Ontology-based E-learning Research Articles

A Survey of Ontologies and Their Applications in e-Learning Environments

Ontology technology has been investigated in a wide range of areas and is currently being utilized in many fields. In the e-learning context, many studies have used ontology to address problems such as the interoperability in learning objects, modeling and enriching learning resources, and personalizing educational content recommendations. We systematically reviewed research on ontology for e-learning from 2008 to 2020. The review was guided by 3 research questions: “How is ontology used for knowledge modeling in the context of e-learning?”, “What are the design principles, building methods, scale, level of semantic richness, and evaluation of current educational ontologies?”, and “What are the various ontology-based applications for e-learning?” We classified current educational ontologies into 6 types and analyzed them by 5 measures: design methodology, building routine, scale of ontology, level of semantic richness, and ontology evaluation. Furthermore, we reviewed 4 types of ontology-based e-learning applications and systems. The observations obtained from this survey can benefit researchers in this area and help to guide future research.

Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem

E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.

An Ontology-Based E-Learning Framework for Healthcare Human Resource Management*

An Ontology-Based E-Learning Framework for Healthcare Human Resource Management*

An Upper Ontology for E-Learning Material Semantic Annotations

Recent research reveals a great interest to introduce the Semantic Web as a promising technology for realizing eLearning requirements. The new, dynamic and distributed business world has motivated the research on developing eLearning. ELearning is efficient, task relevant and just-in-time learning. It gives the learner the ability to efficiently access the related educational resources just-in-time from any place. The vision of the Semantic Web is to make the Web data not only processable but also understandable so it can be used by machines not just for display purposes but for automation, integration and reuse of data across various applications. This study investigates the role of Semantic Web in realizing the e-learning requirements. It proposes an ontology-based e-learning framework that considers the main three component roles of the e-learning architecture: an author, a learner and a repository. The study also shed the light on improving the conventional metadata standards that are used to describe learning materials by proposing a semantic-based ontology to describe three different dimensions of the learning material: content, context and structure. Adopting the proposed ontology would result in facilitating both the process of finding suitable learning materials to build up a certain course and the process of navigating through the learning course.

Ontology-Based e-Learning System for Personalized Learning

Ontology has recently gained popularity in building knowledge base because the description, localization and effective reuse of software patterns and systems of patterns can be approached through an ontology-based formalism. This paper designs ontology in the mobile phone domain for the construction of knowledge base, and presents a new method for knowledge assessment in which quizzing questions are drawn using the mobile phone ontology-based knowledge base to assess the sufficiency of professional knowledge of mobile phone salespersons. An automated learning service is then provided to offer users personalized learning contents subject to their knowledge deficiencies.

Ontology-Based Ecological System Model of e-Learning

The problems of too much emphasis on technology lack of people-centered educational philosophy, and the construction of the e-learning system lack of system concept and the overall view. In this paper, e-learning ecosystem models based on ontology in developed. This paper analyses the learner model specification established by the Committee of Chinese education information technology standards, the ontology-based e-learning ecological subject is constructed. After analyzing cognitive psychologists Anderson's Taxonomy of Educational Objectives, ecological environment ontology is promoted. On this basis, e-learning ecosystem model is designed. The paper focus on the use of e-learning ecological ontology in modern distance education ecosystem. This paper provides theoretical support for the building of e-learning ecosystem, and the technical guidance for the design of e-learning ecosystem.

An intelligent ontology-based e-learning tool for software risk management

In the past decade, ontologies have been widely used in the field of e-learning in order to create a semantic representation of knowledge and to provide a uniform structure to the course material. In order to provide a semantic representation of knowledge, this paper delineates a framework that uses an underlying ontology to structure the course material. In general, ontology-based e-learning tools are primarily static in nature, since they cannot learn new teaching materials and/or teaching strategies on their own. In order to overcome this limitation, an intelligent ontology-based e-learning system has been proposed. It possesses the ability to act in a genuinely intelligent manner by assessing the learners initially, and providing personalised suggestions to the learners indicating their strengths and weaknesses. This tool has been developed with the view of providing industry-oriented training to potential engineers and educating them on managing risks in software engineering. The constructed ontology as well as the developed system has been evaluated separately.

An Ontology-based E-learning Model for Effective Management of Learning Resources

A systematic approach to managing knowledge is an essential pre-requisite to enable learners and knowledge seekers to access relevant material as and when required in the current Internet era. To enable effective e-learning, ontologies can play a crucial role in the management of knowledge with respect to various learning strategies. While ontologies and the semantic web, which provide for semantic links, are increasingly used in business and research, their acceptance in e-learning is still at the preliminary stage. This paper reviews the relevant literature for identification of critical ontology factors supporting various learning strategies, based on which it proposes a comprehensive model to enable efficient reuse and sharing of knowledge in an e-learning environment.