Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems

2019 ◽  
Vol 23 (2) ◽  
pp. 249-268 ◽  
Author(s):  
Alessandro da S. Dias ◽  
Leandro K. Wives
Keyword(s):  
Recommender System ◽  
Learning Objects ◽  
Learning Systems ◽  
Social Signals ◽  
E Learning

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

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.

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 Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

2017 ◽  
pp. 1-23 ◽  
Author(s):  
Renuka Mahajan
Keyword(s):  
Data Mining ◽  
Recommender System ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Comprehensive List ◽  
Research Areas ◽  
New Models ◽  
E Learning ◽  
Research Intensity

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

Interoperability Approach in E-Learning Standardization Processes

2012 ◽  
pp. 542-560 ◽  
Author(s):  
Carmen Bao ◽  
José María Castresana
Keyword(s):  
Learning Environments ◽  
Relational Databases ◽  
Learning Objects ◽  
Learning Technologies ◽  
Learning Systems ◽  
Learning Resources ◽  
Virtual University ◽  
Schema Mappings ◽  
Learning Standards ◽  
E Learning

Providing interoperability by using standards and specifications for E-learning resources is an important element of the virtual learning environments (VLEs). In this context, a large number of international organizations develop specifications that provide principles for reaching a common “language” to be used in exchanging resources among the virtual university. In this paper we turn your attention to an approach and reference for providing interoperability in different standards. The establishment of E-learning standards has promised to improve interoperability between E-learning systems, but can only be done through enforcement of these standards. Many existing E-learning systems are built on top of relational databases, and it is possible a framework which matches XML Schemas (from learning standards) and relational schemas semi-automatically. This type of framework can provide translation between learning objects and relational databases as well as an interface to manually refine existing schema mappings. The focus is E-learning standardization and synchronization in the international and national levels. The work presents a brief updated review and it presents some new challenges, concerning the E-learning standardization processes. This research is in the area of E-learning standardization and issue is one aspect of great interest for all organizations, authorities and experts working in the field of education. Moreover, the most recognized approaches are introduced in order to improve and optimize the management of the E-learning processes. While the establishment of E-learning standards has promised to improve interoperability between E-learning systems, and obviously, this can only be done through enforcement of E-learning standards and E-learning standardization processes. The aim of this work is to discover the useful E-learning technologies as technological tools for teaching. Therefore, teachers must keep in mind clearly that they must optimize teaching by means of them, such as an improvement of quality education for current society in terms of competences, as connections with the current reality that students spent long hours using them. It starts with a brief background to worldwide standardization activities in the field of educational technologies as means of enhancing the accessibility, interoperability, durability, reusability and efficiency of E-learning resources, but more important new demands and problems to be tackled are reviewed. Finally, experimental dates from studies have shown that it is useful a framework that also provides translation between learning objects and relational databases, as well as an interface to manually refine existing schema mappings.

Ontologies in Intelligent Learning Systems

2012 ◽  
pp. 31-48
Author(s):  
Boryana Deliyska ◽  
Peter Manoilov
Keyword(s):  
Semantic Web ◽  
Learning Process ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning Object ◽  
Structural Learning ◽  
Principal Role ◽  
Web Resources ◽  
Human Tutor ◽  
E Learning

The intelligent learning systems provide a direct customized instruction to the learners without intervention of human tutor on the base of Semantic Web resources. The principal role ontologies play in these systems is as an instrument for modeling learning process, learner, learning objects, and resources. In the chapter, a variety of relationships and conceptualizations of ontologies used in the intelligent learning systems are investigated. The utilization of domain and application ontologies in learning object building and knowledge acquisition is represented. The conceptualization of domain ontologies in e-learning is presented by the upper levels of its taxonomies. Moreover, a method and an algorithm intended for generation of application ontologies of structural learning objects (curriculum, syllabus, topic plan, etc.) are developed. Examples of curriculum and syllabus application ontologies are given. Further these application ontologies are used for structural learning object generation.

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