The Use of Corpus-Based Analysis to Enhance Students’ English Lexical Competence in Inclusive Education

Abstract

As inclusive education gains prominence globally, the need for effective instructional approaches that address linguistic diversity becomes increasingly urgent. In Central Asian contexts such as Kazakhstan, transitioning from segregated to inclusive education highlights challenges in meeting diverse learners' vocabulary needs, particularly in English language learning environments. Despite growing interest in corpus-based instruction, limited research exists on its application to enhance lexical competence in inclusive settings, especially among philology students. This study aims to investigate how corpus-based analysis, informed by Vygotskian sociocultural theory, can be used to bridge lexical gaps in inclusive education. It explores pedagogical adaptations necessary to support learners with diverse cognitive, linguistic, and physical abilities. Findings reveal that integrating data-driven learning (DDL) techniques improves lexical awareness, supports differentiated instruction, and fosters learner autonomy. Adaptations such as screen reader-compatible tools and simplified corpus tasks ensure accessibility for all students. Moreover, corpus-based methods outperform traditional vocabulary instruction in building productive lexical competence, as shown by alternative assessment tools. This study contributes an interdisciplinary framework combining corpus linguistics, inclusive pedagogy, and sociocultural theory. It demonstrates how corpus tools can be tailored to universal design principles, enhancing both linguistic proficiency and equity. The research underscores the importance of teacher training in corpus methods and recommends policy shifts that align educational accessibility with technological innovation. By aligning language instruction with inclusive education principles, corpus-based learning can transform vocabulary acquisition into a more equitable, student-centered process.