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Nurassyl Kerimbayev; Karlygash Adamova; Rustam Shadiev; Zehra Altinay – Smart Learning Environments, 2025
This review was conducted in order to determine the specific role of intelligent technologies in the individual learning experience. The research work included consider articles published between 2014 and 2024, found in Web of Science, Scopus, and ERIC databases, and selected among 933 ?articles on the topic. Materials were checked for compliance…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Computer Software, Databases
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Bayounes, Walid; Saâdi, Ines Bayoudh; Kinsuk – Smart Learning Environments, 2022
The goal of ITS is to support learning content, activities, and resources, adapted to the specific needs of the individual learner and influenced by learner's motivation. One of the major challenges to the mainstream adoption of adaptive learning is the complexity and time involved in guiding the learning process. To tackle these problems, this…
Descriptors: Learning Processes, Learning Motivation, Individualized Instruction, Models
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Khaldi, Amina; Bouzidi, Rokia; Nader, Fahima – Smart Learning Environments, 2023
In recent years, university teaching methods have evolved and almost all higher education institutions use e-learning platforms to deliver courses and learning activities. However, these digital learning environments present significant dropout and low completion rates. This is primarily due to the lack of student motivation and engagement.…
Descriptors: Gamification, Electronic Learning, Higher Education, Recognition (Achievement)
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Takami, Kyosuke; Flanagan, Brendan; Dai, Yiling; Ogata, Hiroaki – Smart Learning Environments, 2023
In the age of artificial intelligence (AI), trust in AI systems is becoming more important. Explainable recommenders, which explain why an item is recommended, have recently been proposed in the field of learning technology to improve transparency, persuasiveness, and trustworthiness. However, the methods for generating explanations are limited…
Descriptors: Artificial Intelligence, Personality, Cognitive Processes, Public Health
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Paquette, Gilbert; Marino, Olga; Bejaoui, Rim – Smart Learning Environments, 2021
Competency is a central concept for human resource management, training and education. We define a competency as the capacity of a person to display a generic skill with a certain level of performance when applied to one or more knowledge entities. Competencies, and competency referentials grouping competencies, are essential elements for user…
Descriptors: Competence, Individualized Instruction, Technology Uses in Education, Philosophy
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Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Smart Learning Environments, 2022
Personalized learning systems use several components in order to create courses adapted to the learners'characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the…
Descriptors: Individualized Instruction, Technology Uses in Education, Electronic Learning, Mathematics
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Monica F. Contrino; Maribell Reyes-Millán; Patricia Vázquez-Villegas; Jorge Membrillo-Hernández – Smart Learning Environments, 2024
It is becoming increasingly clear that not all students require the same education, and the requirement of personalized education is increasingly in demand. The incorporation of adaptive learning (AL) has increased in recent years. However, research on this subject is still evolving at the university level. In this study, we investigated the…
Descriptors: Individualized Instruction, Academic Achievement, Student Satisfaction, Online Courses
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Said A. Salloum; Khaled Mohammad Alomari; Aseel M. Alfaisal; Rose A. Aljanada; Azza Basiouni – Smart Learning Environments, 2025
The integration of artificial intelligence in educational environments has the potential to revolutionize teaching and learning by enabling real-time analysis of students' emotions, which are crucial determinants of engagement, motivation, and learning outcomes. However, accurately detecting and responding to these emotions remains a significant…
Descriptors: Artificial Intelligence, Emotional Response, Psychological Patterns, Individualized Instruction
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Yiling Dai; Heinz Ulrich Hoppe; Brendan Flanagan; Kyosuke Takami; Hiroaki Ogata – Smart Learning Environments, 2024
Educational recommender systems have been supporting personalized learning in various ways. However, less discussion is conducted about whether and how to personalize the strategies to generate recommendations based on student differences. In this study, we aim at investigating how students judge recommendations based on different strategies, and…
Descriptors: Foreign Countries, High School Students, Student Characteristics, Individual Differences
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Zhong, Lin – Smart Learning Environments, 2022
While role-playing games and personalized learning have been regarded as effective tools to improve students' learning, incorporating personalized learning into role-playing games is challenging and approaches are limited to cognitive and motivational variables. Aiming at expanding approaches to incorporate personalization into role-playing games,…
Descriptors: Individualized Instruction, Role Playing, Cognitive Processes, Difficulty Level
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Galiya Ldokova; Svetlana Frumina; Suad Abdalkareem Alwaely – Smart Learning Environments, 2025
The aim of the study is to examine the influence of students' psychotypes on their learning using digital educational technologies within the Metaverse. In the course of the longitudinal experimental study, the results of the initial testing of 79 students during their undergraduate studies and the re-testing of 75 of these students during their…
Descriptors: Undergraduate Students, Graduate Students, Psychological Characteristics, Brain