数据挖掘方法在大学通识课建设中的实践研究——以《计算机网络技术与应用》课程为例  

The Practical Research of Data Mining Method in the Construction of Liberal Arts Education in Universities—Taking “Computer Network Technology and Application” as an Example

作　　者：王行建[1] 刘欣[1] 

机构地区：[1]东北林业大学信息与计算机工程学院,黑龙江 哈尔滨

出　　处：《教育进展》2023年第3期1255-1262,共8页Advances in Education

摘　　要：为了提升通识课教育的教学方法和改进通识课培养方式,本文以某高校《计算机网络技术与应用》课程为例,在大量教学数据的基础上,采用关联规则的数据挖掘方法,对课堂测试成绩、作业完成度、任务点完成度、期末成绩等内容进行了关联性挖掘分析,获得了五条关联规则,发现了新的教学规律,文章还利用决策树算法,考虑不同学科、不同课程基础、不同人才培养方案设定等条件下,进行课程成绩预测,从而为学业预警提供了有力的支撑。In order to improve the teaching and training of Liberal Arts Education courses, this paper takes the case of “Computer Network Technology and Application”. Based on a large amount of teaching data, using the data mining method of association rules, it conducts association mining analysis on classroom test scores, homework completion, task point completion, and final grade, and obtains five association rules as well as discovers new teaching rules. The article also uses decision tree algorithms to predict course performance under the condition in different disciplines, different course foundations, and different talent cultivation schemes, providing strong support for academic warning.

关 键 词：关联规则 决策树 学业预警 通识课 

分 类 号：G63[文化科学—教育学]