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Collaborative learning for improving intellectual skills of dropout students using datamining techniques

机译：使用数据挖掘技术提高辍学学生的智力技能的协作学习

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In each year millions of students drop out without completing their educational course. In such a case, both the individual student and institution will have an effect of dropping out. The proposed research work pays significant research attention towards analysing the higher education and upper primary students to identify their behaviour, which leads them to discontinue in the early stage and stop the dropout by taking necessary action towards the dropout reason. This in turn results in the lack of skilled workspace and weaken the productive system of the country and also student dropouts are more likely to become as the recipients of unemployment subsidies. This research is more focused on the dimension reduction techniques, which involves both the feature selection and feature extraction methods. It also aims to implement better prediction in identifying dropout students and implement collaborative learning with engagement. When PCR measures, the key elements do not look at the reaction but rather at the predictors (by looking for a linear combination of the predictors that has the highest variance). It assumes that, the answer is correlated with the linear combination of the predictors with greatest variance. It is presume that, the regression plane differs when selecting the main variable in the other orthogonal direction, along the line and it does not differ. The second path is disregarded by selecting one component and not the other. Principal Component Analysis (PCA) is a method used for extracting features that use orthogonal linear projections to capture the database. This is illustrated in two phases. First phase is the development of dimension reduction using PCA to identify an accurate prediction variance of dropout students by using various ML algorithms and the second phase involves the developing of collaborative learning with engagement through social media and improves their intellectual skills by performing SVM hypothesis test.
机译：在每年的每年里，学生都会辍学而不完成他们的教育课程。在这种情况下，个人学生和机构都会产生辍学的效果。拟议的研究工作促进了分析高等教育和上小学生以确定其行为的重大研究，这导致他们在早期停止，通过对辍学原因采取必要行动来停止辍学。这反过来导致缺乏技术工作空间，削弱了该国的生产系统，并且学生辍学者更有可能成为失业补贴的接受者。该研究更专注于尺寸减少技术，这涉及特征选择和特征提取方法。它还旨在在识别辍学学生并实现与参与的协作学习来实施更好的预测。当PCR措施时，关键元素不看反应，而是在预测器中（通过寻找具有最高方差的预测器的线性组合）。它假设，答案与具有最大方差最大的预测器的线性组合相关。它是假设的，回归平面在沿着线路在其他正交方向上选择主变量时不同，并且它没有不同。通过选择一个组件而不是另一个组件来忽略第二路径。主成分分析（PCA）是一种用于提取使用正交线性投影来捕获数据库的功能的方法。这有两相说明。第一阶段是使用PCA的尺寸减少的发展，以确定通过使用各种ML算法来确定辍学学生的准确预测方差，第二阶段涉及通过社交媒体参与的协作学习的发展，并通过执行SVM假设测试来提高他们的智力技能。

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