This paper explores the potential role of technologies in further enhancing the pedagogical efficacy of PELE with an ultimate aim to increase its scalability and availability. First, it reports on a project that developed an asynchronous learning platform for PELE using OpenLearning, which was used to aid synchronous face-to-face teaching in 2019 and synchronous online teaching from 2020. Second, it will also introduce an ongoing pilot study that reviews and synthesizes third-party tests and tools, especially those of an automated nature based on artificial intelligence (AI), to help international students diagnose their English language proficiency.
In response to challenges with English communication faced by an increasing number of sojourners at the University of New South Wales (UNSW Sydney), an innovative course titled Personalised English Language Enhancement (PELE) has been running since 2016, based on Kim's Personalised Autonomous (PA) model (Kim 2014; Kim & Jing 2019). The PELE course has proven highly effective in addressing the challenges through developing students' linguistic confidence and self-efficacy (Kim 2018). The course has been offered every term since 2019 within UNSW's trimester system, and the popularity rises continuously, especially among international students. Thus, it becomes important to explore whether the pedagogical efficacy can be maintained or even further enhanced if PELE is to be offered to larger cohorts (several hundreds of students or more), and in blended mode (F2F and online) or fully online. This paper explores the potential role of technologies in further enhancing the pedagogical efficacy of PELE with an ultimate aim to increase its scalability and availability. First, it reports on a project that developed an asynchronous learning platform for PELE using OpenLearning, which was used to aid synchronous face-to-face teaching in 2019 and synchronous online teaching from 2020. It will discuss its design, distinctive features, and highlights its pedagogical efficacy based on teacher surveys [n=6] and large-scale student surveys [n=305] multiple terms in 2019-20. Second, it will also introduce an ongoing pilot study that reviews and synthesizes third-party tests and tools, especially those of an automated nature based on artificial intelligence (AI), to help international students diagnose their English language proficiency.ReferencesKim, M. (2014) 'Action Research on Advanced Bilingual Enhancement in Translator Education', in K. Kunz, E. Teich, S. Hansen-Schirra, S. Neumann and P. Daut (eds), Caught in the Middle – Language Use and Translation, Saarbrucken: Saarland University Press, pp. 195-213.Kim, M. (2018) A Personalised Autonomous Model for Multilingual University Students, The Applied Linguistics Association of Australia (ALAA) conference, Wollongong, November 2018.Kim, M. and Jing, B. (2019) 'A Personalised Autonomous Model for Enhancing Translation Students' Linguistic Competence,' in M. Koletnik and N. Froeliger (eds), Translation and Language Teaching – Continuing the Dialogue between Translation Studies and Language Didactics, Newcastle upon Tyne, Cambridge Scholars Publishing, pp. 127-146.
