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Nargiza Mikhridinova; Carsten Wolff; Wim Van Petegem – Education and Information Technologies, 2024
An individual competence is one of the main human resources, which enables a person to operate in everyday life. A competence profile, formally captured and described as a structured model, may enable various operations, e.g., a more precise evaluation and closure of a training gap. Such application scenarios supported by information systems are…
Descriptors: Taxonomy, Competence, Models, Profiles
Pattamaporn Piriyapongpipat; Sally Goldin; Nadh Ditcharoen – Smart Learning Environments, 2024
Global trends in higher education emphasize the development of curricula that offer greater responsiveness to learners. Creating flexible and responsive curricula will require additional support systems for curriculum management. The first step toward sustainably developing this kind of system is to represent essential curricular information in a…
Descriptors: Curriculum Development, Taxonomy, Higher Education, Models
Herbert W. Marsh; Jiesi Guo; Reinhard Pekrun; Oliver Lüdtke; Fernando Núñez-Regueiro – Educational Psychology Review, 2024
Multi-wave-cross-lagged-panel models (CLPMs) of directional ordering are a focus of much controversy in educational psychology and more generally. Extending traditional analyses, methodologists have recently argued for including random intercepts and lag2 effects between non-adjacent waves and giving more attention to controlling covariates.…
Descriptors: Self Concept, Academic Achievement, Correlation, Educational Psychology
Valentina Gliozzi – Cognitive Science, 2024
We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying…
Descriptors: Infants, Computation, Models, Cognitive Development
Seyed Parsa Neshaei; Richard Lee Davis; Paola Mejia-Domenzain; Tanya Nazaretsky; Tanja Käser – International Educational Data Mining Society, 2025
Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…
Descriptors: Artificial Intelligence, Classification, Natural Language Processing, Technology Uses in Education
Bertens, Laura M. F. – Arts and Humanities in Higher Education: An International Journal of Theory, Research and Practice, 2022
Although the art historical canon has been the subject of fierce debate, it remains an essential construct, shaping textbooks and survey courses. Visual representations of the canon often illustrate these narratives. Students encounter diagrams in their studies and it is important to make them aware of the illusion of scientific objectivity. This…
Descriptors: Art History, Art Education, Models, Visual Aids
Horvat, Ines; Miloševic, Marija; Hasenay, Damir – Education for Information, 2023
Written heritage preservation is a complex field that requires a holistic approach reflected in the model of comprehensive written heritage preservation management. Model encompasses key issues through five key aspects, namely strategic and theoretical, economic and legal, educational, technical and operational and cultural and social aspect.…
Descriptors: Cultural Maintenance, Writing (Composition), Models, Taxonomy
Corazza, Giovanni Emanuele; Lubart, Todd – Journal of Intelligence, 2021
This theoretical article proposes a unified framework of analysis for the constructs of intelligence and creativity. General definitions for intelligence and creativity are provided, allowing fair comparisons between the two context-embedded constructs. A novel taxonomy is introduced to classify the contexts in which intelligent and/or creative…
Descriptors: Intelligence, Creativity, Taxonomy, Time
Constance Tucker; Sarah Jacobs; Kirstin Moreno – Intersection: A Journal at the Intersection of Assessment and Learning, 2024
Learning outcomes and assessment frameworks guide educators in curricular decisionmaking, impact assessment, gap identification, and equity evaluation, aligning with anticipated learning objectives. Common frameworks include Bloom's taxonomy, Kirkpatrick's model, Fink's taxonomy, and Moore's Outcomes model. The authors identified a lack of focus…
Descriptors: Student Evaluation, Outcomes of Education, Taxonomy, Decision Making
Ryan, Tracii; Henderson, Michael; Ryan, Kris; Kennedy, Gregor – Teaching in Higher Education, 2023
Due to recent conceptual shifts towards learner-centred feedback, there is a potential gap between research and practice. Indeed, few models or studies have sought to identify or evaluate which semantic messages, or feedback components, teachers should include in learner-centred feedback comments. Instead, teacher practices are likely to be…
Descriptors: Feedback (Response), Taxonomy, Validity, College Faculty
Suliman Zakaria Suliman Abdalla; Amal Khalfan Rashid AlSalti – Educational Process: International Journal, 2025
Background/purpose: This study examines the behavioral factors influencing the adoption of AI-powered generative technologies in higher education and their impact on students' cognitive engagement--a crucial element of sustainable, inclusive, and high-quality learning, as envisioned by UNESCO's Sustainable Development Goal 4 (SDG4). The study…
Descriptors: Artificial Intelligence, Cognitive Development, College Students, Computer Uses in Education
López-Zambrano, Javier; Lara, Juan A.; Romero, Cristóbal – Journal of Computing in Higher Education, 2022
One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…
Descriptors: Learning Analytics, Prediction, Models, Semantics
Murphy, Victoria L.; Littlejohn, Allison; Rienties, Bart – Journal of Workplace Learning, 2022
Purpose: Learning from incidents (LFI) is an organisational process that high-risk industries use following an accident or near-miss to prevent similar events. Literature on the topic has presented a fragmented conceptualisation of learning in this context. This paper aims to present a holistic taxonomy of the different aspects of LFI from the…
Descriptors: Workplace Learning, Informal Education, Organizational Learning, Safety
Nour Eddine El Fezazi; Smaili El Miloud; Ilham Oumaira; Mohamed Daoudi – Educational Process: International Journal, 2025
Background/purpose: Mobile learning (M-learning) has become a crucial component of higher education due to the increasing demand for flexible and adaptive learning environments. However, ensuring personalized and effective M-learning experiences remains a challenge. This study aims to enhance M-learning effectiveness by introducing an AI-driven…
Descriptors: Electronic Learning, Learning Management Systems, Instructional Effectiveness, Artificial Intelligence
Ley, Tobias; Tammets, Kairit; Pishtari, Gerti; Chejara, Pankaj; Kasepalu, Reet; Khalil, Mohammad; Saar, Merike; Tuvi, Iiris; Väljataga, Terje; Wasson, Barbara – Journal of Computer Assisted Learning, 2023
Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Analytics, Instructional Effectiveness

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