Identity Text: an educational intervention to foster cultural interaction

Background: Sociocultural theories state that learning results from people participating in contexts where social interaction is facilitated. There is a need to create such facilitated pedagogical spaces where participants share their ways of knowing and doing. The aim of this exploratory study was to introduce pedagogical space for sociocultural interaction using ‘Identity Text’.
Methods: Identity texts are sociocultural artifacts produced by participants, which can be written, spoken, visual, musical, or multimodal. In 2013, participants of an international medical education fellowship program were asked to create their own Identity Texts to promote discussion about participants’ cultural backgrounds. Thematic analysis was used to make the analysis relevant to studying the pedagogical utility of the intervention.
Result: The Identity Text intervention created two spaces: a ‘reflective space’ helped
participants reflect on sensitive topics like institutional environments, roles in
interdisciplinary teams, and gender discrimination. A ‘narrative space’ allowed
participants to tell powerful stories that provided cultural insights and challenged cultural hegemony; they described the conscious and subconscious transformation in identity that evolved secondary to struggles with local power dynamics and social demands involving the impact of family, peers and country of origin.
Conclusion: Whilst the impact of providing pedagogical space using Identity Text on
cognitive engagement and enhanced learning requires further research, the findings of
this study suggest that it is a useful pedagogical strategy to support cross-cultural
education.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.