The Use of Digital Tools in French as a Second Language Teacher Education in Ontario

This article discusses a 2021 survey of French as a second language (FSL) teacher candidates (TCs) in faculties of education in Ontario whose practice teaching experiences were affected by the COVID-19 pandemic, pivoting them into remote FSL teaching and learning. The survey, which formed a component of a larger mixed method SSHRC-funded research project, was designed to capture the varied practice-teaching experiences of FSL teacher candidates in order to ascertain symmetries and asymmetries in their preferred digital practices, devices, and tools for both social communication and French language teaching and learning. Survey respondents from different teacher education programs in universities across Ontario provided a picture of scattered and fragmented approaches to FSL digital pedagogies and hinted at a persistent reliance on traditional FSL pedagogies in the classroom. Digital preferences for teaching and learning tended to be anchored in common educational tools and platforms that reaffirmed teacher-centred approaches to FSL rather than more innovative, learner-centred, and agentive language teaching and learning. The survey results raise an important question: Has FSL teacher education adequately moved with the communicative changes wrought by wider socio-technical transformations and related pedagogical innovations?

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.