Negative online public sentiment generated by government mishandling of pandemics and other disasters can easily trigger widespread panic and distrust, causing great harm. It is important to understand the law of public sentiment dissemination and use it in a timely and appropriate way. Using the big data of online public sentiment during the COVID-19 period, this paper analyzes and establishes a cross-validation based public sentiment system dynamics model which can simulate the evolution processes of public sentiment under the effects of individual behaviors and governmental guidance measures. A concrete case of a violation of relevant regulations during COVID-19 epidemic that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. By running the model, the results show that an increase in government responsiveness contributes to the spread of positive social sentiment but also promotes negative sentiment. Positive individual behavior suppresses negative emotions while promoting the spread of positive emotions. Changes in the disaster context (epidemic) have an impact on the spread of sentiment, but the effect is mediocre.

Digital filter design in electronics/electrical engineering involves complicated mathematical equations and dynamic waveform variations. It is a consensus among educators that using simulation tools helps improve students' learning experience. However, very few studies provided scientific evidence to support this notion. In a digital signal processing (DSP) course, students received three different types of instructional messages (online text only, text plus static illustrations, and text plus simulations). An experimental study was designed to investigate potential main effects of the independent variables: instructional message on students' post-test scores. Multivariate analysis of variance (MANOVA) was performed to analyze collected data. Results from 132 students showed significant differences among students in a post-test learning achievement (F(2,130)=13.590, p<.05). A follow-up test indicated that students who used a simulation tool performed significant better scores on their learning achievement test than those who only received online text materials and than those who received text plus static illustrations (p<.05)

Aim Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making. Subject and methods Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics. Results The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds. Conclusion The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts.

Computer Science 101 at Penn State University provides an introduction to fundamental techniques of problem solving using a procedural computer programming language: C++. Since no prior computer programming experience is assumed, one major problem observed in the course is that students learn about programming in a somewhat passive pattern. This paper proposes to create a blended learning environment utilizing a case-based reasoning (CBR) approach to improve students' programming skills. CBR is an innovative instructional approach that utilizes the specific knowledge of previously experienced problem situations (cases). A new problem can be solved by finding a similar past case and reusing it in the new problem situation. Then a new case is retained each time when a problem has been solved, making it immediately available for future problem

This study aims at exploring pre-service teachers’ perspectives on one-to-one (1:1) computing in teaching and learning. It gathered data regarding various aspects of 1:1 computing from 145 pre-service teachers at a public university in USA. Both quantitative and qualitative methods were applied in data analysis. Findings show that pre-service teachers consider the use of 1:1 computing in the classroom changes interactions between instructors and students. The positive influence includes enhanced communication and providing medium for presenting materials. Significant difference was found in time spent using 1:1 computing in varied types of settings (F(3,576)=37.70, p<.05). In addition, pre-service teachers believe that technology can influence teaching in a positive way; the use of 1:1 computing increases engagement and facilitates knowledge transfer.