Data-Driven Learning Tasks and Involvement Load Hypothesis

Zaha Alanazi


Despite the increasing research on the benefits of using corpora in language teaching and learning, Data-Driven Learning (henceforth, DDL) research has been criticized for its lack of contribution to second language theories. This paper intends to address this gap by examining the assumptions of Involvement Load Hypothesis (ILH) using two DDL tasks with different cognitive loads. Learners were assigned to one of two conditions: reading only or translation. Based on ILH, translation is more effective than reading in learning vocabulary, as it induces more cognitive involvement (Laufer & Hulstjin, 2001). The two groups received a pretest to ensure their unfamiliarity with six target words. Each group underwent one instructional session under one of the two conditions. After the session, students took three immediate post tests on the six target items: active recall of form, passive recall of meaning, and production. Contrary to the expectations of ILH, the results of the immediate post tests showed no statistically significant difference in the mean of vocabulary knowledge between the two groups. In addition, in the delayed test, the reading-only group showed statistically higher scores in the active recall of form than their translation peers. The findings highlight some important theoretical and pedagogical implications for using DDL tasks, particularly for EFL vocabulary learning.

Full Text:



World Journal of English Language
ISSN 1925-0703(Print)  ISSN 1925-0711(Online)

Copyright © Sciedu Press

To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders. If you have any questions, please contact: