An adjustable personalization of search and delivery of learning objects to learners

Biletskiy, Yevgen; Baghi, Hamidreza; Keleberda, Igor; Fleming, Michael W.

doi:10.1016/j.eswa.2008.12.038

Cited by 46 publications

(41 citation statements)

References 9 publications

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“…It provides a common understanding of the domain [13] and facilitate knowledge sharing and reuse while also very expressive using a web ontology languages. For example, Biletskiy et al [14] has used ontology as a user modelling technique. The IMS Learner Information is presented as many different categories which contribute to the ontology model, including their academic performance, interests and aspirations.…”

Section: Related Workmentioning

confidence: 99%

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

Wang

Chen

Solheim

2017

Proceedings of the 2017 1st International Conference on E-Education, E-Business and E-Technology

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-Increased user motivation from interaction process leads to improved interaction, resulting in increased motivation again, which forms a positive self-propagating cycle. Therefore, a system will be more effective if the user is more motivated. Especially for students with dyslexia, it is common for them to experience more learning difficulties that affect their learning motivation. That's why we need to employ techniques to enhance user motivation in the interaction process. In this research, we will present a system architecture for motivation-enhanced learning and the detailed process of the construction of our motivation model using ontological approach for students with dyslexia. The proposed framework of the personalised learning system incorporates our motivation model and corresponding personalisation mechanism aiming to improve learning motivation and performance of students with dyslexia. Additionally, we also provide examples of inference rules and a use scenario for illustration of personalisation to be employed in our system.

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Section: Related Workmentioning

confidence: 99%

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

Wang

Chen

Solheim

2017

Proceedings of the 2017 1st International Conference on E-Education, E-Business and E-Technology

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“…Metadata standards generally used to describe information resources, in order to facilitate their categorization, storage, search and retrieval [44], [45]. Consequently, in reference to the Dublin Core Metadata Element Set (DCMI, http://dublincore.org/), each LO is defined via the attributes: Title, Author, Depositor, Publisher, Date, Type, Topics, Description, Language, and Keywords.…”

Section: Repository Servicesmentioning

confidence: 99%

A Framework of Semantic Recommender System for e-Learning

Fraihat¹,

Shambour²

2015

JSW

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Abstract:With the rapid increasing of learning objects (LOs) in a variety of media formats, it becomes quite difficult and complicated task for learners to find suitable LOs based on their needs and preferences. To support personalization, recommender systems can be used to assist learners in finding the appropriate LOs which will be needed for their learning. In this paper, we propose a framework of a semantic recommender system for e-learning in which it will assist learners to find and select the relevant LOs to their field of interest. The proposed framework utilizes the intra and extra semantic relationships between LOs and the learner's needs to provide personalized recommendations for learners. The semantic recommendation algorithm is based on the extension of the query keywords by using the semantic relations, concepts and reasoning means in the domain ontology. The proposed system can be used to reduce the time and effort involved in finding suitable LOs, and thus, improves the quality of learning.Key words: E-learning, learning object, personalization, recommender system, semantic web, semantic indexing system, ontology modeling, semantic query processing. IntroductionWith the rapid growth of Web-based learning applications, e-learning is becoming more and more popular than the traditional educational approaches. Learning management systems (LMSs) are typically employed in large-scale educational institutions to facilitate the delivery and organization of e-learning [1]. LMS can be defined as "the infrastructure that delivers and manages instructional content, identifies and assesses individual and organizational learning or training goals, tracks the progress towards meeting those goals, and collects and presents data for supervising the learning process of an organization as a whole" [2].In general, courses in LMSs consist of LOs. LO can be defined as a digital and reusable piece of content used to achieve a learning objective. LO can be a text document, an audio file, a video, a picture, or a complete website [3]. Commonly, LMSs are considered as one-size-fits-all systems as they deliver the same kind of course structure and LOs to each learner [1], [4]. However, each learner has different characteristics such as levels of expertise, learning styles, prior knowledge, cognitive abilities and interests, and therefore, a one-size-fits-all systems do not support most learners.Personalization is a promising way to deal with this problem by supporting each learner independently based on his/her characteristics. Personalization in LMSs occurs when such systems uniquely address a learner's needs and characteristics. This will help in improving the learner's satisfaction and in overall the quality of learning. To support personalization, recommender systems can be employed to overcome current limitations of LMSs in providing personalization through recommending suitable LOs to learners based on their individual needs and Salam Fraihat * , Qusai Shambour Software Engineering Department, Faculty of Information...

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“…At the present time, there are many approaches and techniques developed within e-Learning initiatives that facilitate the search and delivery of appropriate learning objects to learners, for example, concept-based search, context-sensitive delivery and personalization, ontology-based course assembly and learning content development, adaptive learning and adaptive media, etc. (Aroyo et al, 2006;Baghi, Biletskiy, & Li, 2008;Biletskiy, Vorochek, & Medovoy, 2004;Biletskiy, Baghi, Keleberda, & Fleming, 2008;Biletskiy, Brown, & Ranganathan, 2009;Boley et al, 2005;Devedzic, 2004;Friezen, 2005;Jovanovic, Gašević, Knight, & Richards, 2007;Keleberda, Repka, & Biletskiy, 2006;Pahl & Hollogan, 2009;Wang, Tsai, Lee, & Chiu, 2007;Zouaq, Nkambou, & Frasson, 2007). Usually, these techniques involve a vast number of learning objects accompanied by learning object metadata (LOM) distributed on the Web, in digital…”

Section: Introductionmentioning

confidence: 99%

Focused Crawling for Downloading Learning Objects

Biletskiy

Wojcenovic

Baghi

2009

IJELL

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The present paper describes architecture, design, and implementation details of a Web Crawler for learning objects. The Web Crawler developed provides a technical solution to mass downloading learning objects from digital libraries on the Web for further search and delivery of appropriate learning objects to learners using various e-Learning services (e.g. personalization). The task of mass downloading learning objects, which would be long if done manually, can be performed quickly using the present system. Throughout the development of the system, the main objective was to decrease the time and user interaction needed to obtain the desired information from the Web repositories containing learning objects. The present system implements features of finding, parsing, and downloading web pages with learning object metadata and content. The system has been developed with reusability in mind, so this system can be used as the basis for any other task related to downloading XML-based documents from the Web. The final result of the presented work is a functioning system that meets the objectives and requirements given.

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An adjustable personalization of search and delivery of learning objects to learners

Cited by 46 publications

References 9 publications

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

A Framework of Semantic Recommender System for e-Learning

Focused Crawling for Downloading Learning Objects

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