Recommendation Method in the Context of Real-world Language Learning

Abstract

This paper explores a recommendation method in the context of real-world language learning based on ubiquitous learning logs. Ubiquitous learning log stands for a digital record of what they have learned in the daily life using ubiquitous technologies. One of the issues of ubiquitous learning analytics is how we should detect or mine effective and efficient learning patterns from many learning data accumulated in a ubiquitous learning system. To tackle this issues, this paper proposes a visualization and analysis system called VASCORLL (Visualization and Analysis system for COnnecting Relationships of Learning Logs) in order to link learners in the real world and learning logs that are accumulated in a cyber space by a ubiquitous learning system called SCROLL (System for Capturing and Reminding of Learning Log). Using VASCORLL, learners can predict their next learning steps and then find learning patterns related to their current learning situation. The initial evaluation was conducted to measure whether VASCORLL can increase learners’ learning opportunities and whether the recommended learning patterns are appropriate for learners or not. In this evaluation, we found important criteria for recommending appropriate learning patterns for learners in the real-world language learning. In addition, VASCORLL succeeded in increasing learners’ learning opportunities.