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Showing 1 to 15 of 45 results Save | Export
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Ashraf, Erum; Manickam, Selvakumar; Karuppayah, Shankar; Malik, Sufiana Khatoon – Journal of Educators Online, 2023
As the drive to move from traditional face-to-face classroom learning to e-learning is ever in demand, the knowledge corpus exposed to students can be overwhelming because there is a need to automate certain functions of the e-learning framework. One of these functions is the course recommendation feature. Course recommendations help students save…
Descriptors: Electronic Learning, Cognitive Style, Student Behavior, Course Selection (Students)
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Erum Ashraf; Selvakumar Manickam; Khurrum Mahmood; Shams Ul Arfeen Laghari; Amber Baig – Journal of Educators Online, 2025
The recommendation of courses in an online educational system is done using different factors that include course length, duration, and learning material information. The course design is based on the instructor's teaching style, which may not necessarily correspond with the various learning preferences of students. Matching the learning style of…
Descriptors: Online Courses, Electronic Learning, Cognitive Style, Teaching Styles
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Xiang Wu; Huanhuan Wang; Yongting Zhang; Baowen Zou; Huaqing Hong – IEEE Transactions on Learning Technologies, 2024
Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate…
Descriptors: Artificial Intelligence, Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
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Wijaya, Adi; Setiawan, Noor Akhmad; Shapiai, Mohd Ibrahim – Electronic Journal of e-Learning, 2023
This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric…
Descriptors: Bibliometrics, Cognitive Style, Diagnostic Tests, Content Analysis
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Troussas, Christos; Chrysafiadi, Konstantina; Virvou, Maria – Education and Information Technologies, 2021
Personalized computer-based tutoring demands learning systems and applications that identify and keep personal characteristics and features for each individual learner. This is achieved by the technology of student modeling. One prevalent technique of student modeling is stereotypes. Furthermore, individuals differ in how they learn. So, the way…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style, Stereotypes
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Joy, Jeevamol; Renumol, V. G. – International Journal of Learning Technology, 2021
In the e-learning domain, content recommender systems had evolved to recommend relevant learning contents based on the learner preferences. One of the significant drawbacks of content recommenders in the e-learning domain is the new user cold-start problem. The objective of this study is to propose a recommendation model for addressing the…
Descriptors: Cognitive Style, Electronic Learning, Metadata, Integrated Learning Systems
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Hamza, Lamia; Tlili, Guiassa Yamina – International Journal of Web-Based Learning and Teaching Technologies, 2018
This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture…
Descriptors: Cognitive Style, Hypermedia, Educational Technology, Computer Oriented Programs
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El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf – Education and Information Technologies, 2019
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of…
Descriptors: Classification, Cognitive Style, Models, Electronic Learning
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Alhasan, Khawla; Chen, Liming; Chen, Feng – International Association for Development of the Information Society, 2017
Various learners with various requirements have led to the raise of a crucial concern in the area of e-learning. A new technology for propagating learning to learners worldwide, has led to an evolution in the e-learning industry that takes into account all the requirements of the learning process. In spite of the wide growing, the e-learning…
Descriptors: Semantics, Models, Cognitive Style, Electronic Learning
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Abuhamdieh, Ayman H.; Harder, Joseph T. – Contemporary Issues in Education Research, 2015
This paper proposes a meta-cognitive, systems-based, information structuring model (McSIS) to systematize online information search behavior based on literature review of information-seeking models. The General Systems Theory's (GST) prepositions serve as its framework. Factors influencing information-seekers, such as the individual learning…
Descriptors: Online Searching, Systems Approach, Metacognition, Models
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Liu, Sanya; Ni, Cheng; Liu, Zhi; Peng, Xian; Cheng, Hercy N. H. – International Journal of Distance Education Technologies, 2017
Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data…
Descriptors: Online Courses, Student Records, Learning Strategies, Cognitive Style
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Alhawiti, Mohammed M.; Abdelhamid, Yasser – Journal of Education and e-Learning Research, 2017
With the advent of web based learning and content management tools, e-learning has become a matured learning paradigm, and changed the trend of instructional design from instructor centric learning paradigm to learner centric approach, and evolved from "one instructional design for many learners" to "one design for one learner"…
Descriptors: Electronic Learning, Student Centered Learning, Instructional Design, Individualized Instruction
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Wanapu, Supachanun; Fung, Chun Che; Kerdprasop, Nittaya; Chamnongsri, Nisachol; Niwattanakul, Suphakit – Education and Information Technologies, 2016
The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system.…
Descriptors: Correlation, Cognitive Style, Resource Units, Foreign Countries
Barefah, Allaa; McKay, Elspeth – International Association for Development of the Information Society, 2016
Courseware designers aim to innovate information communications technology (ICT) tools to increase learning experiences, spending many hours developing eLearning programmes. This effort gives rise to a dynamic technological pedagogical environment. However, it is difficult to recognise whether these online programmes reflect an instructional…
Descriptors: Electronic Learning, Courseware, Instructional Design, Quasiexperimental Design
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Maaliw, Renato R. III; Ballera, Melvin A. – International Association for Development of the Information Society, 2017
The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…
Descriptors: Classification, Cognitive Style, Electronic Learning, Decision Making
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