A New Technology Design for Personalized Incidental Vocabulary Learning using Lifelog Image Analysis

Abstract

Incidental vocabulary learning is the process of learning foreign vocabulary without the intention of doing so. In language learning pedagogy, incidental learning is considered to be an effective way to enhance foreign vocabulary from context. Conventional vocabulary learning systems in ubiquitous learning scenarios are developed primarily for supporting intentional vocabulary learning. When learning foreign vocabulary using conventional tools, it is not feasible to learn incidental vocabulary. Moreover, there does not exist a general framework to formulate lifelong images along with typical ubiquitous learning logs such as location, time, and demographics. Therefore, this research precisely looked into the scope of formulating lifelog images as sensor data for enhancing incidental vocabulary knowledge of English as Foreign Language (EFL) learners. The main pursuit of this article is to introduce a model for enriching ubiquitous learning literature, in which incidental vocabulary can be learned. In this study, a technology-enhance environment for incidental vocabulary learning in the EFL context is presented. The research objectives are to- i) design a new technology that is capable of analyzing lifelong images in order to generate a bag of incidental vocabularies, ii) determining the top-5 vocabularies that could be recommended to the learner, and iii) to automatically create learning material for each of the recommended words. We employed visual content analytics on lifelong learning images using object detection method. This is known to be an applied artificial intelligence method that is applicable across a variety of fields, such as natural language processing, computer vision, and others. We aimed to apply this applied AI method to design a new technology-enhanced learning environment.