scholarly journals To support adaptivity in agent-based learning systems - the use of learning objects and learning style

2005 ◽  
Author(s):  
Shanghua Sun ◽  
M. Joy ◽  
N. Griffiths
Keyword(s):  
Learning Style ◽  
Learning Objects ◽  
Learning Systems ◽  
Agent Based

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

scholarly journals HyCo – An Authoring Tool to Create Semantic Learning Objects for Web-Based E-learning Systems

2004 ◽  
pp. 344-348 ◽  
Author(s):  
Francisco J. García ◽  
Adriana J. Berlanga ◽  
Maria N. Moreno ◽  
Javier García ◽  
Jorge Carabias
Keyword(s):  
Learning Objects ◽  
Learning Systems ◽  
Authoring Tool ◽  
Web Based ◽  
Semantic Learning ◽  
E Learning

Ubiquitous Multi-Agent Context-Aware System for Enhancing Teaching-Learning Processes Adapted to Student Profile

2014 ◽  
pp. 237-253
Author(s):  
Demetrio Ovalle ◽  
Oscar Salazar ◽  
Néstor Duque
Keyword(s):  
Learning Objects ◽  
Learning Systems ◽  
Mobile Technologies ◽  
Context Aware ◽  
Student Centered ◽  
Student Profile ◽  
Multi Agent ◽  
Use Of The Internet ◽  
Context Aware System ◽  
Teaching Learning

The need for ubiquitous systems that allow access to computer systems from anywhere at anytime and the massive use of the Internet has prompted the creation of e-learning systems that can be accessed from mobile smart phones, PDA, or tablets, taking advantage of the current growth of mobile technologies. The aim of this chapter is to present the advantages brought by the integration of ubiquitous computing-oriented along with distributed artificial intelligence techniques in order to build student-centered context-aware learning systems. Based on this model, the authors propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student’s profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. Finally, the authors present some solutions considering the functionalities that a u-learning multi-agent context-aware system should exhibit.

Technological Evaluation and Optimization of E-Learning Systems Components

2011 ◽  
pp. 150-173 ◽  
Author(s):  
Eugenijus Kurilovas ◽  
Valentina Dagiene
Keyword(s):  
Quality Evaluation ◽  
Evaluation Criteria ◽  
Learning Objects ◽  
Learning Systems ◽  
Main Research ◽  
Evaluation Models ◽  
E Learning ◽  
Multiple Criteria Evaluation ◽  
Technological Evaluation ◽  
Learning Software

The main research objective of the chapter is to provide an analysis of the technological quality evaluation models and make a proposal for a method suitable for the multiple criteria evaluation (decision making) and optimization of the components of e-learning systems (i.e. learning software), including Learning Objects, Learning Object Repositories, and Virtual Learning Environments. Both the learning software ‘internal quality’ and ‘quality in use’ technological evaluation criteria are analyzed in the chapter and are incorporated into comprehensive quality evaluation models. The learning software quality evaluation criteria are further investigated in terms of their optimal parameters, and an additive utility function based on experts’ judgements, including multicriteria evaluation, numerical ratings, and weights, is applied to optimize the learning software according to particular learners’ needs.

Development of Adaptive Kanji Learning System for Mobile Phone

2012 ◽  
pp. 181-192
Author(s):  
Mengmeng Li ◽  
Hiroaki Ogata ◽  
Bin Hou ◽  
Satoshi Hashimoto ◽  
Yuqin Liu ◽  
...  
Keyword(s):  
Language Learning ◽  
Mobile Phone ◽  
Learning Style ◽  
Adaptive Learning ◽  
Learning Systems ◽  
Learning System ◽  
Difficulty Level ◽  
Proficiency Level ◽  
Main Emphasis ◽  
Adaptive Learning System

This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects: sending the contents to a learner following his or her interests, adjusting the difficulty level of the tests to suit the learner’s proficiency level, and adapting the system to his or her learning style. Additionally, this system has already been evaluated by the learners and the results show that most of them benefited from the system and would like to continue using it.

Toward an Agent-Based Platform for Learning Objects Management

2006 ◽  
pp. 111-119
Author(s):  
Nicandro Farias Mendoza ◽  
Gabriel Cruz ◽  
Orvil Ceja ◽  
Miguel Díaz ◽  
José Macías
Keyword(s):  
Learning Objects ◽  
Agent Based

A Model for an Adaptive Hypermedia Learning System Based on Data Mining Technique

2017 ◽  
pp. 65-85
Author(s):  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Learning Performance ◽  
Adaptive Hypermedia ◽  
Data Mining Technique ◽  
Pedagogical Strategies ◽  
Hypermedia Learning

Since the learning style of each learner is different. Adaptive hypermedia learning system (AHLS) must fit different learner's needs. A number of AHLS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: less attention was paid to the relationship between learning styles and learning performance. This paper proposes an AHLS model based on learning styles and learning performance. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her learning style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

Social and Affective Agents to Motivate Collaboration on Agent-Based Intelligent Tutoring Systems

2008 ◽  
pp. 201-227
Author(s):  
Elisa Boff ◽  
Cecília Dias Flores
Keyword(s):  
Learning Style ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Affective State ◽  
Educational Systems ◽  
Probabilistic Networks ◽  
Tutoring Systems ◽  
Agent Based ◽  
Social Agent ◽  
The Social

This chapter presents a social and affective agent, named social agent, that has been modeled using probabilistic networks in order to support and motivate collaboration in an intelligent tutoring system (ITS). The social agent suggests to students a workgroup to join in. Our testbed ITS is called AMPLIA, a probabilistic multiagent environment to support the diagnostic reasoning development and the diagnostic hypotheses modeling of domains with complex and uncertain knowledge, as the medical area. The AMPLIA environment is one of the educational systems, integrated in Portedu, which is a Web portal that provides access to educational contents and systems. The social agent belongs to Portedu platform and it is used by AMPLIA. The social agent reasoning is based on individual aspects, such as learning style, performance, affective state, personality traits, and group aspects, as acceptance and social skills. The chapter also presents some experiments using AMPLIA, and results obtained by the social agent.

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