveness, Educational Finance, Standards, Guidelines
Clifford, Richard M.; Yazejian, Noreen; Jang, Wonkyung; Jigjidsuren, Dari – Teachers College Press, 2021
Early childhood is a crucial stage in a child's life, and aspects of the environment in the physical, social-emotional, cognitive, and health and safety domains all play important roles in shaping children's development during these early years. Having a valid and reliable measure of the quality of these aspects of children's care settings is…
Descriptors: Early Childhood Education, Data Analysis, Data Interpretation, Educational Quality
Hilbert, Sven; Coors, Stefan; Kraus, Elisabeth; Bischl, Bernd; Lindl, Alfred; Frei, Mario; Wild, Johannes; Krauss, Stefan; Goretzko, David; Stachl, Clemens – Review of Education, 2021
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non-linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but…
Descriptors: Artificial Intelligence, Online Courses, Educational Research, Data Analysis
Daniel A. Mak; Sebastian Dunn; David Coombes; Carlo R. Carere; Jane R. Allison; Volker Nock; André O. Hudson; Renwick C. J. Dobson – Biochemistry and Molecular Biology Education, 2024
Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and…
Descriptors: Biochemistry, Kinetics, Science Instruction, Teaching Methods