Personalization Between Pedagogy and Adaptive Hypermedia System

Anoir, Lamya; Khaldi, Mohamed; Erradi, Mohamed

doi:10.1007/978-3-031-07969-6_17

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Learner profiles demonstrate how learners engage with content, including their behaviour and interaction patterns, as well as their preferred learning styles, which center on individual learner traits. When considering learning scenarios, they are delineated based on the information to be learned and the materials presented, all from the learner's perspective [24], [25]. The learning style is defined by a variety of attributes of the learner in connection to several aspects, each of which influences unique variations in the learning environment.…”

Section: Resultsmentioning

confidence: 99%

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Anoir,

Chelliq,

Khaldi

et al. 2024

IJCDS

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The impact of Artificial Intelligence (AI) has significantly remodelled the educational environment, with tutoring systems emerging as essential tools for adapting personalized learning tracks. This article explores the significant benefits achieved through the smooth integration of Intelligent Tutoring Systems (ITS) and Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR). Intelligent tutoring systems, which operate as an interactive platform, exploit the strength of educational data mining to construct meticulously personalized learner profiles. In tandem, multi-agent systems facilitate dynamic collaboration between a whole range of agents, including profile agents, recommendation agents, assessment agents and adaptation agents. This collaborative effort aims to orchestrate personalized learning activities that are finely adjusted to respond to the specific needs of each learner. The introduction of case-based reasoning elevates the sophistication of personalized learning by exploiting the depth of prior knowledge and experience. By systematically exploring a specific knowledge base of similar cases, the system provides recommendations and proven solutions. This ensures a learning experience that not only works with each learner's unique profile but also guarantees relevance and effectiveness. This article embarks on a comprehensive exploration of personalized learning activities by integrating ITS, MAS and CBR transparently. The main objective is to optimize learning engagement and effectiveness by proactively adapting educational content to the individual needs of each learner. This exploration is part of the continued focus on improving the educational experience through the advancement of AI and educational technologies.

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Section: Resultsmentioning

confidence: 99%

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Anoir,

Chelliq,

Khaldi

et al. 2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…The key to managing an overabundance of information in the knowledge-based society is perceived to be personalization (Ebner et al, 2011;Lamya et al, 2021).…”

Section: • E-learning 20mentioning

confidence: 99%

Symbiotic Evolution: The Inextricable Relation between the Web and E-Learning

Lamya Anoir,

Mohamed Khaldi,

Mohamed Erradi

2024

dirosat

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This article explores the strong relation between the evolution of the Web and that of e-learning. From the modest beginnings of e-learning, the Web has played a key role in widening access to education on a global scale. The emergence of online educational platforms, increased interactivity, adaptability thanks to artificial intelligence, mobility and accessibility have redefined e-learning. However, challenges remain, including online security and unequal access. In conclusion, the symbiosis between the Web and e-learning offers immense potential for the future of education, demonstrating the capacity of technological progress to transform our teaching methods.

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“…Teaching and curriculum design experts combine theoretical and empirical evidence to identify effective methods of knowledge mastery and transformation. Learning systems, such as adaptive instruction, (intelligent) tutoring, adaptive educational hypermedia and recommendation systems, adopt macro-level approaches being developed with more advanced capabilities for learners (Lamya et al, 2022;Sakkinah et al, 2022).…”

Section: Macro-adaptive Approach For Adaptation Of Knowledge Goalsmentioning

confidence: 99%

Reflection on the Construction and Impact of an Adaptive Learning Ecosystem

Zhang,

Galaup

2023

RITPU

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Emerging technologies are enabling adaptive learning systems to develop. This specific system consists of several models, including a learner model, domain knowledge model, instructional model, learning analytics model, and adaptive engine model. This paper reviewed multiple studies and highlighted the importance of refining each model in the context of creating a conceptual framework. We also proposed a metacognitive auxiliary model and an adaptive assessment model. The objective is to advance research into logical transitions in the internal structure of an adaptive learning ecosystem through the interpretation of different approaches, technologies, and solutions that facilitate the decision-making processes.

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Personalization Between Pedagogy and Adaptive Hypermedia System

Cited by 5 publications

References 36 publications

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Symbiotic Evolution: The Inextricable Relation between the Web and E-Learning

Reflection on the Construction and Impact of an Adaptive Learning Ecosystem

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