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Showing 1 to 15 of 41 results Save | Export
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Bellarhmouch, Youssra; Jeghal, Adil; Tairi, Hamid; Benjelloun, Nadia – Education and Information Technologies, 2023
Nowadays, the need for e-learning is amplified, especially after the COVID-19 pandemic. E-learning platforms present a solution for the continuity of the learning process. Learners are using different platforms and tools for learning. For this, it is necessary to model the learner for the personalization of the learning environment according to…
Descriptors: Electronic Learning, Educational Environment, Models, Individualized Instruction
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Nicolas J. Tanchuk; Rebecca M. Taylor – Educational Theory, 2025
AI tutors are promised to expand access to personalized learning, improving student achievement and addressing disparities in resources available to students across socioeconomic contexts. The rapid development and introduction of AI tutors raises fundamental questions of epistemic trust in education. What criteria should guide students' critical…
Descriptors: Individualized Instruction, Artificial Intelligence, Technology Uses in Education, Tutors
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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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Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
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Xingle Ji; Lu Sun; Xueyong Xu; Xiaobing Lei – International Journal of Information and Communication Technology Education, 2024
This study examines the current research on educational data mining, educational learning support services, personalized learning services, and personalized learning paths in education. The authors aim to integrate personalized learning concepts into traditional support services by drawing on the latest theoretical and practical research. Using…
Descriptors: Information Retrieval, Data Analysis, Educational Research, Individualized Instruction
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Elizabeth B. Harkey; Angela T. Barlow; Victoria Groves-Scott – Mathematics Teacher: Learning and Teaching PK-12, 2023
Many teachers reported improved skills in using technology as a direct result of teaching virtually during the pandemic (Bushweller, 2020). The authors wondered which uses of technology would continue as teachers transitioned back into face-to-face classrooms. Recognizing that mere incorporation of technology into instruction does not guarantee…
Descriptors: Educational Technology, Mathematics Instruction, Technology Uses in Education, Models
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Tracy Bobko; Mikiko Corsette; Minjuan Wang; Erin Springer – IEEE Transactions on Learning Technologies, 2024
This article discusses the transformative impact of technology on knowledge acquisition and sharing, focusing on the emergence of the metaverse as a virtual community with vast potential for virtual learning. Learning in the metaverse is found to enhance engagement, motivation, and retention, while fostering 21st-century skills. It also offers…
Descriptors: Educational Innovation, Computer Simulation, Technology Uses in Education, Models
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Xie jingyi; Cavin Pamintuan – SAGE Open, 2024
This study investigates methods and effectiveness of implementing Differentiated Instruction (DI) in intelligent education based on learners' learning needs. The paper employs a mixed-method approach to collect and analyze data from various learner groups. Guided by the assessment results of the KANO model, the research prioritizes attributes for…
Descriptors: Individualized Instruction, Artificial Intelligence, Technology Uses in Education, Student Needs
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Alamri, Hamdan A.; Watson, Sunnie; Watson, William – TechTrends: Linking Research and Practice to Improve Learning, 2021
Personalized learning has the potential to transfer the focus of higher education from teacher-centered to learner-centered environments. The purpose of this integrative literature review was to provide an overview of personalized learning theory, learning technology that supports the personalization of higher education, current practices, as well…
Descriptors: Individualized Instruction, Educational Technology, Technology Uses in Education, Blended Learning
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Abby Mcguire; Warda Qureshi; Mariam Saad – International Journal of Technology in Education, 2024
Building on previous research that has demonstrated close connections between constructivism, technology, and artificial intelligence, this article investigates the constructivist underpinnings of strategically integrating GenAI experiences in higher educational contexts to catalyze student learning. This study presents a new model for leveraging…
Descriptors: Constructivism (Learning), Models, Artificial Intelligence, Individualized Instruction
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Liu, Xinyang; Ardakani, Saeid Pourroostaei – Education and Information Technologies, 2022
The purpose of this study is to propose an e-learning system model for learning content personalisation based on students' emotions. The proposed system collects learners' brainwaves using a portable Electroencephalogram and processes them via a supervised machine learning algorithm, named K-nearest neighbours (KNN), to recognise real-time…
Descriptors: Foreign Countries, Undergraduate Students, Electronic Learning, Artificial Intelligence
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Nóvoa, António; Alvim, Yara – Prospects: Quarterly Review of Comparative Education, 2020
This Viewpoint argues that the debates about the future of education and the need to rethink the school model started long before the pandemic crisis. But the situation we are experiencing has accelerated this need and showed that changes are possible. Consumerist trends in education have been accentuated, now with the massive use of digital…
Descriptors: COVID-19, Pandemics, Educational Trends, Futures (of Society)
Bander Ayed Allogmany – ProQuest LLC, 2023
Advances in data analytics and intelligent technologies are enabling smart learning environments that promote personalized learning. Personalized learning systems where learners engage with information in a manner tailored to their unique needs, goals, and abilities have garnered significant academic research attention. If students can achieve…
Descriptors: Individualized Instruction, Learning Management Systems, Artificial Intelligence, Technology Uses in Education
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Goria, Cecilia; Konstantinidis, Angelos – Turkish Online Journal of Distance Education, 2023
In spite of its increasing popularity, distance education faces challenges -- levels of digital literacy, access to technology, workload and time management, students' feelings of isolation and disconnection -- that can have a significant impact on the experience of the learners. In addressing these issues, we propose a pedagogical model for…
Descriptors: Models, Electronic Learning, Online Courses, Distance Education
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