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首页> 外文期刊>Information Sciences: An International Journal >Mining direct acyclic graphs to find frequent substructures - An experimental analysis on educational data
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Mining direct acyclic graphs to find frequent substructures - An experimental analysis on educational data

机译：挖掘直接非循环图以找到频繁的子结构 - 教育数据的实验分析

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摘要

The number of undergraduate students joining universities in Brazil has largely grown in the recent years. However, the number of students who actually graduate remains low. Some studies show that this is due to a phenomenon called retention, consisting of a student taking more time to graduate than the minimum required by the program, which may lead to late graduation. Hence, identifying retention patterns in an undergraduate program may assist the universities in anticipating the entrance of qualified professionals in the job market, while lessening the students' dropout rate. Undergraduate programs and grade reports can be represented by DAGs, in which each course (as a task to be accomplished by each student) is represented as a vertex, and relations between courses are represented as edges. This article proposes methods for mining DAGs using statistical analysis and Apriori-based concepts, to identify retention patterns in undergraduate programs. This work also presents an experimental analysis using real data from Fluminense Federal University, a Brazilian public higher education institution, for evaluating the methods. (C) 2019 Elsevier Inc. All rights reserved.
机译：加入巴西大学的本科生数量在近年来大部分发展。但是，实际毕业的学生人数仍然很低。有些研究表明，这是由于叫做保留的现象，由学生组成的学生比计划所需的最低限度，这可能导致毕业后期。因此，识别本科课程中的保留模式可以帮助大学预测工作市场的合格专业人士的入口，同时减少学生的辍学率。本科课程和等级报告可以由DAG代表，其中每个课程（作为每个学生的任务）表示为顶点，以及课程之间的关系表示为边缘。本文提出了使用统计分析和基于Apriori的概念进行挖掘的方法，以识别本科课程中的保留模式。这项工作还通过来自巴西公共高等教育机构的浮动联邦大学的实际数据提出了一种实验分析，用于评估方法。 （c）2019 Elsevier Inc.保留所有权利。

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