A Personalized e-Learning Framework

With the advent of web based learning and content management tools, e-learning has become a matured learning paradigm, and changed the trend of instructional design from instructor centric learning paradigm to learner centric approach, and evolved from “one instructional design for many learners” to “one design for one learner” or “many designs for one learner”. Currently, there are mature technologies that can lead to the construction of a personalized e-learning environment, namely: Ontology, Semantic web, learning objects, and content management systems. In this paper, a personalized e-learning framework is proposed, where learning objects are classified according to their suitability for the different types and styles of learning, and where these learning objects are offered to individual learners according to their personal preferences, skills and needs

Shortcomings of learning design approaches and a possible way out

Shifting away from traditional instructional design to younger research streams like personalized, workflow-based or collaborative e-learning, learning design (LD) has become an important issue in the field of technology-enhanced learning. Nevertheless, current LD approaches turn out to be rather unhandy or costly in teaching and research practice. In this paper, we discuss these shortcomings and propose an alternative solution approach which is based on a web application mashup, learner interactions, and a semantic layer for tool recommendations. As the evaluation of our first prototype is in progress, we can not highlight first experiences, but outline benefits and possible application scenarios in this position paper

Flexible virtual learning environments: a schema-driven approach using sematic web concepts

Flexible e-Iearning refers to an intelligent educational mechanism that focuses on simulating and improving traditional education as far as possible on the Web by integrating various electronic approaches, technologies, and equipment. This mechanism aims to promote the personalized development and management of e-learning Web services and applications. The main value of this method is that it provides high-powered individualization in pedagogy for students and staff.Here, the thesis mainly studied three problems in meeting the practical requirements of users in education. The first question is how a range of teaching styles (e.g. command and guided discovery) can be supported. The second one is how varieties of instructional processes can be authored. The third question is how these processes can be controlled by learners and educators in terms of their personalized needs during the execution of instruction.In this research, through investigating the existing e-Iearning approaches and technologies, the main technical problems of current virtual learning environments (VLEs) were analyzed. Next, by using the Semantic Web concepts as well as relevant standards, a schema-driven approach was created. This method can support users' individualized operations in the Web-based education. Then, a flexible e-learning system based on the approach was designed and implemented to map a range of extensive didactic paradigms. Finally, a case study was completed to evaluate the research results. The main findings of the assessment were that the flexible VLE implemented a range of teaching styles and the personalized creation and control of educational processes

An Ontology-based Approach to Student Skills in Multiagent e-Learning Systems

The main idea of our approach is that the domain ontology is not only the instrument of learning but an object of examining student skills. We propose for students to build the domain ontology of examine discipline and then compare it with etalon one. Analysis of student mistakes allows to propose them personalized recommendations and to improve the course materials in general. For knowledge interoperability we apply Semantic Web technologies. Application of agent-based technologies in e-learning provides the personification of students and tutors and saved all users from the routine operations

The Model for Learning Objects Design Based on Semantic Technologies

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

An Ontology-Based Approach in Personalization of the e-Learning System

The emergence of the Semantic Web and its technologies have opened the way, over the last decade, for the development of ontologies and systems that use ontologies in various fields, including e-learning. This article presents elements that underpin the development of an e-learning system in the area of the Human Resource Management in the field of ontology health, respectively basic notions about the semantic Web, ontologies, personalization in e-learning. The article presents a personalized e-learning environment that uses new technologies, semantic Web and ontologies to improve the human resource management training process, being addressed to hospital managers. The necessity of this approach is given by the training requirements in the field of human resources management for the specialists from the medical system in Romania, as well as by the need to solve current limitations of the e-learning systems. The implementation of the concept of personalization of learning in the e-learning system is performed starting from the student model built to determine the level of knowledge and the objectives of training. Modeling the student profile using ontologies has demonstrated the possibility of re-using the models, the detailed and complete representation of the student’s knowledge as well as the reasoning process. This learning program aims to increase the performance, skills and competence of health managers, by deepening knowledge in the field of public health, healthcare management, and human resource management

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness

We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relatedness preferences. Our method is corpus independent and can essentially rely on any sufficiently large (unstructured) collection of coherent texts. Moreover, the approach facilitates the fitting of semantic models for specific users or groups of users. We present the results of extensive range of experiments from small to large scale, indicating that the proposed method is effective and competitive with the state-of-the-art.Comment: 37 pages, 8 figures A short version of this paper was already published at ECML/PKDD 201