A Framework For Intelligent Voice-Enabled E-Education Systems

ABSTRACT

Voice-enabled e-Education system is the enabling technology that allows users to access Learning resources on a web using a telephone. Telephony applications in the area of education are developed mainly for the visually impaired to address the limitations of user interface of a mobile learner in accessing web learning resources. However, with the proliferation of learning resources on the web, finding suitable content (using telephone) has become a rigorous task for voice-based online learners to achieve better performance. The problem of Finding Content Suitability (FCS) with voice e-Learning applications is more complex when the sight impaired learner is involved. Existing voice-enabled applications in the domain of e-Education lack the attributes of adaptive and reusable learning objects to be able to address the FCS problem. Nevertheless, the adoption of artificial intelligence methodologies involving case-based reasoning (CBR) and domain ontology is required to provide recommender and adapted systems in voice-enabled learning towards solving the FCS problem. This study provides a framework for intelligent Voice-based e-Education Systems (iVeES) and an implementation of a system based on the framework with dual user interfaces – voice and web. The framework was designed using schematic diagram, mathematical models and ontology; and implemented using PHP, VoiceXML and MySQL. A usability study was carried out in a visually impaired and a non visually impaired school using the International Standard Organization 9241-11 (1998) and Nielsen (2003) specification to determine the level of effectiveness, efficiency, user satisfaction, learnability and memorability. The result of the usability evaluation reveals that the prototype application developed for the school has “Good Usability” rating of 4.12 out of 5 scale. This shows that the application will not only complement existing mobile and web-based learning systems but will be of immense benefit to users, based on the system’s capacity for taking autonomous decisions that are capable of adapting to the needs of both visually impaired and non-visually impaired learners.