Vocabulary Learning through Data-driven Learning in an English as a Second Language Setting

Author/s: Adem Soruç, Bilal Tekin

DOI: 10.12738/estp.2017.6.0305  OnlineFirst published on September 10, 2017

Abstract

The past twenty years have witnessed rapid advances in the field of corpus studies, with some studies, for instance, investigating the effectiveness of data-driven learning (DDL), through which students can discover and learn. However, empirical evidence on this approach to learning is still limited. Therefore, the present study, first, investigates whether DDL is effective in vocabulary learning compared to traditional instruction (TI), and, second, explores students’ attitudes toward vocabulary learning when taught with either a DDL or a TI approach. The study was carried out at a secondary school in Kampala, Uganda, and compared students in a DDL group (N = 36) to those in a TI (control) group (N = 36). The pre-/posttest analyses revealed that, although both groups performed in the posttests better than the pretest, the DDL group achieved significantly higher scores than the TI group on both the immediate and the delayed posttest. Moreover, semi-structured interviews with six students corroborated the quantitative results, revealing that the students favored DDL, based on such expressions as “freedom,” “comfortable,” “relaxing,” “technological,” “feeling myself responsible,” and so on. The paper gives some implications for the same teaching/learning situation and makes suggestions further research.

Keywords
Corpus, Concordance, Data-driven learning, Vocabulary, Autonomy

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