scholarly journals From Learning Objects to Adaptive Content Services for E-Learning

2008 ◽  
pp. 243-261 ◽  
Author(s):  
Peter Brusilovsky ◽  
Vincent P. Wade ◽  
Owen Conlan
Keyword(s):  
Learning Objects ◽  
Learning Systems ◽  
Educational Systems ◽  
Next Generation ◽  
Distributed Architecture ◽  
E Learning ◽  
Adaptive Educational Systems ◽  
New Generation ◽  
The Way ◽  
Adaptive Content

This paper argues that a new generation of powerful E-learning systems could start on the crossroads of two emerging fields: courseware re-use and adaptive educational systems. We argue for a new distributed architecture for E-learning systems based on the idea of adaptive reusable content services. This paper discusses problems that have to be solved on the way to the new organization of E-learning and reviews existing approaches and tools that are paving the way to next generation E-learning systems. It also presents two pioneer systems - APeLS and KnowledgeTree that have attempted to develop a new service-based architecture for adaptive E-learning.

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

scholarly journals Learner-User Satisfaction Survey in the AdaptWeb Platform using the Learner Choices from Learner-Driven Learning

2020 ◽  
Vol 28 ◽  
pp. 420-435
Author(s):  
Alessandro Da Silveira Dias ◽  
Leandro Krug Wives
Keyword(s):  
User Satisfaction ◽  
Serious Games ◽  
Learning Systems ◽  
Online Course ◽  
Satisfaction Survey ◽  
Learning Paradigm ◽  
Making Choices ◽  
E Learning ◽  
Definition Of ◽  
The Way

In this paper, we review the definition of the learner choices from the Learner-driven Learning paradigm for e-learning systems. After this, we analyze how different categories of e-learning systems enable the user to make these choices, such as Serious Games. We present in detail how AdaptWeb platform makes available these choices to learner users. Additionally, we present a satisfaction survey performed after an online course on AdaptWeb platform. The survey questions were about making choices during learning and about the way AdaptWeb makes the choices available to learner-users. Summarizing the results, students enjoyed being able to make choices about their own learning and felt that this possibility was beneficial to their learning. Moreover, they liked the way AdaptWeb makes the choices available to students. Most of the students found the system easy to use, intuitive, and the student's choices were explicit and easy to take.

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.

Techniques, Technologies and Patents Related to Intelligent Educational Systems

2011 ◽  
pp. 1-28
Author(s):  
Jim Prentzas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Artificial Intelligence ◽  
Learning Systems ◽  
Future Research ◽  
Educational Systems ◽  
Learner Characteristics ◽  
Tutoring Systems ◽  
Artificial Intelligence Methods ◽  
E Learning ◽  
Future Research Directions ◽  
Intelligent Methods

E-learning systems play an increasingly important role in lifelong learning. Tailoring the learning process to individual needs is a key issue in such systems. Intelligent Educational Systems (IESs) are e-learning systems employing Artificial Intelligence methods to effectively adapt to learner characteristics. Main types of IESs are Intelligent Tutoring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent methods. In this chapter, the authors present technologies and techniques used in the primary modules of IESs and survey corresponding patents. They present issues and problems involving specific IES modules as well as the overall IES. The authors discuss solutions offered for such issues by Artificial Intelligence methods and patents. They also discuss categorization aspects of patents related to IESs and briefly present the work described in some representative patents. Lastly, the authors outline future research directions regarding IESs.

Indexing Serious Games to Meet Adaptive E-Learning System Needs

2020 ◽  
pp. 284-303
Author(s):  
Yassine El Borji ◽  
Mohammed Khaldi
Keyword(s):  
Serious Games ◽  
Main Idea ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
The Other ◽  
E Learning ◽  
Technical Sense ◽  
Application Profile ◽  
Educational Field

This chapter aims to strengthen the integration of serious games in the educational field by providing tools to monitor and assist the progress of learners/players. The main idea is to address the integration aspects and the deployment of serious games in adaptive e-learning systems based on the automatic package and the export of serious games as reusable learning objects (LO). This integration will allow SGs to benefit from the tracking and support features offered by the LMS. On the other hand, LMS can supplement their training offer and reach a certain maturity. The approach aims to meet the specific needs of SGs in terms of metadata so that they can be described, indexed, and capitalized. This is a new application profile of the IEEE LOM standard entitled “SGLOM” integrating fields to describe SGs not only in a technical sense but also by examining the pedagogical and playful criteria. The authors also focus on the integration and extraction aspects of SGs in an LMS using the ADL SCORM 2004 data model that defines how content can be packaged as a SCORM PIF (package interchange file).

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.

scholarly journals Towards an Educational Platform with Real-Time Collaboration and Monitoring of Students Achievement

2020 ◽  
Author(s):  
Paulo De Souza ◽  
Wagner Marques ◽  
Jaline Mombach
Keyword(s):  
Software Engineering ◽  
Real Time ◽  
Learning Environments ◽  
Virtual Learning ◽  
Learning Objects ◽  
Virtual Learning Environments ◽  
Educational Systems ◽  
E Learning

Several studies have been undertaken aiming to improve the efficiency of e-learning through the development of features to Virtual Learning Environments. However, such researches have no focus on the use of collaboration of learning objects and analysis of students’ progress in real-time. Hence, this paper presents an educational platform that allows real-time co-authorship and monitoring of students’ progress in learning objects, through the implementation of software engineering techniques and patterns designed for educational systems.

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