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.

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.

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.

Affective Agents in E-Learning Platforms

2012 ◽  
pp. 1225-1233
Author(s):  
Christos N. Moridis ◽  
Anastasios A. Economides
Keyword(s):  
Artificial Intelligence ◽  
Learning Environments ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Human Learning ◽  
Human Reasoning ◽  
Conversational Agents ◽  
Emotional Aspects ◽  
E Learning ◽  
Learning Platforms

During recent decades there has been an extensive progress towards several Artificial Intelligence (AI) concepts, such as that of intelligent agent. Meanwhile, it has been established that emotions play a crucial role concerning human reasoning and learning. Thus, developing an intelligent agent able to recognize and express emotions has been considered an enormous challenge for AI researchers. Embedding a computational model of emotions in intelligent agents can be beneficial in a variety of domains, including e-learning applications. However, until recently emotional aspects of human learning were not taken into account when designing e-learning platforms. Various issues arise when considering the development of affective agents in e-learning environments, such as issues relating to agents’ appearance, as well as ways for those agents to recognize learners’ emotions and express emotional support. Embodied conversational agents (ECAs) with empathetic behaviour have been suggested to be one effective way for those agents to provide emotional feedback to learners’ emotions. There has been some valuable research towards this direction, but a lot of work still needs to be done to advance scientific knowledge.

Bringing AI to E-Learning

2012 ◽  
pp. 126-138
Author(s):  
K. Giotopoulos ◽  
C. Alexakos ◽  
G. Beligiannis ◽  
A. Stefani
Keyword(s):  
Learning Environment ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
Student Model ◽  
Basic Part ◽  
Learning Platform ◽  
Front End ◽  
E Learning ◽  
Autonomous Component

This paper presents a newly developed student model agent, which is the basic part of an e-learning environment that incorporates Intelligent Agents and Computational Intelligence Techniques. The e-learning environment consists of three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. The basic aim of this contribution is to describe in detail the agent’s architecture and the innovative features it provides to the e-learning environment through its utilization as an autonomous component. Several basic processes and techniques are facilitated through the agent in order to provide intelligence to the e-learning environment.

Securing the Infrastructure for Service-Based E-Learning Environments

2011 ◽  
pp. 1-24
Author(s):  
Simon Schwingel ◽  
Gottfried Vossen ◽  
Peter Westerkamp
Keyword(s):  
Web Services ◽  
Learning Environment ◽  
Learning Environments ◽  
User Authentication ◽  
Learning Objects ◽  
Learning System ◽  
Learning Material ◽  
Single Sign On ◽  
User Data ◽  
E Learning

E-learning environments and their system functionalities resemble one another to a large extent. Recent standardization efforts in e-learning concentrate on the reuse of learning material only, but not on the reuse of application or system functionalities. The LearnServe system, under development at the University of Muenster, builds on the assumption that a typical learning system is a collection of activities or processes that interact with learners and suitably chosen content, the latter in the form of learning objects. This enables us to divide the main functionality of an e-learning system into a number of stand-alone applications or services. The realization of these applications based on the emerging technical paradigm of Web services then renders a wide reuse of functionality possible, thereby giving learners a higher flexibility of choosing content and functionalities to be included in their learning environment. In such a scenario, it must be possible to maintain user identity and data across service and server boundaries. This chapter presents an architecture for implementing user authentication and the manipulation of user data across several Web services. In particular, it demonstrates how to exploit the SPML and SAML standards so that cross-domain single sign-on can be offered to the users of a service-based learning environment. The chapter also discusses how this is being integrated into LearnServe.

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