Application of data mining in educational databases for predicting academic trends and patterns
Application of data mining in educational databases for predicting academic trends and patterns
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DOI：
10.1109/ictee.2012.6208617
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Suhem Parack;Zain Zahid;Fatima Merchant
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文献类型：
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作者：
Suhem Parack;Zain Zahid;Fatima Merchant
数据挖掘是从数据库和数据仓库中识别和提取隐藏模式和信息的过程。有各种算法和工具可用于此目的。数据挖掘有着广泛的应用，从商业到医学到工程。本文讨论了数据挖掘技术在教育中对学生进行分类和分组的应用。我们利用Apriori算法进行学生分析，这是一种流行的挖掘关联的方法，即发现项目集之间的关联。用于对学生进行分组的另一种算法是K-means聚类，它将一组观察结果分配到子集中。在学术领域，数据挖掘在发现有价值的信息方面非常有用，这些信息可用于根据学生的学业成绩对他们进行分析。我们将Apriori算法应用于包含不同学生学业记录的数据库，并尝试提取关联规则，以便根据考试成绩、学期作业成绩、出勤率和实践考试等各种参数对学生进行分析。我们还将K均值聚类应用于同一组数据，以便对学生进行分组。所实现的算法提供了一种有效的分析学生的方法，可用于教育系统。
Data mining is a process of identifying and extracting hidden patterns and information from databases and data warehouses. There are various algorithms and tools available for this purpose. Data mining has a vast range of applications ranging from business to medicine to engineering. In this paper, we discuss the application of data mining in education for student profiling and grouping. We make use of Apriori algorithm for student profiling which is one of the popular approaches for mining associations i.e. discovering co-relations among set of items. The other algorithm used, for grouping students is K-means clustering which assigns a set of observations into subsets. In the field of academics, data mining can be very useful in discovering valuable information which can be used for profiling students based on their academic record. We apply Apriori algorithm to the database containing academic records of various students and try to extract association rules in order to profile students based on various parameters like exam scores, term work grades, attendance and practical exams. We also apply K-means clustering to the same set of data in order to group the students. The implemented algorithms offer an effective way of profiling students which can be used in educational systems.