ABSTRACT
In 2020, many nurses were confronted with heightened work-related and personal stressors imposed by the COVID-19 pandemic. As daily routines were upended, we wanted to understand the impact on nurses' participation in continuous learning. We retrospectively analyzed the learning logs of 194 nurses enrolled in a 12-month distance learning course, one cohort from March 2019 to February 2020 and one from March 2020 to February 2021 during the COVID-19 pandemic. The frequency of monthly logins for the COVID-19 pandemic cohort was compared for nurses with and without prior distance learning experience. Login frequency was also compared for nurses who cared directly for COVID-19 patients and those who did not. Monthly login frequency for March 2020 was significantly higher than for March 2019, while log in frequency for April 2020 was significantly lower than for April 2019. We attribute this to an increase in COVID-19 cases, hospitalizations, and deaths in April 2020. From March 2020 to August 2020, login frequency was significantly higher for nurses without previous distance learning experience, suggesting their distance learning strategies were not yet established. During September and October 2020, login frequency was significantly higher in the group with distance learning experience, from which we inferred active procrastination. We found no significant differences in the login frequencies of nurses who cared for COVID-19 patients and those who did not. The results of our study suggest that stressors imposed by the COVID-19 pandemic had a significant negative impact on distance learning progress. Screening in advance for previous distance learning experience and providing mentoring and learning supports are recommended to mitigate interference with distance learning progress during times of heightened professional and personal stress. © 2021 IEEE.
ABSTRACT
With the increasing proliferation of mobile phone, internet and communication technologies, social network sites (SNS) are gaining importance worldwide. People express and exchange their opinion on various social network sites like twitter, facebook, blogs over different local and global issues. Efficient analysis of such vast text data from SNS provides a good way of understanding insights of public opinion, government policy and social condition of different countries. Topic modeling is a popular tool for extracting information from text data. Dynamic topic tracking and its visualization provides a means for capturing the change of topics over time which is important for visualization of changing needs of the society and keeping updated with the current situation. In this work, COVID-19 related twitter data in two different languages are collected and analyzed by dynamic topic model to track the spread of the evolved topics during the pandemic in two different countries in order to visualize the differences and commonness of the effect of pandemic. Here we mainly focused on the tweet data related to Japan and India in Japanese and English respectively. It is found that the country specific characteristics are prominent in some topics while some topics express the general concerns during the pandemic. This study seems to be effective to provide a technique for capturing the opinion and needs of people during a pandemic by analysis of tweet data. © 2021 IEEE.
ABSTRACT
Latent Dirichlet Allocation (LDA) is a typical example of a topic model that estimates the latent topics of sentences. It is widely used in topic discovery, information retrieval, and document modeling. In recent years, with the advancement of research about neural networks, topic models using neural networks, such as NVLDA and ProdLDA, have been presented. Since it is easy to use accelerators such as GPUs for training these, topic modeling for large corpora can be done efficiently. Topic models are also used for short texts such as social networking sites and product reviews, where the number of words in a document is often short. This means that the co-occurrence information of words is also extremely less, and it is difficult to infer latent topics for easy understanding by humans when training with only the target corpus. In this paper, we investigated whether trained word embedding vectors on other large corpora such as Wikipedia compensate for the lack of word information in short texts. While previous studies have been conducted on long texts such as newspaper articles, we specifically checked the effect on short texts. As a result, we confirmed that under many conditions, Topic Coherence evaluation using Wikipedia was improved by using word embedding. However, we were not able to achieve stable high performance in terms of topic diversity. We also used this approach for topic modeling of tweets related to COVID-19. © 2021 IEEE.