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1.	Kastrati, Zenun, 1984-, et al. (author) Building Domain Ontologies for Hyperlinked Multimedia Pedagogical Platforms 2014 In: HCI International 2014 - Posters’ Extended Abstracts. - Cham : Springer. - 9783319078540 - 9783319078533 ; , s. 95-100 Conference paper (peer-reviewed)abstract This paper examines building of the course ontology for describing and organizing hyperlinked pedagogical content. The ontology is used to structure and classify multimedia learning objects (MLO) in hyperlinked pedagogical platform called HIP, and to assist students to search for lectures and other teaching materials in a reasonable time and more efficiently. In addition, this paper proposes a new approach to improve the classification performance by enhancing the information representation model using concepts from the pedagogical course domain ontology. The model will automatically estimate weight of concepts within the ontology, and it will combine the weight with concepts’ importance which is calculated using Term Frequency Inverse Document Frequency – tf*idf algorithm. This paper is a work in progress. We are in process of creating and implementing the course ontology and an experiment will be conducted to evaluate the classification performance in terms of efficiency and effectiveness for the approach proposed in this paper.

Kastrati, Zenun, 1984-, et al. (author)
Building Domain Ontologies for Hyperlinked Multimedia Pedagogical Platforms
2014
In: HCI International 2014 - Posters’ Extended Abstracts. - Cham : Springer. - 9783319078540 - 9783319078533 ; , s. 95-100
Conference paper (peer-reviewed)abstract
- This paper examines building of the course ontology for describing and organizing hyperlinked pedagogical content. The ontology is used to structure and classify multimedia learning objects (MLO) in hyperlinked pedagogical platform called HIP, and to assist students to search for lectures and other teaching materials in a reasonable time and more efficiently. In addition, this paper proposes a new approach to improve the classification performance by enhancing the information representation model using concepts from the pedagogical course domain ontology. The model will automatically estimate weight of concepts within the ontology, and it will combine the weight with concepts’ importance which is calculated using Term Frequency Inverse Document Frequency – tf*idf algorithm. This paper is a work in progress. We are in process of creating and implementing the course ontology and an experiment will be conducted to evaluate the classification performance in terms of efficiency and effectiveness for the approach proposed in this paper.

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