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Abdessamad Chanaa; Nour-eddine El Faddouli – Smart Learning Environments, 2024
The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate…
Descriptors: Artificial Intelligence, Prerequisites, Metadata, Electronic Learning
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Amane, Meryem; Aissaoui, Karima; Berrada, Mohammed – International Journal of Information and Learning Technology, 2023
Purpose: Together, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience. Design/methodology/approach: The development of LOs and…
Descriptors: Electronic Learning, Resource Units, Metadata, Algorithms
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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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Clavié, Benjamin; Gal, Kobi – International Educational Data Mining Society, 2020
We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students' online behaviour and meta-data about students and educational content. The model uses these representations to predict student performance. We evaluate DPE on standard datasets from the literature, showing superior performance to the…
Descriptors: Student Behavior, Electronic Learning, Metadata, Prediction
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Solomou, Georgia; Pierrakeas, Christos; Kameas, Achilles – Educational Technology & Society, 2015
The ability to effectively administrate educational resources in terms of accessibility, reusability and interoperability lies in the adoption of an appropriate metadata schema, able of adequately describing them. A considerable number of different educational metadata schemas can be found in literature, with the IEEE LOM being the most widely…
Descriptors: Educational Resources, Electronic Learning, Metadata, Profiles
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Somyürek, Sibel – International Review of Research in Open and Distributed Learning, 2015
This paper aims to give a general review of existing literature on adaptive educational hypermedia systems and to reveal technological trends and approaches within these studies. Fifty-six studies conducted between 2002 and 2012 were examined, to identify prominent themes and approaches. According to the content analysis, the new technological…
Descriptors: Educational Trends, Hypermedia, Educational Technology, Literature Reviews
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Hsu, I-Ching – Educational Technology & Society, 2012
The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability, and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM lacks the…
Descriptors: Electronic Learning, Metadata, Knowledge Representation, Artificial Intelligence
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Biletskiy, Yevgen; Baghi, Hamidreza; Steele, Jarrett; Vovk, Ruslan – Interactive Technology and Smart Education, 2012
Purpose: Presently, searching the internet for learning material relevant to ones own interest continues to be a time-consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is…
Descriptors: Internet, Search Engines, Online Searching, Electronic Libraries
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Jovanovic, Jelena; Gasevic, Dragan; Torniai, Carlo; Bateman, Scott; Hatala, Marek – Interactive Learning Environments, 2009
Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments…
Descriptors: Models, Interaction, Educational Technology, Design Requirements
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Dietze, Stefan; Gugliotta, Alessio; Domingue, John – Journal of Interactive Media in Education, 2007
IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…
Descriptors: Learning Strategies, Learning Processes, Metadata, Resource Allocation
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Aroyo, Lora; Dicheva, Darina – Educational Technology & Society, 2004
The big question for many researchers in the area of educational systems now is what is the next step in the evolution of e-learning? Are we finally moving from a scattered intelligence to a coherent space of collaborative intelligence? How close we are to the vision of the Educational Semantic Web and what do we need to do in order to realize it?…
Descriptors: Semantics, Semiotics, Internet, Electronic Learning
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers