A Proposal for a Quantitative Evaluation Model for Error Image Generation in L2 Vocabulary Learning

Abstract

Vocabulary learning that incorporates visual information has become widely recognized as an alternative to context-based methods. However, few studies focus on learners' incorrect answers. On the Other hand, fossilization caused by repeated errors has been a concern. Our proposed system, L-VEIGe, effectively prevents repeated errors by visualizing learners' incorrect answers through image generation, which encourages introspection. However, there exists a 'Feature Disappearance' problem, where the generated images for incorrect answers lack sufficient information for comprehension. This study proposes a method for quantitatively evaluating these error images from a cognitive perspective.