Adaptable and adaptive human-computer interface to recommend learning objects from repositories

Abstract

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.