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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
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Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
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Lluch Molins, Laia; Cano García, Elena – Journal of New Approaches in Educational Research, 2023
One of the main generic competencies in Higher Education is "Learning to Learn". The key component of this competence is the capacity for self-regulated learning (SRL). For this competence to be developed, peer feedback seems useful because it fosters evaluative judgement. Following the principles of peer feedback processes, an online…
Descriptors: Learning Analytics, Learning Management Systems, Peer Evaluation, Higher Education
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Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Gonzalez, Naihobe; Alberty, Elizabeth; Brockman, Stacey; Nguyen, Tutrang; Johnson, Matthew; Bond, Sheldon; O'Connell, Krista; Corriveau, Adrianna; Shoji, Megan; Streeter, Megan; Engle, Jennifer; Goodly, Chelsea; Neely, Adrian N.; White, Mary Aleta; Anderson, Mindelyn; Matthews, Channing; Mason, Leana; Means, Sheryl Felecia – Mathematica, 2022
The Education-to-Workforce Indicator Framework (E-W Framework), commissioned by the Bill & Melinda Gates Foundation and developed in partnership with leading experts representing more than 15 national and community organizations, is designed to encourage greater cross-sector collaboration and alignment across local, state, and national data…
Descriptors: Education Work Relationship, Evidence Based Practice, Student Characteristics, Partnerships in Education
Dina Foster; Caitlin McLemore; Brandon Olszewski; Ali Chaudhry; Ekaterina Cooper; Laurie Forcier; Rose Luckin – UK Department for Education, 2023
A rapid, exploratory review to establish a shared understanding of what constitutes good quality EdTech and implementation. The review: (1) examined existing frameworks and standards; and (2) identified the characteristics, quality components, essential conditions and evaluation criteria for the design and implementation of EdTech. The report…
Descriptors: Standards, Educational Technology, Guidelines, Evidence Based Practice
Letitia Hewlett Roman – ProQuest LLC, 2021
Leadership strategies for school turnaround and how to prioritize improvement efforts seem to lack a clear pattern for leaders of failing schools. The purpose of this study was to determine successful leadership strategies and methods for school turnaround and how best to prioritize the efforts by surveying a panel of experts through a Delphi…
Descriptors: School Turnaround, Delphi Technique, Intervention, Leadership Role