针对高校教务管理系统中学生成绩数据连续值偏多的情况，导致无法对学生成绩有效地进行智能分析等问题，提出了基于局部择优离散技术的 C4.5改进算法，进而构建学生成绩分析模型，并采用后剪枝算法对模型进行了优化，抽取了学生成绩的分类规则。实验表明，改进后的 C4.5算法保证较高分类正确率的同时，执行效率得到了提高，有助于挖掘出学生成绩与各种因素之间的潜在联系，为教学工作改革提供决策依据和支持。%For student Performance data in teaching Management System existing consecutive values above normal, intelligent analysis of student achievement cannot be effectively. We propose the improved C4.5 algorithm based on a partial selection of the discrete technology. We also build the student achievement analysis model, which had been optimized by using after pruning algorithm. Furthermore, we also generated classification rules of student achievement. Experiments have shown that the improved decision tree algorithm ensured higher classification accuracy at the same time;the implementation efficiency had been improved. It will help to dig out the potential link between student achievements with a variety of factors and provide basis of decision making for educational reform.
展开▼
