Adaptable and Adaptive Human-Computer Interface to Recommend Learning Objects from Repositories
In the last decades, some useful contributions have occurred to human-computer interfaces and e-learning system developments such as adaptation, personalization, ontological modeling, as well as, learning object repositories. The aim of this paper is to present the advantages of integrating ontologies as knowledge representation scheme in order to support adaptable and adaptive functionalities that can be offered by a human-computer interface when recommending LOs from Repositories. A human-computer interface model is proposed which is composed of several modules that allow deploying adaptable and adaptive functionalities such as the following: (1) store and retrieving of LOs from repositories, (2) representation of events by learners within the GUI, (3) performing of inferences through ontological reasoned, (4) adaptation of the GUI for each of the users’ profiles and (5) monitoring of all changes made by the user on the GUI and storing of them in the system database for further processing. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of the proposed human-computer interface model which combines adaptability along with adaptive characteristics.
KeywordsHuman-computer interfaces Personalization Adaptable and adaptive systems Ontologies Learning objects (LO) LO repositories
This research was developed with the aid of the master grants offered to Oscar M. Salazar and Thomas Quiroz by COLCIENCIAS through “Convocatoria 645 de 2014. Capítulo 1 Semilleros-Jóvenes Investigadores”. This research was also partially funded by the COLCIENCIAS project entitled: “RAIM: Implementación de un framework apoyado en tecnologías móviles y de realidad aumentada para entornos educativos ubicuos, adaptativos, accesibles e interactivos para todos” from the Universidad Nacional de Colombia, with code 1119-569-34172.
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