Workshop
[Delivered at joint 8th International Conference on ESP in Asia and 3rd International Symposium on Innovative Teaching and Research in ESP, UEC, Tokyo. August 21, 2016]
In presentations, particularly during conference presentation Q&A, sci-tech EAP learners often prove unable to distil the underlying intentions of their research design or to identify the argument(s) surrounding their claim and the generalizability of their results.
These EAP learners usually have little training in rhetorical orchestration, especially since their research papers are built on the IMRAD structure, a rather poor metaphor for argument. As a result, these learners find spontaneous oral explanation and argument summarization difficult.
This workshop introduces the operation of a structured, low-text approach which has produced consistent, rapid development of the foundation target skills (argument analysis, argument construction) in classroom application (masters and PhD level). The key tool in this approach is the cross-platform freeware CmapTools, now widely adopted in science education. CmapTools automatically generates Novakian maps (maps in which each link is articulated by a relation phrase). Learners find these maps easy to evaluate in terms of correctness of relations and shockingly accessible in terms of structure of information.
This workshop begins with an overview of current styles of concept visualization (and their attendant syntax and information structures) so as to give participants a broad practical overview of mapping practice today. Participants will then be introduced to the use of CmapTools, and will take part in guided model task performance.
The workshop activities will be low-tech (post-its and marker pens) to maximize accessibility.
However, participants who would like to 'lean in' on this skill set are encouraged to download Cmap Tools to their laptops (Mac, Win or Linux) or iPads, familiarize themselves with the basic functions of the software (takes about 15 minutes), and show up equipped for bigger-curve learning.