Dataset Information

Leveraging affordances in an ecological stance: Reflective language teaching for professional development during COVID-19.

ABSTRACT: The COVID-19 pandemic has made a prominent impact of social contexts on teachers' professional development in remote classroom teaching. To explore how the change has altered human-environment relationships in university language classes, this qualitative case study investigated three teachers' progressive reflection on their use of affordances for teaching Chinese as a second language (L2) during COVID-19. Under the framework of human ecological language pedagogy, three themes of emergency remote teaching emerged from monthly semi-structured interviews about the three teachers' reflective practice in remote classrooms: computer-dominant teaching conditions, flexible classroom interaction, and rational social empathy in L2 education. The findings suggest the importance of a growth mindset for L2 teachers to leverage their teaching abilities and environmental resources for continuing professional development during COVID-19 and post-pandemic periods.

SUBMITTER: Chen M

PROVIDER: S-EPMC10150415 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Leveraging affordances in an ecological stance: Reflective language teaching for professional development during COVID-19.

Chen Mengtian M

Heliyon 20230501 5

The COVID-19 pandemic has made a prominent impact of social contexts on teachers' professional development in remote classroom teaching. To explore how the change has altered human-environment relationships in university language classes, this qualitative case study investigated three teachers' progressive reflection on their use of affordances for teaching Chinese as a second language (L2) during COVID-19. Under the framework of human ecological language pedagogy, three themes of emergency remo ...[more]

PMID: 37153434

Similar Datasets

Project description:ObjectivesThe automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.Materials and methodsA corpus of 6170 tweets about COVID-19 vaccination, posted between March 1, 2020 and January 4, 2022, was manually annotated by native speakers. Traditional predictive models were compared with deep learning models to ascertain a baseline performance for the detection of stance in Spanish tweets. The evaluation focused on the ability of multilingual and language-specific embeddings to contextualize the topic of those short texts adequately.ResultsThe BERT-Multi+BiLSTM combination yielded the best results (macroaveraged F1 and Matthews correlation coefficient scores of 0.86 and 0.79, respectively; interpolated area under the receiver operating curve [AUC] of 0.95 for tweets against vaccination and 0.85 in favor of vaccination and a score of 0.97 for tweets containing no stance information), closely followed by the BETO+BiLSTM and RoBERTa BNE-LSTM Spanish models and the term frequency-inverse document frequency+SVM model (average AUC decrease of 0.01). The main differentiating factor among these models was the ability to predict tweets against vaccination.DiscussionThe BERT Multi+BILSTM model outperformed the other models in terms of per class prediction capacity. The main assumption is that language-specific embeddings do not outperform multilingual embeddings or TF-IDF features because of the context of the topic. The inherent context of BERT or RoBERTa embeddings is general. So, these embeddings are not familiar with the slang commonly used on Twitter and, more specifically, during the pandemic.ConclusionThe best performing model detects tweet stance with performance high enough to ensure its usefulness for public health applications, namely awareness campaigns, misinformation detection and other early intervention and prevention actions seeking to improve an individual's well-being based on autoreported experiences and opinions. The dataset and code of the study are available on GitHub.

Project description:Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

Dataset Information

Leveraging affordances in an ecological stance: Reflective language teaching for professional development during COVID-19.

Publications

Leveraging affordances in an ecological stance: Reflective language teaching for professional development during COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets