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2020 | OriginalPaper | Chapter

Multi-Agent System of an Adaptive Learning Hypermedia Based on Incremental Hybrid Case-Based Reasoning

Authors : Nihad El Ghouch, Mohamed Kouissi, El Mokhtar En-Naimi

Published in: Innovations in Smart Cities Applications Edition 3

Publisher: Springer International Publishing

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Abstract

Adaptive learning systems are defined by their ability to adjust the learning process to the needs, characteristics and learning styles of learners. These systems must acquire and process all the information relating to the learning process characterizing the temporal and dynamic evolution of learners’ behavior so that they make appropriate decisions for each change detected during the learning act and ensure deterministic and predictable behaviors, by offering personalized learning paths in real-time.
To ensure this customization of the courses, we propose an architecture of a multi-agent adaptive learning system based on the Reasoning from Hybrid Incremental Case which makes it possible to take the right decision in real time in the face of a dynamic learning situation, by performing a classical cycle or a dynamic cycle of reasoning from case. This decision will be made by assigning at each step of the Reasoning from Hybrid Incremental Case at least one agent performing a well-defined task and collaborating with other agents to dynamically support the learner during the learning process by proposing them a personalized learning path in real time.

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Metadata
Title
Multi-Agent System of an Adaptive Learning Hypermedia Based on Incremental Hybrid Case-Based Reasoning
Authors
Nihad El Ghouch
Mohamed Kouissi
El Mokhtar En-Naimi
Copyright Year
2020
Book
Innovations in Smart Cities Applications Edition 3

Print ISBN: 978-3-030-37628-4

Electronic ISBN: 978-3-030-37629-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37629-1_12

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