Choosing optimal means of knowledge visualization based on eye tracking for online education.

In online education, the appropriate choice of means of knowledge visualization can reduce cognitive load and improve cognitive efficiency. However, no universal basis for selection can cause confusion in the pedagogical context. This study used the revised Bloom's taxonomy to combine the types of knowledge with cognitive goals. We used a course on marketing research as an example to summarize the choices for visualizing factual knowledge (FK), conceptual knowledge (CK), procedural knowledge (PK), and metacognitive knowledge (MK) through four experiments. Visualized cognitive stages were used to determine the cognitive efficiencies of visualization for different knowledge types. In this stage, eye tracking is used for collecting eye movement indicators to measure cognitive load. The cognitive goals stage is used to get cognitive goals of the means of knowledge visualization. Combining the two stages, we get the conclusions as follows: Teachers and students can mostly benefit from presenting FK and CK points via mind maps. Using mind maps to teach FK online could be indirectly beneficial for improving students' creativity. Concept maps may be chosen for this point if the linked knowledge points are PK and the achievement of the analytical objective is emphasized in the student's knowledge points. The flowchart can be used to display PK, while timelines could be utilized if the PK point is to be presented in a temporal dimension. Teachers should choose the curve area chart to display MK. A pie chart might be chosen and added more instructions. The findings suggest that mind maps are very effective as a means of knowledge visualization in online education. In the meantime, it suggests that overly simplistic graphs increase cognitive load, while it also raises the possibility that redundant information in the text may increase cognitive load.

The effect of natural and socioeconomic factors on haze pollution from global and local perspectives in China.

Analyzing the factors that cause haze and the regional differences in the influence of factors on haze is the premise and critical to precise prevention and control of haze pollution. This paper explores the global effects of haze pollution drivers and the spatial heterogeneity of factors on haze pollution using global and local regression models. The results show that, from a global perspective, a 1 µg/m3 increase in the average PM2.5 concentration of a city's neighbors will increase the city's PM2.5 concentration by 0.965 µg/m3. Temperature, atmospheric pressure, population density, and green coverage of built-up areas are positively associated with haze, while GDP per capita is the opposite. From a local perspective, each factor has different influencing scales on haze pollution. Specifically, technical support is on a global scale, and for every 1 unit increase in technical support level, the PM2.5 concentration will decrease by 0.106-0.102 µg/m3. The influencing scales of other drivers are local. In southern China, the concentration of PM2.5 decreases by 0.001-0.075 µg/m3 for every 1 °C increase in temperature, while in northern China, the concentration of PM2.5 increases by 0.001-0.889 µg/m3. In the region around the Bohai Sea in eastern China, the concentration of PM2.5 will decrease by 0.001-0.889 µg/m3 for every 1 m/s increase in wind speed. Population density positively impacts haze pollution, and the impact intensity gradually increases from 0.097 to 1.140 from south to north. For every 1% increase in the proportion of the secondary industry in southwest China, the PM2.5 concentration will increase by 0.001-0.284 µg/m3. For cities in northeast China, for every 1% increase in the urbanization rate, the PM2.5 concentration will decrease by 0.001-0.203 µg/m3. These findings help policymakers develop targeted joint prevention and control policies for haze pollution, considering regional differences.

Impact of anti-dumping on global embodied air emissions: a complex network perspective.

After the rise of trade protectionism, anti-dumping has become a common means of political and trade games between countries. Global supply chains move production emissions between countries or regions through trade. In the context of carbon neutrality, anti-dumping measures representing the right to trade may become a tool for the game of emission rights between countries. Therefore, it is very important to study the environmental effects of anti-dumping to cope with global climate change and promote national development. Taking a sample of 189 countries and regions from the EORA input-output table with a study period of 2000-2016, we use the complex network, multi-regional input-output and panel regression models to verify the impact of anti-dumping on air emission transfer by constructing an anti-dumping network and an embodied air emission network. The results show that the initiator of anti-dumping can use anti-dumping to realize the cross-border transfer of ecological costs, reduce the burden of emission reduction and save more on emission quota. Developing countries lacking the right to speak in trade will increase the export volume of commodities after being subjected to a large number of anti-dumping sanctions, thus paying higher ecological costs and consuming more emission quotas. From a global perspective, additional emissions from product production can further contribute to global climate change.