Enriching the Korean Learner Corpus with Multi-reference Annotations and Rubric-Based Scoring

Written and spoken data produced by second language (L2) learne rs have long served as important resources for studying L2 acquisition. With the develo pment of electronic collections of learner data--known as learner corpora--rese archers now benefit from more systematic and scalable analysis methods. These corpor a, often large and representative, offer a stronger empirical foundation than smalle r, manually compiled datasets. They improve the reliability of research by enabling large-scale analysis of learner errors, interlanguage patterns, and developmental sta ges of acquisition ( Zhang & Fu, 2024). In addition, advanced computational tools make it possible to per - form quick and detailed analyses, facilitating broader investigations into L2 learning ( Le Bruyn & Paquot, 2021).