教育数据挖掘和学习分析

Akansha Mishra, Rashi Bansal, Shailendra Narayan Singh
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引用次数: 12

摘要

近年来,数据挖掘是一种新兴的趋势,目前在不同的领域,特别是在学生教育和学习分析中得到了应用。手工分析数据和查找隐藏信息是非常困难和耗时的。为了改进教育数据挖掘,本文将使用聚类。因为我们需要即兴的性能以及所获得的模型的明确性。我们使用了84名本科生的数据,并根据他们在课程中取得的最终分数对学生进行了分组,我们使用了聚类方法。结果表明,具体模型的清晰度比一般模型好得多,模型的无歧义性也有所提高。
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Educational data mining and learning analysis
These days' data mining is an emerging trend, which is presently used in different areas especially in student educational and learning analytics. It is very hard and time consuming to analyze data and finding the hidden information manually. To improvise educational data mining, clustering will be used in the paper. As we need to improvise performance as well as unambiguousness of obtained models. We have used 84 under-graduate student data and grouped students according to their final marks they achieved in the course and this we have done by using clustering approach. The result which we get shows that the clarity of specific model is much better than the general model and the unambiguousness of the model is also increase.
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