基于一卡通数据活动熵的学生活动规律研究

Measuring Students Activities via Active Entropy Model with Smartcard Data

随着信息化技术的快速发展与广泛应用,数据挖掘在教育大数据中得到越来越多的重视。目前尚无利用一卡通数据对学生活动规律性进行量化评价的研究。本文以某高校13575名本科生为研究对象,利用其一学年的790万条校园一卡通消费记录,定义了活动熵,提出衡量学生活动规律性的时空加权活动熵算法,计算并量化每个学生的活动规律值。基于活动熵对学生进行聚类,并结合部分学生的成绩数据、图书借阅数据以及消费特征,运用Apriori关联算法分析学生群体和个体行为,挖掘出隐含的关联规则。分析结果与心理学科的研究结果有较高的一致性,对于多元评价学生及智慧校园管理具有科学的指导作用。

With the rapid developments and extensive application, information technology brings new ideas and opportunity for education. The combination of education management and data mining draws more and more attention in the education field. At present, there is little research on the quantitative analysis of the orderliness of student activities. To grasp these opportunities, this paper introduces campus active entropy by using the Smartcard Consumption Records（SCR）. A spatial-temporal weighted active entropy algorithm is proposed to quantize the orderliness of 13575 undergraduates with 7.9 million SCR in one year. Based on the active entropy, the students are clustered into group and analyzed. Meanwhile, Apriori algorithm is adopted to analyze the correlation of campus behavior and academic performance. The association rules found out are consistent with existing psychology research. The evaluation results show the effectiveness of this approach in the field of student’s orderliness measure, and active entropy is helpful for evaluating students and providing guidance on smart campus management.