数据采掘在学生成绩预测中的应用
Data Mining Applications: A comparative Study for Predicting Student's performance
摘要：
知识发现和数据采掘（KDD）是一个多学科领域，重点是从数据中提取有用知识的方法，有几个有用的KDD工具来提取知识。这些知识可以用来提高教育质量。但是教育机构没有对这些数据使用任何知识发现过程方法。数据采掘可以用于教育系统的决策。决策树分类器是基于分而治之技术的用于数据探索的最广泛使用的监督学习方法之一。本文讨论了决策树在教育数据采掘中的应用。运用决策树算法对学生过去的成绩数据进行处理，生成模型，并利用该模型对学生的成绩进行预测。它有助于及早确定辍学者和需要特别关注的学生，并使教师能够提供适当的建议/咨询。
英文摘要：
Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
