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Student Academic Streaming Using Clustering Technique

机译:使用聚类技术的学生学术流媒体

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The balance of human capital supply and industry demands are crucial to sustain a competitive advantage in order to ensure stability of economic growth. Unfortunately, companies often find it hard to recruit the right people. Many ideas obviously relate to connect human capital to education because human capital is created through education. In the education sector, there is a critical need to develop an effective planning mechanism to distribute the students into the most suitable area in the industry. In order to achieve this, the student pathway needs to be planned systematically in school by identifying the student streaming based on their academic performance. In this case, students who have the same performance will be grouped in the same cluster using a data mining technique. However, the problem is that, it is difficult to identify real potential for the students, because their performance is not well monitored, and current assessment systems do not support the student academic planning activities. Besides, there is no specific technique used to group students into clusters based on their performance. This study aims to overcome the problem by grouping the students according to their performance using clustering techniques and to propose a suitable model. This study aims to overcome the problem by identifying a suitable clustering model that can be used to analysis an educational data. The data involves is student performance data. Based on the data, two clusters of students are created which is science and arts. A novelty in the method of study is the use of three clustering models and a comparison among them in order to find a suitable clustering model to be used with student academic performance data. The study was conducted in five schools in Malaysia to support students?grouping in two different academic streams, which are science and art. The result demonstrated the best model of clustering technique that is suitable for mining the educational data. Moreover, suitable streaming based on the students?performance and education policy was created from the results. It can be used to assist schools and students in determining the appropriate streaming for the students, and support for the human capital needs by the country in the future.
机译:人力资本供应与行业需求之间的平衡对于维持竞争优势以确保经济增长的稳定至关重要。不幸的是,公司通常很难招到合适的人。因为人力资本是通过教育创造的,所以许多思想显然涉及将人力资本与教育联系起来。在教育领域,迫切需要开发一种有效的计划机制,以将学生分配到该行业中最合适的领域。为了实现这一目标,需要在学校中通过根据学生的学习成绩识别学生流来系统地计划学生的学习途径。在这种情况下,使用数据挖掘技术将具有相同表现的学生分组到同一集群中。但是,问题在于,由于无法很好地监控学生的表现,并且当前的评估系统不支持学生的学术计划活动,因此很难确定学生的真正潜力。此外,没有根据学生的表现将学生分组的具体方法。这项研究旨在通过使用聚类技术根据学生的表现对他们进行分组来克服该问题,并提出一个合适的模型。这项研究旨在通过确定可用于分析教育数据的合适聚类模型来克服这一问题。数据涉及学生成绩数据。根据数据,创建了两个学生群体,即科学和艺术。学习方法的新颖之处在于使用了三种聚类模型并进行了比较,以找到适合学生学习成绩数据的聚类模型。这项研究是在马来西亚的五所学校进行的,以支持学生分为两个不同的学科领域,分别是科学和艺术。结果证明了适用于挖掘教育数据的最佳聚类技术模型。此外,结果还根据学生的表现和教育政策创建了合适的流媒体。它可用于帮助学校和学生确定适合学生的流媒体,并支持该国将来的人力资本需求。

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