Teaching interpreting in the time of COVID: exploring the feasibility of using Gather
Keywords
Interpreter educationVirtual learning environment
Synchronous teaching and learning online
Gather
/dk/atira/pure/sustainabledevelopmentgoals/quality_education
SDG 4 - Quality Education
/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
SDG 3 - Good Health and Well-being
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https://pure.qub.ac.uk/en/publications/63aa66fe-cd9c-4b01-8dfa-3a983af49e9ahttps://doi.org/10.1007/978-981-19-6680-4_16
Abstract
Interpreter education has undergone tremendous changes in the past two years due to COVID-19, with training being confined to the virtual environment. Luckily, there are now signs of returning to campus, but the uncertain development of the pandemic renders the arrangement precarious. Many differences exist between distance and on-site learning, and therefore the pedagogy for one cannot be entirely replicated for the other. Interpreting scenarios that rely less on interaction or non-verbal communication are easier to accommodate in both remote and on-site settings, though designing and implementing activities, providing feedback, and maintaining classroom dynamics are still harder in virtual classrooms. By contrast, training for escort and public service interpreting is difficult to be delivered remotely because non-verbal cues, which play a bigger role in these settings, are harder to capture or they disappear entirely, including gesture, facial expressions, posture, and proximity. This study explores how the challenges facing distance interpreter education can be mitigated using Gather, a proximity-based platform. Online synchronous CI and SI teaching setups for two mock events were introduced, and a questionnaire was used to understand students’ experience with Gather, Microsoft Teams, and face-to-face training. Preliminary findings show that the majority of the participants are positive about Gather, and it has the potential to bridge the gap between distance and on-site interpreter education in both online and offline scenarios, although its applicability remains to be tested with a larger sample size.Date
2023-01-01Type
bookPartIdentifier
oai:pure.qub.ac.uk/portal:publications/63aa66fe-cd9c-4b01-8dfa-3a983af49e9ahttps://pure.qub.ac.uk/en/publications/63aa66fe-cd9c-4b01-8dfa-3a983af49e9a
https://doi.org/10.1007/978-981-19-6680-4_16
urn:ISBN:9789811966828
urn:ISBN:9789811966798