The Use of Mouth Movement Video and Digitised Phonetic Symbols on Pronunciation Learning among Learners with Different Psychological Profiles

Being able to communicate is relatively indispensable in today’s world considering the increased people’s mobility, joint study programmes, and commercial networks. Pronunciation is a salient element of effective communication. However, due to different cultural backgrounds and native languages, non-native English speakers experience pronunciation difficulties. The conventional pronunciation teaching method of aural-oral approach has not achieved the expected outcomes, and it shapes the learners to be passive and dependent by mere duplicating the sounds of words without any explicit understanding to critically discern the sounds. Thus, this study investigates the contribution of mouth movements and digitised phonetic symbols on the learning of pronunciation among non-native English speakers with different psychological profiles. The Multimedia Pronunciation Learning Management System (MPLMS) designed and developed in this study is expected to address the issues by teaching pronunciation through the universally agreed system of phonetic symbols. A 3 x 3 x 2 quasi-experimental factorial design in a non-equivalent control group pretest-posttest design is adopted in this study. The independent variables are the three modes of Multimedia Pronunciation Learning Management System (MPLMS): Text + Sound + Mouth Movements (TSMM), Text + Sound + Phonetic Symbols (TSPS), and Text + Sound + Mouth Movements + Phonetic Symbols (TSMMPS). The moderator variables are visualiser/verbaliser, language anxiety and language aptitude. The dependent variable is pronunciation competence (as measured by the posttest mean scores). ANOVA is to be used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc will be carried out to examine the main effects as well as interaction effects of the independent variables on the dependent variables. Simple and Multiple Regression Analysis will be used to determine the relative contribution of each factor to the changes in the dependent variable.