Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.
The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.
Grazing and enclosing are two of the most important grassland managements. In order to evaluate the effects of different managements on the ecosystem balance of grassland, the vertical distributions of soil nutrients and their stoichiometric ratios were determined in the plots of grazing and enclosing over 38 years in a semi-arid grassland of Inner Mongolia. The results showed that total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), calcium (Ca), magnesium (Mg) and sulfur (S) contents in 0–100 cm soil in the long term enclosing plot were lower than the long term grazing plot and these changes were much greater in the surface soil than in deep soil. However, the soil organic carbon (SOC) and available phosphorus (AP) contents in the long term enclosing plot in the surface soil were higher (p < 0.01) compared with the long term grazing plot. In addition, long term enclosing increased the C/N ratio in each soil layer and improved C/K and C/P ratios in the surface soil compared with long term grazing. However, significant decreases of N/P and N/K ratios in the long term enclosing plot in each soil layer were observed. In conclusion, enclosing for 38 years decreased most of nutrients and reduced the nutrients’ mineralization in the surface soil especially and thus might restrict nutrients cycling in a semi-arid grassland of Inner Mongolia.
Soil microbes play important roles in biochemical processes in the plant-soil-microbe ecosystem. However, the associations between soil microbes and herbal plants mediated by plant medical metabolites are poorly understood. We investigated the linkages of soil microbial biomass (SMB) and diversity based on an analysis of the phospholipid fatty acids and medical metabolites of Artemisia annua at 18 sites (54 plots) at altitudes ranging from 420 to 1420 m altitude in the Guizhou karst terrain of China. We found that the SMB and its diversity significantly linearly increased along the altitude gradient. The artemisinin concentration (0.54-20.82 g/kg) significantly linearly increased with increasing altitude. The artemisic acid concentration and total phenolics significantly linearly decreased with increasing altitude. SMB was significantly positively correlated to artemisinin and negatively correlated with total phenolics. Our results provide basic data regarding the linkages between soil microbes and A. annua medical metabolites, and provide an insight into their interactions.
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