Adaptive learning objects repository structure towards unified E-learning

2011 ◽  
Author(s):  
Nasim Matar
Keyword(s):  
Adaptive Learning ◽  
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.

scholarly journals Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020 ◽  
Vol 10 (2) ◽  
pp. 42
Author(s):  
Othmar Othmar Mwambe ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Learning Styles ◽  
Real Time ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Accurate Information ◽  
Interactive Learning Environment ◽  
Time Dynamic ◽  
E Learning ◽  
Time Basis ◽  
User Friendly

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

scholarly journals Content development approaches in e-learning lessons

2017 ◽  
Vol 2 (1) ◽  
pp. 342-348 ◽  
Author(s):  
Ştefania Kifor
Keyword(s):  
Adaptive Learning ◽  
Student Motivation ◽  
Online Courses ◽  
A Priori ◽  
Learning Objects ◽  
Management Systems ◽  
Content Development ◽  
E Learning ◽  
Level Of Knowledge ◽  
And Behavior

Abstract Developments in the IT field have led to the development and variety of e-learning objects so that video or gaming lessons are found in most online courses and are developed in order to maintain the student’s interest. The current Learning Management Systems challenges refer to how to generate a dynamic content that automatically adapts to the a priori level of a student’s knowledge and behavior during lessons. Starting from the model student, the component of adaptive learning, the aim of this paper is to keep student motivation by designing a course with content related to the level of knowledge of the students. At the same time, lessons combine elements of gamification with quizzes, textual contexts and infographics in order to make the content more varied, and in order for the student to be more engaged, to assimilate, to understand or to recall the concepts presented in the course.

scholarly journals Conceptual design of the new generation adaptive learning management system

2018 ◽  
Vol 7 (2.28) ◽  
pp. 129 ◽  
Author(s):  
Aleksandrs Gorbunovs ◽  
Zanis Timsans ◽  
Bruno Zuga ◽  
Viktors Zagorskis
Keyword(s):  
Adaptive Learning ◽  
Management System ◽  
Learning Management System ◽  
Knowledge Society ◽  
Learning Objects ◽  
High Performing ◽  
Learning Path ◽  
E Learning ◽  
Learning Management ◽  
And Behavior

Taking into account that each new day an amount of daily processed data grows exponentially, it is obvious that knowledge society needs flexible, effective and high performing tools to retrieve, learn and apply necessary information. Learning management systems become more and more intelligent and adaptive. Learning analytics instruments give course developers a possibility to assess and analyze learners’ activities and behavior patterns within e-learning environment, allowing to propose them personalized self-paced learning path and types of learning objects. This paper discusses challenges in development of adaptive learning management system and outlines its prospective models and properties.  

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.

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