Improving Persian-English vocabulary learning through a learning strategy instruction embedded in a purpose-built web application

Author: Siamak Mirzaei

Mirzaei, Siamak, 2022 Improving Persian-English vocabulary learning through a learning strategy instruction embedded in a purpose-built web application, Flinders University, College of Science and Engineering

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Learning new vocabulary is one of the challenges in language learning yet crucial for mastering another language. It is also essential in any discipline or professional field where mastering new terminology is indispensable. VLASTWA is a Vocabulary Learning and Strategy Teaching Web Application designed to help Persian native speakers improve their English vocabulary knowledge. In this study, I examined the effects of VLASTWA’s use on improving Persian learners’ vocabulary. The conducted research presents an evaluation of efficacy and usability of this custom-built (for this project), targeted and learnable web application for teaching an extensively researched vocabulary learning strategy, the keyword method, and for facilitating learning of new vocabulary with the aid of this method. In this longitudinal study (n = 240, age 18+), effectiveness of the use of the keyword method taught within the designed web application (app) or traditional pen and paper (P&P) was compared between four experimental (two P&P and two app groups – with differences in receiving the encoding or encoding and recall keyword method training) and two control groups (one app and one P&P). In the experimental groups, participants learned to use the keyword method, applied it in learning 22 new (English) words, and tested the recall of this newly learned vocabulary within the app and P&P methods on four different occasions (times) while in the control groups – the only difference was not being given any keyword method instruction.

Keywords: keyword method, explicit strategy instruction, human–computer interaction, computer assisted language learning, usability testing

Subject: Computer Science thesis

Thesis type: Doctor of Philosophy
Completed: 2022
School: College of Science and Engineering
Supervisor: Dr Trent Lewis