Incorporating Mobile-based Artificial Intelligence to English Pronunciation Learning in Tertiary-level Students: Developing Autonomous Learning - Sariani, -; Miladiyenti, Fithratul; Rozi, Fazrol; Haslina, Welsi; Marzuki, Dony (International Journal of Advanced Science Computing and Engineering, 2022)
EFL pronunciation learning is known to be teacher-centered, in which students are reliant on the feedback given by their teachers. Fortunately, the presence of technology-based learning tools is believed to be able to reduce students' dependency on teachers. A built-in Automatic Speech Recognition technology (ASR): ELSA Speak is a mobile-based application that could help students with pronunciation learning by providing prompt feedback and detailed evaluation of its users' pronunciation accuracy. The objective of this study was to examine the use of the mobile-based application, ELSA, in pronunciation learning and whether it could stimulate learners to become more autonomous. A mix-method approach was employed in this study. The quantitative data, in the forms of pre-test and post-test, was gathered through students' pronunciation recordings available in ELSA. This data was analyzed using the paired sample t-test. In addition, a Google Form questionnaire, in five-point Likert Scale, was used to support the quantitative findings. Meanwhile, a semi-structured interview with selected participants served as the qualitative one. These data were analyzed thematically based on the participants' responses. The result shows that incorporating mobile-based technology into English pronunciation learning could provide an opportunity for students to improve their pronunciation. Moreover, students could also broaden their repertoire of available practice strategies that might be useful to improve their autonomous learning ability.