E-Learning Objects

2004 ◽  
Vol 46 (3) ◽  
pp. 222-224 ◽  
Author(s):  
Gerhard F. Knolmayer
Keyword(s):  
Learning Objects ◽  
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.

Evaluasi Aksesibilitas Learning Object Berdasarkan Web Content Accessibility Guidelines 2.0

2017 ◽  
Vol 9 (2) ◽  
pp. 67-71
Author(s):  
Herru Darmadi ◽  
Yan Fi ◽  
Hady Pranoto
Keyword(s):  
Learning Objects ◽  
Authoring Tool ◽  
Learning Object ◽  
Web Content ◽  
Web Browser ◽  
E Learning ◽  
Index Terms ◽  
Accessibility Guidelines ◽  
Content Accessibility

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web

Learning objects management and evaluatio in an e-learning environment

1970 ◽  
Vol 6 (2) ◽  
Author(s):  
Hugo Rego ◽  
Tiago Moreira ◽  
Francisco José García-Peñalvo
Keyword(s):  
Knowledge Management ◽  
Learning Environment ◽  
Learning Process ◽  
Group Learning ◽  
Learning Objects ◽  
Authoring Tools ◽  
Learning Platform ◽  
E Learning ◽  
Teaching Learning ◽  
Educational Contents

The main aim of the AHKME e-learning platform is to provide a system with adaptive and knowledge management abilities for students and teachers. This system is based on the IMS specifications representing information through metadata, granting semantics to all contents in the platform, giving them meaning. In this platform, metadata is used to satisfy requirements like reusability, interoperability and multipurpose. The system provides authoring tools to define learning methods with adaptive characteristics, and tools to create courses allowing users with different roles, promoting several types of collaborative and group learning. It is also endowed with tools to retrieve, import and evaluate learning objects based on metadata, where students can use quality educational contents fitting their characteristics, and teachers have the possibility of using quality educational contents to structure their courses. The learning objects management and evaluation play an important role in order to get the best results in the teaching/learning process.

Augmented Reality in Online Teacher Traning for Digital Competence

2021 ◽  
Vol LXIV (5) ◽  
pp. 503-519
Author(s):  
Evgenia Goranova ◽  
◽  
Valentina Voinohovska ◽  
Keyword(s):  
Augmented Reality ◽  
Learning Objects ◽  
Variable Component ◽  
Qr Code ◽  
Digital Learning ◽  
Digital Competence ◽  
Video Information ◽  
Field Independent ◽  
E Learning ◽  
Teacher Traning

The article presents an approach applied in the online training of pre-service teachers to acquire digital competence. The content of the concept of ‘digital competence’ in its sustainable and variable component is clarified. The understanding of ‘augmented reality’ to e-learning objects is presented. Two forms of ‘augmented reality’ have been proposed to visualize video information to a clarified concept. The first one is presented via a QR code for quick access and applicable for mobile learning. The other is provided by icons and is applicable to e-learning with a computer. It is believed that ‘augmented reality’ can differentiate students’ online learning according to the field-dependent and field-independent cognitive style and their preferences on the use of different digital learning devices.

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 The Model for Learning Objects Design Based on Semantic Technologies

2017 ◽  
Vol 12 (2) ◽  
pp. 227
Author(s):  
Daina Gudoniene ◽  
Rytis Maskeliunas ◽  
Danguole Rutkauskiene
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Learning Environments ◽  
Information And Communication Technologies ◽  
Learning Objects ◽  
Semantic Web Services ◽  
Innovative Learning ◽  
Modern Learning ◽  
E Learning ◽  
Integration Of Learning

The paper presents a comparison of state of the art methods and techniques on implementation of learning objects (LO) in the field of information and communication technologies (ICT) using semantic web services for e-learning. The web can serve as a perfect technological environment for individualized learning which is often based on interactive learning objects. This allows learners to be uniquely identified, content to be specifically personalized, and, as a result, a learner’s progress can be monitored, supported, and assessed. While a range of technological solutions for the development of integrated e-learning environments already exists, the most appropriate solutions require further improvement on implementation of novel learning objects, unification of standardization and integration of learning environments based on semantic web services (SWS) that are still in the early stages of development. This paper introduces a proprietary architectural model for distributed e-learning environments based on semantic web services (SWS), enabling the implementation of a successive learning process by developing innovative learning objects based on modern learning methods. A successful technical implementation of our approach in the environment of Kaunas University of Technology is further detailed and evaluated.

scholarly journals From Cognitive Landscapes to Digital Hyperscapes

2003 ◽  
Vol 4 (2) ◽  
Author(s):  
Jose Bidarra ◽  
Ana Dias
Keyword(s):  
Learning Styles ◽  
Learning Management Systems ◽  
Learning Objects ◽  
Management Systems ◽  
Digital Information ◽  
Cognitive Profiles ◽  
E Learning ◽  
Learning Management ◽  
Key Terms ◽  
Massive Information

<P> The widespread diffusion of e-Learning in organizations has encouraged the discovery of more effective ways for conveying digital information to learners, for instance, via the commonly called Learning Management Systems (LMS). A problem that we have identified is that cognitive variables and pedagogical processes are rarely taken into consideration and sometimes are confused with the mere use by learners of “diversified” hypermedia resources. Within the context of widespread dissemination of multimedia content that has followed the emergence of massive information resources, we discuss the need for more powerful and effective learner-centered tools capable of handling all kinds of design configurations and learning objects. </p> <P class=abstract><B>Key Terms: </B>cognitive profiles, learning styles, mind mapping, multimedia and hypermedia content, hyperscapes, e-Learning, learning objects, Learning Management Systems (LMS).</P>

scholarly journals INTELLIGENT VOICE-BASED E-EDUCATION SYSTEM: A FRAMEWORK AND EVALUATION

2010 ◽  
pp. 327-334
Author(s):  
Ambrose Azeta ◽  
Charles K. Ayo ◽  
Aderemi Aaron Anthony Atayero ◽  
Nicholas Ikhu-Omoregbe
Keyword(s):  
Education System ◽  
User Satisfaction ◽  
Visually Impaired ◽  
Learning Objects ◽  
Usability Study ◽  
Web Based ◽  
System A ◽  
Autonomous Decision ◽  
E Learning ◽  
Phone Access

Voice-based web e-Education is a technology-supported learning paradigm that allows phone-access of learners to e-Learning web-based applications. These applications are designed mainly for the visually impaired. They are however lacking in attributes of adaptive and reusable learning objects, which are emerging requirements for applications in these domain. This paper presents a framework for developing intelligent voice-based applications in the context of e-Education. The framework presented supports intelligent components such as adaptation and recommendation services. A prototype Intelligent Voice-based E-Education System (iVEES) was developed and subjected to test by visually impaired users. A usability study was carried out using the International Standard Organization’s (ISO) 9241-11 specification to determine the level of effectiveness, efficiency and user satisfaction. Report of our findings shows that the application is of immense benefit, based on the system’s inherent capacity for taking autonomous decision that are capable of adapting to users’ requests.

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.

Sign in / Sign up

Export Citation Format

Share Document