With the popularity of MOOCs and other online learning platforms, Educational Data Mining (EDM) has been receiving tremendous attention from researchers due to its great significance. Modeling students' effort and learning ability is a very interesting but challenging research topic. It is beneficial for student profiling, personalization recommendation, etc. Thus, numerous attempts have been devoted to this study. However, most of the existing work treat the problem in a static scenario, but they ignore the dynamic characteristics in real word applications. To address this problem, we propose a novel model to describe students' effort and learning ability (ELA) from a generative perspective. The temporal variations of both effort and learning ability of students are taken into account. To evaluate the performance of the proposed model, some extensive experiments are carried out. The experimental results have demonstrated that the proposed model outperforms other competitive methods greatly.
Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.
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