Faculty Publications and Conference Presentations

The current study employs a contrastive approach to analyze five consonantal features (TH stopping/ fronting, L vocalization, [n, l]/[s, ʃ] conflation, /r/, /v/, /w/ substitution and consonant cluster modification [CCM]) of Hong Kong English (HKE) from 37 online sound clips from 29 speakers. Compared to the traditional contrastive approach, the current study uses a world Englishes paradigm to analyze the data, which aligns more with the recent movement of world Englishes. The result shows that all the five features exist in the corpus; however, TH-stopping/fronting and CCM are more common than others. The results behind the features in HKE could be hinted from the comparison with Cantonese, the L1 of Hongkongers. Moreover, the results help to develop the categorization of HKE speakers—Hong Kong English Continuum—which potentially facilitates the discussion of HKE under the world Englishes paradigm in the long run.

<p>The current article presents a state-of-the-art review of research on the social factors that have been found to impact how learners acquire and use a second language (L2) sound system. These factors include ethnic group identification, gender, and study abroad experience. The research synthesis presents the key findings on each social factor, with examples drawn from the cited research for illustration. The article then presents recommendations for pedagogical practice. These recommendations are aimed at both teachers and learners, and for use both outside and inside the L2 classroom; they include examples and links to free online resources that both teachers and learners can use to enhance meaningful L2 pronunciation teaching and learning.</p>

<p>Recent years have witnessed a rapidly growing trend of incorporating synchronous online teaching tools into language teaching, yet the interaction patterns that unfold in online environment and its effectiveness on young learners remain underexplored. The present study narrows this research gap through closely examining the multi-modal exchanges between a veteran primary teacher and his EFL Grade 6 students during synchronous online English lessons, using a video-conferencing tool called ZOOM. 80 recordings from whole-class and small-group sessions over a four-month span were obtained and the various modes of synchronous computer-mediated communication that the teacher employed as well as spoken discourses were analyzed. The findings indicated that the teacher successfully utilized the affordances provided by ZOOM to elicit a large number of non-verbal responses and expanded verbal responses from students. The better-able students also demonstrated remarkable interactional skills during small-group sessions, as seen from their increased use of prompting and repair speech acts. Students’ reticence emerged as an alarming concern, though it was alleviated by extending the wait-time. Overall, this study offers a prototype for primary teachers to base upon during synchronous online lessons, whilst also highlights the need for re-conceptualizing the constituents of classroom interactional competence (CIC).</p>

<p>Background: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. Objective: We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. Methods: Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. Results: We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. Conclusions: Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.</p>