Error-Driven Learning: Development of Vocabulary Learning Support System that Utilizes Suggestibility of Error by Image Generation

Abstract

Vocabulary learning has long been taught as a basis for L2 English language learning. The use of visual clues is a widely recognized method in vocabulary learning. Several studies have applied automatic image caption generation, invented with computer vision and natural language processing, to English vocabulary learning in recent years. However, these vocabulary learning systems mainly use correct answers and their corresponding images. Another study focuses on the effectiveness of making errors, utilizing simulation to visualize the learner's error, and its suggestibility contributes to the learning effectiveness of the physics task. We focus on the effectiveness of error with image generation implying suggestibility and hypothesize that errors encourage the students to memorize the vocabulary effectively. We propose a vocabulary learning support system with image generation corresponding to learner's error for L2 English language learners. To accomplish the development, we defined three tasks and researched its possibility.