Online Machine Translation as A Call Tool: Enhancing EFL Writing Accuracy in Indonesia

Abstract

The integration of technology in language learning, particularly Computer-Assisted Language Learning (CALL), has made online machine translation (OMT) a common tool for EFL students. However, its impact on specific writing skills, such as grammatical accuracy, remains a subject of debate, with concerns about over-reliance and passive learning. This study aimed to empirically investigate the effect of using OMT as a CALL tool on the grammatical accuracy and vocabulary diversity in the writing of Indonesian EFL students. A quasi-experimental design was employed with 57 first-year EFL students, divided into an experimental group (n=30) that used OMT and a control group (n=27) that did not. Data were collected through pre-test and post-test writing assessments and analysed using paired and independent t-tests. A supplementary survey provided qualitative insights into the students' experiences. The experimental group demonstrated a statistically significant improvement in writing scores (mean pre-test=40.83, post-test=76.93; p < 0.001), far exceeding the minimal gain in the control group (mean pre-test=42.70, post-test=45.78). An independent t-test confirmed a significant difference between the groups post-intervention (t=18.004, p < 0.001). Qualitatively, while 80% of users reported proficiency with OMT, 40% noted challenges with contextual understanding. The use of OMT as a CALL tool significantly enhances EFL students' writing performance, particularly in grammatical accuracy. However, to mitigate over-reliance and maximize learning outcomes, educator guidance is crucial to ensure critical and autonomous use of this technology.