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Faculty performance evaluation based on prediction in distributed data mining

机译：基于分布式数据挖掘预测的教师性能评估

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摘要

Education is a very large area to study. In the real world, predicting the performance of the faculties is a very much challenging task. We can find different parameters used in evaluating faculty performance to be used with different classification algorithms that predicts the faculty performance. After investigation we can predict the performance of the faculty and then it becomes feasible for taking necessary action to improve it. It can be proved helpful for academic institutes. This topic provides a better solution for the problem of predicting and analyzing faculty performance in distributed data mining. With the use of distributed data mining we can fetch data from the different sources then we can apply classification algorithm on it. Distributed data mining provides an efficient path for data storing and thus data can be accessed quickly and easily. By classification we can get better efficiency and accuracy in measuring the performance of faculty. And we can build the performance prediction model based on faculty's skills, punctuality and performance in various tests. This classification technique is tested in WEKA tool to get accurate results.
机译：教育是一个非常大的学习区域。在现实世界中，预测院系的表现是一个非常具有挑战性的任务。我们可以找到用于评估要与预测教师性能的不同分类算法一起使用的教师性能的不同参数。调查后，我们可以预测教师的表现，然后对采取必要的行动来改善它变得可行。可以证明有助于学术研究所。本主题为在分布式数据挖掘中预测和分析教师性能的问题提供了更好的解决方案。随着分布式数据挖掘的使用，我们可以从不同的来源获取数据，然后我们可以应用分类算法。分布式数据挖掘为数据存储提供了有效路径，因此可以快速且容易地访问数据。通过分类，我们可以获得更好的效率和准确性来衡量教师的表现。我们可以根据教师的技能，准时和性能在各种测试中构建性能预测模型。在Weka工具中测试了该分类技术，以获得准确的结果。

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