A case study for the application of data and process mining in intervention program assessment and improvement
A case study for the application of data and process mining in intervention program assessment and improvement
复制标题
数据和过程挖掘在干预方案评估和改进中的应用案例研究
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
H. Darabi
中科院分区：
文献类型：
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作者：
Elnaz Douzali;H. Darabi
位于芝加哥的伊利诺伊大学提供了一个干预计划，允许学生进入其荣誉学院。我们称之为荣誉计划（HP）。HP提供额外的宝贵资源，可以积极影响个别学生的教育轨迹。目前进入惠普或从惠普解雇的选择过程是传统的。管理层查看学生当前的累积平均成绩点（CGPA），而不查看学生的过去。在本文中，我们利用一个学生的教育历史，使每个学生从惠普录取或解雇更有效。我们使用数据和过程挖掘技术来研究学生CGPA的痕迹和他们的HP参与历史。我们根据学生的CGPA痕迹和HP参与历史来衡量学生的毕业率。我们发现，它是可能的，以提高学生的毕业率，如果HP录取/解雇规则的设计基于CGPA的痕迹和HP的参与历史，而不仅仅是目前的CGPA的学生。从我们的研究产生的模型创建了一个方法，为学生和管理，以评估学生是否受益于参与惠普。对于符合资格并可能从HP受益的学生，该模型还确定进入HP的最佳学期。
The University of Illinois at Chicago offers an intervention program by admitting students to its Honors College. We call this program Honors Program (HP). HP offers additional, valuable resources that can positively affect the educational trajectory of an individual student. The current selection process for admission into HP or dismissal from HP is traditional. Administration views a students’ current Cumulative Grade Point Average (CGPA) and does not look at the students’ past. In this paper, we take advantage of the educational history of a student to make the admission or dismissal of each student from HP more effective. We use data and process mining techniques to study students CGPA traces and their HP participation history. We measure the graduation rates of students based on their CGPA traces and HP participation history. We show that it is possible to improve the graduation rate of students if HP admission/dismissal rules are designed based on CGPA traces and HP participation history rather than just the current CGPA of students. The model produced from our study creates a method for both students and administration to evaluate whether or not a student benefits from participating in HP. For students who are eligible and might benefit from HP, the model also determines the optimal entering semester to HP.