Bharathiar University, Coimbatore, India.
Department of Computer Application, St Thomas College (Autonomous), Thrissur, Kerala, India.
J Med Syst. 2019 Apr 30;43(6):162. doi: 10.1007/s10916-019-1295-4.
Data mining offers strong techniques for different sectors involving education. In the education field the research is developing rapidly increasing due to huge number of student's information which can be used to invent valuable pattern pertaining learning behavior of students. The institutions of education can utilize educational data mining to examine the performance of students which can support the institution in recognizing the student's performance. In data mining classification is a familiar technique that has been implemented widely to find the performance of students. In this study a new prediction algorithm for evaluating student's performance in academia has been developed based on both classification and clustering techniques and been ested on a real time basis with student dataset of various academic disciplines of higher educational institutions in Kerala, India. The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in prediction of academic performance of the students.
数据挖掘为涉及教育的不同领域提供了强大的技术。在教育领域，由于可以利用大量学生信息来发现与学生学习行为相关的有价值模式，因此研究发展迅速。教育机构可以利用教育数据挖掘来检查学生的表现，从而为机构提供学生的表现情况。在数据挖掘中，分类是一种常见的技术，已被广泛应用于发现学生的表现。在这项研究中，基于分类和聚类技术，针对印度喀拉拉邦高等教育机构各个学科的学生数据集，开发了一种新的预测学生学业成绩的预测算法，并实时进行了测试。结果证明，将聚类和分类方法相结合的混合算法在预测学生的学业成绩方面具有更高的准确性。
