ABSTRACT
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance.
ABSTRACT
In the past few years, CRISPR-based (Clustered regularly interspaced short palindromic repeats-based) detection techniques have arisen in the field of bioanalysis, molecular biology and clinical diagnosis. However, there are still some limitations, such as high cost, high technical requirements, complexity, cross contamination, lack of quantification strategies, etc. Microfluidic platforms, as emerging technol-ogies, have the potential to take CRISPR-based technology to the new level in the future. In this review, we illustrate the recent progress as well as our work and collaborative work in CRISPR-based microfluidic detection assays based on the categories of microfluidic chips, including paper-based microfluidics, centrifugal microfluidics, digital microfluidics, electrochemical microfluidics and wearable microfluidics, mainly focusing on how microfluidic platforms improve CRISPR-based detection method in the aspect of visualization, detection throughput, anti-contamination, simple operation, sensitivity, specificity and quantification, etc. And we outlook the future trends of CRISPR-based microfluidic detection methods in both innovation of CRISPR-based and design of microfluidic platforms. (c) 2022 Elsevier B.V. All rights reserved.
ABSTRACT
To meet the assessments requirements during the COVID-19 epidemic, many schools adopted the large-scale blended online examination, in which teachers invigilate through online video and students answer questions with pen and paper. Exploring the factors that influence students’ acceptance of the blended online examination will help the exam service understand the characteristics of students’ adoption and provide better support for staffs. It will help teachers and parents to assist students to take the blended examination and maintain the traditional exam atmosphere that will facilitate students’ learning performance and effectiveness. In this study, a questionnaire survey and structural equation method were adopted to explore the influence of perceived ease of use, perceived usefulness, social presence, place presence, and social influence on learners’ acceptance of blended online examination. Data analysis of 760 college students who underwent a blended online examination showed that perceived ease of use had a significant negative direct impact on exam acceptability and overall had a significant negative impact. Perceived usefulness, social presence, and social influence have significant positive effects on exam acceptability and social presence and social influence also have significant positive effects indirectly by influencing perceived usefulness. Finally, the limitations of this study are discussed, and the implications and future direction are put forward. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic and can occur at any age, including children. Children with COVID-19 can develop the clinical symptoms of multiple systems, among which symptoms of the nervous system have been reported increasingly, and thus it is particularly important to understand COVID-19-associated neurological damage in children. This article reviews the mechanisms and types of COVID-19-associated neurological damage in children.
ABSTRACT
The epidemic caused by coronavirus poses a serious threat to human health, but there is no specific drug or vaccine for the treatment of this kind of virus infection. Herein, this article selects typical case studies in recent years and reviews the medicinal chemistry strategies of anti-SARS-CoV, MERS-CoV and other coronavirus drugs from the perspective of medicinal chemistry, and tries to provide some clues to current drug research against-SARS-CoV-2.