The Effectiveness of Machine Translating Tools to Enhance Higher Education Students' Knowledge in Non-English-Speaking Countries: Best Practice From Indonesia

Introduction

English language competency is one of the obstacles for university students in Indonesia in increasing their knowledge. This happens because most knowledge sources are written in English. Quality knowledge sources are mostly written in English. Without good English language competence, it is quite difficult for university students to immediately understand the contents of texts in English. One solution that is often used is translating English texts into Indonesian. Translating texts from English or other foreign languages into Indonesia is not easy because of their limited English skills. This means that students cannot easily translate a difficult text just by relying on their own abilities.

The solution often used by university students to overcome this problem is to use machine translation tools to translate difficult English texts into Indonesian language or to translate Indonesian texts into English when they want to write in English. The reason why they use machine translation tools to translate texts from English into the Indonesian language or vice versa is due to their lack of written English competence. It is hard for them to understand difficult English texts or to write in English. This happens because not all university students in Indonesia are good at English, particularly at written English. The difficulty they have in understanding or writing English texts forces them to use machine translation tools. The use of machine translation tools is very helpful in terms of students quickly understanding the contents of a text in a foreign language that they did not previously understand (Aiken & Balan, 2011). Unfortunately, sometimes the translation results using machine translation tools are less than satisfactory due to the low level of translation accuracy. When this happens, the accuracy of the scientific information obtained by the students can of course deviate greatly.

Previous research results that explore the accuracy of translation results using machine translation tools, especially Google Translate (GT), show that the translations are not very accurate in several aspects such as the use of vocabulary, grammar, or sentence complexity (Milam Aiken, 2019; Lee, 2021; Qin, 2019).

Tongpoon-Patanasorn and Griffith (2020) express that the quality of translations using machine translation tools does not meet language accuracy standards for scientific writing because errors related to punctuation and fragmentation still appear. The empirical data that I have from my experience as a translation lecturer also show that the results of translations made by students using machine translation tools are of poor quality due to inaccuracies in the use of grammar, vocabulary, and sentence editing. As a result, the meaning in the source language is not conveyed clearly in the target language. Preliminary data in the form of students’ work that we have also show that actually poor translation quality is not caused by the machine translation tools used, but because the students do not know the best way to use the machine translation tools. In short, previous studies were limited to describing translation results using translation tools but did not present the best techniques for using machine translation tools. Therefore, this research provides a relevant and significant contribution to students in countries where English is a foreign language. In this case, the paramount contribution is that students can understand and apply best practices in using machine translation tools to translate a text with quality results.