محسن محمودی دهکی

Languages as living and dynamic structured systems were influenced at the very first moments of the COVID-19 pandemic as they became medicalized by the use and practice of various jargons and technical terms. Meanwhile, homebound individuals around the world started to create a COVID-19 pandemic-related lexical-overload of neologisms (Coroneologisms or Corona coinages) in different languages which started to spread across the globe by mass media, and some of them even entered the official databases of the well-known dictionaries and Wikipedia corpora. This study was a scrutinized attempt to explore the English vs. Persian (Farsi) Coroneologisms across various Extensible Markup Language (XML) corpora and a huge body of E-resource discourses (websites, social media, and news channels and forums) based on embedded mixed-method design to provide a comprehensive insight into their types, structures, and meanings. To provide a better understanding of how and why particular Coroneologisms were created in terms of social, cultural, or political contributing factors, they were investigated based on the reflections of a series of sociolinguistic focus-group E-interviews with 184 volunteer English and Persian native speakers. This study might provide implications for sociolinguistics, corpus-based language studies, lexicographers, designers of corpora, etymologists, and discourse analysts in English and Persian.

The upheaval time of COVID-19 highlighted the importance of an effective way of teaching English to the front-line healthcare workers such as medical doctors and researchers with different digital-divide status, particularly in non-English-speaking countries because they were first-hand users of critical pandemic-related information in mostly-English articles published online by scientific journals. This study attempted to investigate the pedagogical effects of utilizing the two main User-Generated Content (UGC) platforms in E-learning, namely LMS vs. LXP, on the results of the Electronic Ministry of Health Language Examinations (E-MHLE) among Iranian English for Medical Purposes (EMP) learners across their digital-divide status (digital natives vs. digital immigrants) during the COVID-19 pandemic. A series of focus-group E-interviews were conducted to determine the reasons behind the lowest scores among the participants and to find out possible suggestions for success in high-stake E-tests. To this end, 272 EMP learners who were users of LMS and LXP were conveniently selected from a university of medical science to participate in this sequential explanatory mixed-method research. The results of descriptive and inferential statistics showed that the LXP group outperformed the LMS one in the results of E-MHLE. Moreover, the digital natives obtained higher scores than the digital immigrants in both groups but the difference was not significant in the LXP. The findings of the E-interviews were thematically analyzed and discussed. The findings of this study might offer practical and realistic benefits to the whole EMP community, particularly policymakers for the post-COVID-19 era.