DATA-DRIVEN LEARNING AND THE ACQUISITION OF TECHNICAL VOCABULARY

Karimova Feruza Inoyatovna

Authors

Karimova Feruza Inoyatovna Senior Lecturer, Department of Languages and Humanities Andijan State Technical Institute

Keywords:

data-driven learning, corpus linguistics, concordancing, technical vocabulary, collocation, English for Specific Purposes, lexicogrammar.

Abstract

The article analyzes data-driven learning (DDL) as a corpus-based approach to the acquisition of technical vocabulary in English for Specific Purposes (ESP) instruction. Conventional vocabulary teaching, organized around bilingual word lists and isolated definitions, is contrasted with the inductive examination of concordance lines, in which the learner acts as a language investigator deriving patterns of use from authentic data. Meta-analytic and review evidence is examined showing that DDL yields large effects on vocabulary and lexicogrammar and that it teaches not only meaning but collocation, colligation, and typical usage. At the same time, the research–practice gap, the cognitive demands of raw concordance data, and the limited evidence on durability of learning are discussed. Criteria for the principled classroom use of DDL in technical vocabulary instruction are formulated.

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