L2 Learners’ Perspectives on Data-Driven Learning for Identifying Properties of Near-Synonymous Words: A Convergent Mixed-Methods Study

Abstract

This study examines second language (L2) learners’ perspectives regarding the affordances and challenges of using the Data-Driven Learning (DDL) to identify the properties of near-synonymous words. Employing a convergent mixed-method design, this study deciphers the perceptions of 40 undergraduate L2 learners majoring in English language teaching. After an initial identification of the learners’ vocabulary levels, the experienced benefits and barriers associated with carrying out experiential tasks were elicited via questionnaire data and open-ended survey questions. Descriptive statistics, including means and standard deviations, were revealed and thematic analyses of the responses to the survey questions were documented. The results indicate that completing tasks through the corpus was found to enhance their knowledge of collocations. Integrating corpus tasks into YouGlish (an online practice tool for authentic spoken English in context) was found to increase their awareness of the contextual properties of words. The identification of condensed language exposure, lexical inference, and elicitation of flexible and context-specific patterns were reported to be beneficial. Acknowledging these benefits, gaining familiarity with the corpus interface, encountering limited access to search queries, and analyzing large amounts of concordance lines posed challenges for learners. This research presents the implementation of the DDL supported by experiential learning, contextually rich input, and inductive reasoning tasks in vocabulary learning by further offering instructional implications in L2 contexts.