Abstract: Context: Engineering HDR students (EHDRs) with low level language skills contribute disproportionately to low completion rate statistics and high supervisor workloads. The research presented here is investigating a novel, tri-partite approach to language learning, in the distinctive context of engineering modes of cognition, designed to accelerate the development of EHDRs' language learning skills. Drawing together threads from Systemic Functional Linguistics (SFL), Editing Practice (EP), English for Academic Purposes (EAP) and Gifted and Talented Education (GATE), it builds around a needs analysis conducted with EHDRs at the University of Adelaide, which include a significant cohort of international students at this level of study. The solution seeks to engage at both intellectual and emotional levels, creating flow or intense learning experiences.
Purpose: The purpose of this research is two-fold: to explore how engineers learn and to align language teaching pedagogies to engineering modes of cognition (EMoCs).
Approach: This early research uses Participative Action Research (PAR) spirals to ensure that learning and outcomes are egalitarian in nature and constantly refined in the light of all possible information. The spiral components are a) purpose-designed Language Trees, b) a Concordancing tool using a new corpus fashioned from extant, recent, published Mechanical Engineering journal articles and c) a Systems Engineering tool: a new app using Boolean logic to enhance self-editing skills. The evaluative data is both qualitative and quantitative, collected using a mixture of specific questions which require responses on a seven point Likert scale and semi-structured interviews/questions to enrich the numerical data.
Results: Early results are currently available from the first two spirals, evaluating the use and impact of the Language Trees and the Concordancing tool for Mechanical Engineers. These results show high levels of engagement, the transfer of skills from the research workshops to free writing and support in dealing with both perfectionism and imposter syndrome, leading to an improvement in engagement and flow in the students' own work.
Conclusions: Tentative current conclusions include 1) that the approach is working effectively to lift engagement and up-skilling of students; 2) that the physical-tactile, visual-spatial nature of the Language Trees is highly engaging for self-editing purposes and 3) that the Concordancing tool is solving both specific collocation issues and issues surrounding noun phrases in expository writing, using a headword system for phrase generation. We are hoping to have completed very early trials of the Systems Engineering tool (an online grammar device using both SFL and Traditional Grammar (TG) approaches) by the time of final full paper submission.
To cite this article: Hunter, Alison-Jane; Kestell, Colin and Cargill, Margaret. Optimising accurate academic language skills for engineering higher degree by research students [online]. In: 27th Annual Conference of the Australasian Association for Engineering Education : AAEE 2016. Lismore, NSW: Southern Cross University, 2016: 367-376.
[cited 26 Jul 17].