A bibliometric analysis of the trends, topics, and findings of research publications on asynchronous and synchronous online language learning over three decades

Since the first study on computer-mediated communication tools in support of language learning was published in 1992, asynchronous and synchronous tools have been widely adopted;however, few reviews have been conducted to explore the research status in this field. As COVID-19 has increased the use of online tools in education, the need to understand how asynchronous and synchronous tools are being used in language education has grown. In this bibliometric analysis, we reviewed asynchronous and synchronous online language learning (ASOLL) by analyzing the trends, topics, and findings of 319 articles on ASOLL. The results indicate that interest in ASOLL has increased over the past three decades with ASOLL for oral proficiency development and collaborative ASOLL being the two main research issues. Interest in three topics collaborative ASOLL, emotions, and corrective feedback - was especially apparent. The review contributes to the understanding of ASOLL while providing practical implications for using information communication technologies to enhance language learning.

An Innovative Judge Monitoring Algorithm for the Online Defense

Since the outbreak of COVID-19, teaching and learning activities have gradually shifted online. In addition to traditional teaching, the student defense, thesis proposal, interim inspection, admission and other defense processes are also online. Online teaching and online assessment methods are facing challenges in student engagement. At the same time, for teaching executors (teachers), there are also participation and emotional initiative problems. Especially in the defense process, it is not easy to adequately monitor the teacher evaluation process because of the use of secret ballots. In this paper, we propose a defense monitoring algorithm that analyzes the working status of the judges from the defense score data given by the judges in order to identify judges who are not working seriously and remove their scores. A large amount of measured and simulated data is available to justify the algorithm. © 2022 IEEE.

Equilibrium social activity during an epidemic

During an infectious-disease epidemic, people make choices that impact transmission, trading off the risk of infection with the social-economic benefits of activity. We investigate how the qualitative features of an epidemic's Nash-equilibrium trajectory depend on the nature of the economic benefits that people get from activity. If economic benefits do not depend on how many others are active, as usually modeled, then there is a unique equilibrium trajectory, the epidemic eventually reaches a steady state, and agents born into the steady state have zero expected lifetime welfare. On the other hand, if the benefit of activity increases as others are more active ("social benefits”) and the disease is sufficiently severe, then there are always multiple equilibrium trajectories, including some that never settle into a steady state and that welfare dominate any given steady-state equilibrium. Within this framework, we analyze the equilibrium impact of a policy that modestly reduces the transmission rate. Such a policy has no long-run effect on society-wide welfare absent social benefits, but can raise long-run welfare if there are social benefits and the epidemic never settles into a steady state. © 2022 Elsevier Inc.

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.

Study of Fluorescence Immunochromatography Based on Quantum Dots for the Detection of SARS-CoV-2 anti-N Protein IgG Antibody

Background: Based on the current prevalence of Coronavirus Disease 2019 (COVID-19), early diagnosis, isolation, and treatment are important methods to prevent and control infectious diseases. The establishment of convenient and efficient immunochromatographic detection techniques is essential for the prevention and control of COVID-19 epidemic. Objective: To establish a method for the detection of SARS-CoV-2 anti-N protein IgG antibody by immun of luorescence chromatography method based on quantum dots labeling technology in August, 2020. In order to determine whether the detected persons had been infected with COVID-19 or been injected with SARS-CoV-2 inactivated vaccine. Methods The prepared rat anti-human secondary antibody and anti-N protein antibody were immobilized on a Nitrocellulose (NC) membrane as detection line (T) and quality control line (C), respectively. Then the SARS-CoV-2 N protein labeled by quantum dots was evenly sprayed on glass fiber, which was assembled, cut and packaged into test strips after drying. The test strips were used to detect the clinical serum of 35 COVID-19 patients and 50 healthy individuals, the results of the initial screening of the ELISA kit were used as a control to calculate the detection specificity and sensitivity of quantum dots fluorescence immunochromatography. The sensitivity of the test strip was detected by using the N protein antibody standard. Results The specificity and sensitivity of the strip were 100.00%, 94.29%, and the susceptibility was 8.53-17.06 ng/ml antibody concentration. Conclusion: The detection of anti-N protein IgG antibody in serum by quantum dots labeling is simple, fast, with strong sensitivity and specificity. Copyright © 2022 by the Chinese General Practice.

Synchronous Computer Mediated Communication in English Language Classes During the Pandemic: A Case Study of Wuhan

This research reports on a case study applying synchronous computer-mediated communication (SCMC) in a Wuhan university’s English language classes during the COVID-19 pandemic. The aim was to identify the advantages and disadvantages of using synchronous technology applications in online English language courses. The dataset consisted of ethnographic observations and in-depth interviews with the teacher and students. The thematic analysis revealed that the advantages of the SCMC include the availability of abundant learning resources, the affordance of instant information exchange, and a comparatively relaxed learning environment. Two major disadvantages were that the non-face-to-face communication tended to lead to the teacher’s “one-person” show, and the limited screen size reduced eye contact between the teacher and students. The present research indicates that SCMC can be applied during and after the pandemic to foster student discussion and collaboration. We also suggest that when language teachers deliver courses using SCMC tools or platforms, students should be given abundant opportunities to express their opinions. © 2021, Springer Nature Switzerland AG.

An Assessment Framework for Online Active Learning Performance

Under the influence of COVID-19, online learning has become the primary way for students to continue their education. At all stages of online learning, active learning is a useful strategy promoting optimal understanding. However, there is a lack of relevant research on how to evaluate students’ active learning performance. This paper presents an online active learning assessment framework based on the learning pyramid and learning dimension theory. After the division of course modules according to the learning pyramid theory, the active learning assessment is performed from five dimensions: (1) positive attitudes and perceptions about learning;(2) acquiring and integrating knowledge;(3) extending and refining knowledge;(4) using knowledge meaningfully, and (5) productive habits of mind. By identifying patterns from each online course module’s weblog data, instructors can assess students’ active learning conveniently from the beginning to the end of the online course. This study helps instructors understand learners’ learning situations and adopt corresponding strategies to adjust teaching activities to ensure high-quality teaching activities. Simultaneously, learners can also actively change their learning status according to active learning assessment to improve the learning effect. © 2021, Springer Nature Switzerland AG.

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019)

Massive Open Online Courses (MOOCs) have become a popular learning mode in recent years, especially since the outbreak of COVID-19 in late 2019, which had resulted in a significant increase in associated research. This paper presents a bibliometric review of 1078 peer-reviewed MOOC studies between 2008 and 2019. These papers are extracted from three influential databases, the Web of Science (WOS), Scopus, and the Education Resources Information Center (ERIC). The MOOC literature analysis with a bibliometric approach identified the research trends, journals, countries/regions, and institutions with high H-index, scientific collaborations, research topics, topic distributions of the prolific countries/regions and institutions, and annual topic distributions, after which the representative research and research implications were discussed. This review gives researchers a deep and comprehensive understanding of current MOOC research and identifies potential research topics and collaborative partners, which supports MOOC-related future research. © 2021, Education Research Institute, Seoul National University, Seoul, Korea.