Educational environment produces a lot of information related with many aspects, besides the traditional system, the growth of online educative systems and the called e-learning, have increased the amount of data available for its analysis. Data Mining offers different techniques for processing information in order to discover patterns that can be used for obtaining knowledge which helps improving or taking decisions about certain topics. This work presents the concepts related with, and the use of the called Educational Data Mining, specifically the technique of Association Rules, for obtaining relationships among the academic and personal factors with academic performance.
The standard Brightness Constancy Equation states spatiotemporal shift invariance of the input data along a local velocity of optical flow. In its turn, the shift invariance leads to a periodic function of a real argument. This allows application of a known test for periodicity to computation of optical flow at random locations. The approach is valid also for higher dimensions: for example, it applies to a sequence of 3D tomography images. The proposed method has a reasonably high accuracy for continuous flow and is noise tolerant. Special attention is paid to weak signal input. It is shown that a drastic reduction in the signal strength worsens the accuracy of estimates insignificantly. For a possible application to tomography, this would lead to an unprecedented diminution of harmful radiation exposure.
International audienceThis paper presents an exploring analysis of the research activity of a country using ISI web of Science Collection. We decided to focus the work on Mexican research in computer science. The aim of this text mining work is to extract the main direction in this scientific field. The focal exploring axe is: clustering. We have done two folds analysis: the first one on frequency representation of the extracted terms, and the second, much larger and difficult, on mining the document representations with the aim of finding clusters of documents, using the most used terms in the title. The cluster algorithms applied were hierarchical, kmeans, DIANA, SOM, SOTA, PAM, AGNES and model. Experiments with different number of terms and with the complete dataset were realized, but results were not satisfactory. We conclude that the best model for this type of analysis is model based, because it gives a better classification, but still it needs better performance algorithms. Results show that very few areas are developed by Mexicans
Data Mining offers great opportunities for analyzing data related with several themes, one of the most interesting is the educative environment, which has a lot information about many areas which can be improved. Scholar drop out, it's one of the biggest problems that education faces, but the great amount of factors that cause it, make it difficult for analyzing. This work presents an analysis of the elementary school drop out problem using techniques like decision tree and generation of association rules for obtaining models that allow to identify the most important family aspects that causes scholar desertion.
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