【作者】 何伟全；

【导师】 段万春；

【作者基本信息】 昆明理工大学 ， 管理科学与工程， 2015， 博士

【摘要】 突发频发且呈加剧之势的高校学生意外伤害,撕扯着学校、家庭和社会的敏感神经。表面上看好像是偶然的独立的事故或事件,实际上是教育行业和高校管理系统内在风险的外化。学术界已开始关注,但研究视角大多局限于某些特例或个别因素,缺乏整体性和关联性。本研究以云南高校为例,结合历年学生意外伤害情况和实际管理经验,设计了包含43个因素的调查问卷。抽样调查了6类不同层次学校的8000名学生,采集到7243份有效问卷,其中12.29%学生有过意外伤害经历。在改进Apriori频繁项挖掘算法基础上,提出了“学生伤害数据频繁项挖掘算法”(AMFISI),优化了时间复杂度、存储空间复杂度和运行效率。通过对125个子项的2125个候选频繁项进行数据挖掘,出现了高发时间、多发场所、易发人群3大关联规则聚集区,获得了诸如“零点伤害”“心痛时刻”“伤心季”“交通事故多发生在春夏或秋冬交替”等时间“密码”,发现了实习见习场所、运动场、宿舍、学校周边区域、往返学校途中等学生意外伤害多发场所顺位,揭示了“熟人伤害在学生意外伤害中比例高”“母亲文化程度与大学生伤害呈负相关”“学生伤害与是否独生子女没有显著关联”等易受伤害人群特征。基于数据挖掘的结果,借鉴风险管理理论,针对性地提出了高校学生意外伤害风险源认定、监测平台建设、风险处置、风控效能评估与改进等内部控制策略,在此基础上,构建了高校学生意外伤害风险内控模型。更多还原

【Abstract】 The emergency accidental injury of college students occurs frequently and there is a tendency that the occurrence of the emergency accidental injury will increase dramatically. It really hits the sensitive nerve of colleges, students, parents, and even the education management departments. Outwardly these injury accidents are unexpected and independent. Indeed, it is the externalization of inherent risk of education sector and college management system. Academia has already paid close attention to the issue. However, the existing research perspectives are generally restricted to special cases or individual factors and lack of integrity andrelevancy.This research designed a questionnaire that contains 43 risk factors by selected Yunnan college students as samples in accordance with the situation of student accidental injury and practical management experiences in the past years. The questionnaire survey sampled 8000 students from different levels in six types of colleges. The survey collected 7243 valid questionnaires and the result showed that 12.29% of the sampled students experienced accidental injury.The research proposed the "Algorithm of Mining Frequent Item from Student Injury Data" (AMFISI) based on the improvement of Apriori frequent mining algorithm. The AMFISI optimizes the time complexity, storage space complexity and operating efficiency. It shows three association rules cluster which are high-incidence time, frequent accidents places and accident-prone groups via data mining of 2125 candidate frequent items in 125 sub-items. Then the time "code" like "midnight injury", "heartbreak moment", "grieved season" and "traffic accidents tend to occur in spring to summer alternation and autumn to winter alternation" are obtained. The sequence of places that student frequently take accidental injury such as internship and trainee places, playgrounds, dormitories, surrounding area of colleges, on the way to commute to school and so forth are detected. Furthermore, the characteristics of vulnerable groups are disclosed, for instance, "higher percentage of injured by acquaintance in student accidental injury", "negative correlation between maternal educational level and college student injury" and "no significant correlation between whether the student is the only child in the family or not and student injury" and so on.On account of the consequences of the data mining and referring the risk management theory, the research specifically presents internal control strategies about identification of college student accidental injury risk source, construction of monitoring platform, risk treatment, risk control effectiveness evaluation and improvementand so on. On this basis, the college student accidental injury risk internal control model is established.更多还原