A Data-driven Learning Focus on Form Approach to Academic English Lecture Comprehension

Abstract

Abstract Using concordancers in English classes is demanding, time-consuming, and challenging to both teachers and learners. On the other hand, research points to the effectiveness of data-driven learning (DDL) in improving second/foreign language learning. The present study proposed a new task type, that is, DDL focus on form (FonF) tasks, as a combination of DDL and FonF approaches, and investigated if using such tasks improves students’ comprehension of English academic lectures. Drawing on a quasi-experimental validating quantitative data triangulation model, 124 intermediate English learners were pretested, randomly assigned to treatment and control groups, exposed to the treatment and control treatment, and post-tested on immediate and delayed academic English lecture comprehension tests. The treatment required working with 12 DDL FonF tasks, specifically proposed for the present study, during 12 45-min sessions. On the other hand, the control treatment involved working with 12 FonF tasks during 12 45-min sessions. Due to the coronavirus pandemic, the study was done remotely and the tasks were sent through WhatsApp in Word and PDF formats. As part of the validating quantitative data triangulation model, semi-structured interviews were held with the learners in the experimental group to shed light on the treatment. The results of the study suggested that using DDL FonF tasks results in both temporary and durable improvement in the learners’ comprehension of English academic lectures. The study concludes that integrating DDL and FonF approaches to second/foreign language learning in the form of tasks improves academic English lecture comprehension through increase in the learner’s noticing, metalinguistic awareness, discovery learning, and agency.