Using Ontology for Personalized E-Learning in K-12 Education

2010 ◽  
pp. 301-309 ◽  
Author(s):  
Petek Askar ◽  
Arif Altun ◽  
Kagan Kalinyazgan ◽  
S. Serkan Pekince
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Environments ◽  
Ontology Development ◽  
Future Directions ◽  
Web Based ◽  
Implementation Processes ◽  
Web Environment ◽  
E Learning ◽  
K 12

This chapter introduces the development of a K-12 education ontology for e-learning environments. It presents design and implementation processes, followed by several recommendations for future directions for ontology development. E-learning environments incorporate the notion of semantic Web-based ontologies into their future directions. Semantic Web uses ontologies to show the interconnectedness in a Web environment. Within the concept of semantic mapping, domain ontology is at the core of intelligent e-learning systems. In order to achieve an ontology for K-12 education, the authors propse a domainspecific ontology PoleONTO (Personalized Ontological Learning Environment) with the emphasis on its development and incorporation into an e-learning environment.

An Ontology Driven Model for E-Learning in K-12 Education

2011 ◽  
pp. 107-116 ◽  
Author(s):  
Petek Askar ◽  
Kagan Kalinyazgan ◽  
Arif Altun ◽  
S. Serkan Pekince
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Environments ◽  
Intelligent Agents ◽  
Ontology Development ◽  
Future Directions ◽  
Implementation Processes ◽  
E Learning ◽  
K 12 ◽  
Computational Intelligent

This chapter introduces the development of a K-12 education ontology for e-learning environments. It presents design and implementation processes, followed by several recommendations for future directions for ontology development. E-learning environments incorporate the notion of semantic web based ontologies into their future directions. Semantic web uses ontologies to show the interconnectedness in a web environment. Ontologies are being developed in order to decrease the annotated amount of markup and increase the reliability of using computational (intelligent) agents. Within the concept of semantic mapping, domain ontology is at the core of intelligent e-learning systems. In order to achieve an ontology for K-12 education, the authors propse a domain-specific ontology PoleONTO (Personilized Ontological Learning Environment) with the emphasis on its development and incorporation into an e-learning environment.

Supporting Evolution of Knowledge Artifacts in Web Based Learning Environments

2009 ◽  
pp. 142-167
Author(s):  
Dimitris Kotzinos ◽  
Giorgos Flouris ◽  
Yannis Tzitzikas
Keyword(s):  
Semantic Web ◽  
Learning Environments ◽  
Knowledge Creation ◽  
Common Knowledge ◽  
Learning Theories ◽  
Web Based ◽  
Knowledge Repository ◽  
Web Based Learning ◽  
E Learning ◽  
Collaborative Knowledge

The development of collaborative e-learning environments that support the evolution of semantically described knowledge artifacts is a challenging task. In this chapter we elaborate on usage scenarios and requirements for environments grounded on learning theories that stress on collaborative knowledge creation activities. Subsequently, we present a comprehensive suite of services, comprising an emerging framework, called Semantic Web Knowledge Middleware (SWKM), that enables the collaborative evolution of both domain abstractions and conceptualizations, and data classified using them. The suite includes advanced services for ontology change, comparison and versioning over a common knowledge repository offering persistent storage and validation.

Multimodal User Interface in E-Learning Environments

2014 ◽  
pp. 33-49
Author(s):  
André Constantino da Silva ◽  
Heloísa Vieira da Rocha
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Multimodal Interaction ◽  
Web Based ◽  
Architecture Model ◽  
Component Based Development ◽  
Multimodal Systems ◽  
Multimodal Features ◽  
E Learning ◽  
Main Components

Multimodal interaction is a proposal to turn the interaction between humans and machines more natural, increasing the usability, flexibility, and convenience of one application. Improve an application with multimodal features impacts on its architecture and, to describe the main components to treat the multimodality, some architecture models are proposed in the literature, including for Web multimodal systems. E-Learning environments are Web-based systems and need a good usability, flexibility, and convenience: requirements that can be improved with implementation of multimodal features on them. Since they have their own peculiarities, we need a more specific multimodal architecture model described in such a way to reuse the components built for multimodal systems and to connect them with the e-learning environment components. This chapter proposes an architecture for multimodal e-learning environments. A viability study was done in the Ae, an e-learning environment developed using a component-based development process, with components to treat the pen and touch modalities.

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 Factors Affecting Students’ Preferences for Online and Blended Learning: Motivational Vs. Cognitive

2020 ◽  
Vol 22 (2) ◽  
pp. 72-86 ◽  
Author(s):  
Sinan Keskin ◽  
Halil Yurdugül
Keyword(s):  
Learning Environment ◽  
Blended Learning ◽  
Learning Environments ◽  
Self Efficacy ◽  
Teaching And Learning ◽  
Learning Motivation ◽  
Task Value ◽  
Individual Factors ◽  
E Learning ◽  
The Individual

AbstractToday’s educational institutions are expected to create learning opportunities independent of time and place, to offer easily accessible learning environments and interpersonal communication opportunities. Accordingly, higher education institutions develop strategies to meet these expectations through teaching strategies, such as e-learning, blended learning, mobile learning, etc., by using teaching technologies. These new technology-based teaching strategies are mainly shaped by decision-makers in education. This study seeks to analyse the individual factors that affect learners’ mode of teaching and learning delivery preferences. In this study, blended and online learning is considered as preferences of learners’ mode of teaching and learning delivery. The individual factors discussed in this research are cognitive learning strategies, e-learning readiness, and motivation. The data were obtained from the pre-service teachers at the end of the academic semester when they experienced online and blended learning. Data were analysed using optimal scaling analysis. The analysis method provides a two-dimensional centroid graph which shows the correlations between the variable categories. According to study findings, there is a correlation between the preferences of the learning environment, and the constructs of self-efficacy, e-learning motivation, and task value. It can be said that the motivational variables are more effective in the learning environment preference. The students with high task value, e-learning motivation, and self-efficacy preferred studying in blended learning environments. Cognitive strategies, self-directed learning, learner control, and test anxiety factors are independent of the learners’ learning delivery preferences.

Personal/Cloud Learning Environment, Semantic Web 3.0, and Ontologies

2015 ◽  
pp. 119-146
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Environments ◽  
Environmental Sustainability ◽  
Teaching And Learning ◽  
Personal Learning ◽  
Web 3.0 ◽  
Digital World ◽  
New Vision ◽  
Multiple Challenges

In the era of digital world that we live in, a new vision for learning is required. Learning is essentially personal, sociocultural, distributed, ubiquitous, flexible, dynamic, and complex in nature. There are multiple challenges, opportunities, and movements in learning that must be considered in the development and implementation of online learning environments. From the emerging computational capacity as a virtualized resource pool available over the network, several benefits can be obtained with regard to the management of computing infrastructures, such as environmental sustainability and improved Personal/Cloud Learning Environment use. In fact, Personal learning environments, Cloud computing, Semantic Web 3.0 and Ontologies are relatively new terms that hold considerable promise for future development and research in higher education contexts. Motivated by the aforementioned perspectives, the purpose of this chapter is to explore and discuss how these terms can be understood towards a more personalized, sociocultural, open, dynamic and encouraging model to support/facilitate teaching and learning processes, fulfilling the integrated view of the educational context presented in Part I of this book.

The Emerging Use of E-Learning Environments in K-12 Education

2011 ◽  
pp. 586-603
Author(s):  
Christopher O’Mahony
Keyword(s):  
Secondary Education ◽  
Learning Environments ◽  
Learning Strategy ◽  
Tertiary Education ◽  
Early Years ◽  
Virtual Learning Environments ◽  
Recent Emergence ◽  
The World ◽  
E Learning ◽  
K 12

Virtual learning environments (VLEs) and managed learning environments (MLEs) are emerging as popular and useful tools in a variety of educational contexts. Since the late 1990s a number of ‘off-the-shelf’ solutions have been produced. These have generally been targeted at the tertiary education sector. In the early years of the new millennium, we have seen increased interest in VLEs/MLEs in the primary and secondary education sectors. In this chapter, a brief overview of e-learning in the secondary and tertiary education sectors over the period from 1994 to 2004 is provided, leading to the more recent emergence of VLEs and MLEs. Three models of e-learning are explored. Examples of solutions from around the world are considered in light of these definitions. Through the case of one school’s journey towards an e-learning strategy, we look at the decisions and dilemmas facing schools and school authorities in developing their own VLE/MLE solutions.

Gender Differences in E-Learning

2012 ◽  
pp. 175-191
Author(s):  
Richard D. Johnson
Keyword(s):  
Gender Differences ◽  
Learning Environments ◽  
Social Presence ◽  
Peer Communication ◽  
Web Based ◽  
Communication Processes ◽  
E Learning ◽  
Communication Differences ◽  
Using Data ◽  
Course Outcomes

Although previous research has suggested that women may be at a learning disadvantage in e-learning environments, this study examines communication differences between women and men, arguing that women’s communication patterns may provide them with a learning advantage. Using data from 303 males and 252 females, this paper discusses gender differences in course communication processes and course outcomes in a web-based introductory information systems course. Results indicate that women communicated more, perceived the environment to have greater social presence, were more satisfied with the course, found the course to be of greater value, and had marginally better performance than men. Despite the challenges facing women in e-learning environments, the results of this study suggest that e-learning environments that allow for peer to peer communication and connectedness can help females overcome some of these disadvantages. Implications for research and practice are also discussed.

Sign in / Sign up

Export Citation Format

Share Document