A Prediction Mechanism of Adaptive Learning Content in the Scalable E-Learning Environment

2007 ◽  
Author(s):  
Chih-Ping Chu ◽  
Yi-Chun Chang
Keyword(s):  
Learning Environment ◽  
Adaptive Learning ◽  
Learning Content ◽  
E Learning ◽  
Prediction Mechanism

Intelligent Adaptable e-Assessment for Inclusive e-Learning

2020 ◽  
pp. 1185-1199
Author(s):  
Lilyana Nacheva-Skopalik ◽  
Steve Green
Keyword(s):  
Human Rights ◽  
Learning Environment ◽  
Open Source ◽  
Learning System ◽  
Access To Education ◽  
Education Systems ◽  
Learning Content ◽  
E Learning ◽  
Individual Needs ◽  
The Individual

Access to education is one of the main human rights. Everyone should have access to education and be capable of benefiting from it. However there are a number who are excluded, not because of a lack of ability but simply because they have a disability or specific need which current education systems do not address. A learning system in which content, tools and interfaces can be personalised and adapted to the individual needs and preferences of a variety of learners, including those with disabilities, becomes inclusive. Assessment is an integral part of an e-learning environment and therefore it has to provide not only inclusive e-learning content but also inclusive e-assessment. The proposed research investigates an intelligent adaptable e-learning system for assessing students' level of skill, knowledge and understanding regardless of their disabilities or accessibility needs. It is based on an innovative use of world's first open source adaptable widget design and authoring toolkit (WIDGaT) as the prototyping environment.

scholarly journals Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

2016 ◽  
Vol 6 (2) ◽  
pp. 81-101 ◽  
Author(s):  
Khalid Almohammadi ◽  
Hani Hagras ◽  
Daniyal Alghazzawi ◽  
Ghadah Aldabbagh
Keyword(s):  
Fuzzy Logic ◽  
Learning Environment ◽  
Adaptive Learning ◽  
Learning System ◽  
Superior Performance ◽  
Teaching Models ◽  
Learning Platform ◽  
E Learning ◽  
Interval Type

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

The Construction of an Ontology-Based Ubiquitous Learning Grid

2011 ◽  
pp. 191-212
Author(s):  
Ching-Jung Liao ◽  
Chien-Chih Chou ◽  
Jin-Tan David Yang
Keyword(s):  
Learning Environment ◽  
Ubiquitous Computing ◽  
Adaptive Learning ◽  
Experimental Results ◽  
Background Information ◽  
Ubiquitous Learning ◽  
Ubiquitous Computing Environment ◽  
Java Programming ◽  
Learning Content ◽  
Information Awareness

The purpose of this study is to incorporate adaptive ontology into ubiquitous learning grid to achieve seamless learning environment. Ubiquitous learning grid uses ubiquitous computing environment to infer and determine the most adaptive learning contents and procedures in anytime, any place and with any device. To achieve the goal, an ontology-based ubiquitous learning grid (OULG) was proposed to resolve the difficulties concerning how to adapt learning environment for different learners, devices, places. OULG through ontology identifying and adapting in the aspects of domain, task, devices, and background information awareness, so that the adaptive learning content could be delivered. A total of 42 freshmen participate in this study for four months to learn Java programming. Both of pretesting and posttesting are performed to ensure that the OULG is useful. Experimental results demonstrate that OULG is feasibile and effective in facilitating learning.

Realization of Adaptive Learning Content Based on Learning Object and Domain Ontology

2010 ◽  
Vol 171-172 ◽  
pp. 527-530
Author(s):  
Xu Wei
Keyword(s):  
Information Technology ◽  
Adaptive Learning ◽  
New Technologies ◽  
Domain Ontology ◽  
Learning System ◽  
Learning Object ◽  
Modern Education ◽  
Learning Content ◽  
E Learning ◽  
Adaptive Content

With the evolution of information technology, E-learning has spread rapidly. Traditional E-learning has proved not fit requirement of modern education, new trend of developing the adaptive and customized E-learning system draws more and more attentions from researchers and practitioners. In this paper, we proposed an E-learning system which can provide adaptive content according to different situations of user. To achieve the adaptive E-learning system, two new technologies have been adopted by the system: learning object and domain ontology. Using these two technologies the system can provide intelligent learning content based on the user’s context, knowledge’s context. The paper also presents a scenario to demonstrate the step of production of adaptive learning content.

scholarly journals Learning Content Model: from Concept Structuring to Adaptive Learning

2021 ◽  
Vol 25 (1) ◽  
pp. 28-39
Author(s):  
J. V. Vainshtein ◽  
R. V. Esin ◽  
G. M. Tsibulsky
Keyword(s):  
Graph Theory ◽  
Adaptive Learning ◽  
Educational Process ◽  
Subject Domain ◽  
Research Issues ◽  
Content Model ◽  
Electronic Environment ◽  
Learning Content ◽  
E Learning ◽  
The Subject

The aim of the study. In modern conditions of changing the global “educational landscape”, the leading trend in building a new educational process management system is the personalization of the educational process in the electronic environment. New pedagogical technologies and innovative forms of organizing personalized learning in the electronic environment are developing, one of which is adaptive learning. The development of the structure and content of adaptive e-learning courses, the design and implementation of an educational strategy, teaching methods, and approaches to assessing results is determined by the model of its subject domain - the model of learning content. The aim of the study is to develop an approach to constructing the learning content model of an adaptive e-learning course that provides a formalized presentation of the educational material of the discipline and the construction of a logically based strategy for its study. Materials and methods. Methodological basis of research methods make up the logical-epistemological analysis and graph theory, and comparative analysis of psychological and pedagogical, scientific and methodical works, analysis of regulatory documents on research issues, professional and federal educational standards of higher education. Results. A feature of the author's approach is structuring of the subject domain in the form of a sequence of terms (training objects) of the learning content, studied in a certain order and presented in several versions of the presentation. The presented model for constructing the learning content of the academic discipline differs from the wellknown ones by the presence of logical ordering of concepts based on the integration of logic methods of concept analysis, using logical and epistemological methods for correlating the volume and content of concepts with the methods of graph theory and hypergraphs. The definition of educational objects of a tree (hypergraphic tree) of terms is obtained on the basis of a concept tree of discipline with a further determination of the sequence of their study, as well as the inclusion of a phenomenological and structural model in the content of the educational object, which allows to identify and disclose the essence of each studied concept within the framework of the subject domain of discipline. Conclusion. The proposed approach has been tested in the educational process of the program 09.03.02 – “Information systems and technologies” at the Siberian Federal University. Analysis of observations and evaluating the effectiveness of adaptive e-learning course in the educational process was carried out using the Kruskal-Wallis test by ranks. As a result of the experiment, it was revealed that at the end of the experiment, the control and experimental groups were statistically significantly different, which allowed us to conclude that the adaptive e-learning course developed in the educational process was effective. Adaptive e-learning courses, which are based on the approach proposed by the authors, made it possible to present educational content in the form of logically integral micro portions, which allow the adaptation of the educational environment to the individual characteristics of students. In the future, the proposed approach can contribute to development of personalized adaptive learning university ecosystems under digitalization formation.

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