The effect of the inductive and deductive data-driven learning (DDL) on vocabulary acquisition and retention

Abstract

Abstract Research on data-driven learning (DDL) has generally suggested that DDL can facilitate second/foreign language learning due to its inductive nature and authentic language samples that provide learners with opportunity of deep and discovery learning. One major obstacle that prevents learners from such benefits lies in the cognitive load that such learning might induce. Limitations of DDL such as this have resulted in a call for more studies that examine the differential effect of inductive and more traditionally, deductive approach in DDL-based ESL/EFL instruction. The present study compared the effect of the deductive and inductive approaches in a DDL context on vocabulary acquisition and retention. A total of 27 EFL learners were randomly divided into deductive and inductive groups making use of the Corpus of Contemporary American English (COCA) to learn eight target words. A modified version of the Vocabulary Knowledge Scale (VKS; Paribakht & Wesche, 1997) was used to assess the students learning of word form, meaning, and use before, immediately after, and 2 weeks after the treatment instruction. Furthermore, the two groups performances on the pre-test and immediate post-test were analyzed to scrutinize their change in vocabulary knowledge. The results showed that both approaches were equally effective in terms of facilitating the learners vocabulary acquisition and retention. Furthermore, the inductive and deductive groups showed similar patterns of the acquisition of target words: their knowledge of words generally moved to a higher level, and seldom stayed at the same level or moved to a lower level. The result that deductive DDL was just as effective as the inductive approach but less time-consuming might suggest that the deductive approach could complement DDL more efficiently when the DDL s inductive nature prevents learners from fully benefiting from its potential advantages.