Going Beyond Computer-Assisted Vocabulary Learning: Research Synthesis and Frameworks

This paper introduces three computer-assisted applications designed for learning foreign vocabulary in an informal setting. The first one, images recommendation application, generates appropriate image recommendations for representing a word. It tackles the challenge for a foreign language learner to determine appropriate images from a standard web search engine such as Google, Yahoo, Flicker, etc. The second application, learning context representation application, generates learning contexts automatically from lifelogging images. It addresses problems associated with describing a learning context in the forms of hand-written descriptions, keeping notes, or taking memos. The third application we discuss here, namely location-based associated word recommendation application, generates recommendations of associated words in a particular learning location by analyzing word learning histories. It seeks to answer a critical question: what I should learn next? This is a critical challenge for the users of ubiquitous learning tools. In order to recommend potential vocabularies which a learner could be learning in a particular location, this study recommends associated words and topic-specific vocabularies. These applications are for AIVAS (Appropriate Image-based Vocabulary Learning System), a platform for computer-assisted vocabulary learning. We report here several evaluations, including human assessment and data-driven assessments, that have been carried out to reveal the importance of these systems.