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- W3130960873 abstract "This paper presents a model framework for achieving semantic adaptation in e-learning systems using multiple parameters for personalisation. The proposed model, which utilises semantic technologies, aims to boost learning experiences and outcomes within the process of learning. This is often achieved through a mechanism that adapts the educational contents of a course in keeping with student’s preferences expressed by multiple parameters (such as their learning styles, media preferences, level of data, language, etc.) The variation process involves real-time mapping of learning resources and student data, semantic annotation, metadata enrichment of learning resources, creation of student profile with relevant preferences, and personalisation of every course in step with the foremost suitable (or preferred) parameters. Achieving this entailed the creation of an ontology and several other modules that work in the background of the adaptive process." @default.
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- W3130960873 date "2021-01-01" @default.
- W3130960873 modified "2023-09-24" @default.
- W3130960873 title "A Semantic Approach to Multi-parameter Personalisation of E-Learning Systems" @default.
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- W3130960873 doi "https://doi.org/10.1007/978-3-030-68527-0_24" @default.
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