课程评价中考虑误导抑制的关联规则高效提取

Efficient Association Rules Extraction by Considering Misleading Suppression in Course Evaluation

针对高校课程评价,研究数据驱动的教学管理与决策问题.由某校的课程评价指标体系,确定涵盖学生、教师、同行专家和教学督导等多维度评价数据的数据结构.对采集的调查问卷数据进行清洗和转换等预处理后,构造完成供数据挖掘的数据集.考虑误导性规则抑制,使用基于差异兴趣度的改进Apriori关联规则挖掘算法,提取评价指标间的关联规则.将发现的关系模式与使用传统Apriori关联规则挖掘算法所得结果进行比较,显示本文所用改进Apriori方法能够提高知识发现的效率和准确性,对课程建设具有更强的指导作用.

For the curriculum evaluation in colleges and universities,data-driven teaching management and decisionmaking issues are investigated in this study.First,the index system of curriculum evaluation from a school determines the data structure of multi-dimensional evaluation data covering students,teachers,peer experts,and teaching supervisors.After clean and conversion of the collected questionnaire data,a data set for data mining is constructed.Then,considering misleading suppression,we apply the improved Apriori association rule mining algorithm based on varying interest degrees to extracting the association rules between the evaluation indices.Finally,a comparison of the discovered relational patterns with the results using the traditional Apriori algorithm shows that the improved Apriori method used in this study can increase the efficiency and accuracy of knowledge discovery and has a prominent guiding role in curriculum construction.