社会网络分析在信息学数学课程关系中的应用

K. Bussaban, Asekha Khantavchai
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引用次数: 0

摘要

本研究旨在探讨资讯数学课程与大学生课程成绩的关系。使用社会网络分析和皮尔逊积矩相关系数来确定哪些课程是重要的和影响预测因子。收集的数据来自2010-2017学年毕业的理科生提交的91份在线报告样本。研究结果表明,线性代数和离散数学的加权度中心性得分最高,数学信息技术的中间度中心性得分最高,信息学数学英语对整个课程的影响最高。
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Applying Social Network Analysis on Courses relationship in Informatics Mathematics Curriculum
This study investigates the relationship the courses achievement of undergraduate students in Informatics Mathematics curriculum. Social Network Analysis and Pearson product Moment Correlation Coefficient are used to determine which courses are significant important and influence predictors. Data collected is from the sample of 91 online report submitted by science graduated students who have graduated during the academic year 2010-2017. The results of the study indicate that Linear Algebra and Discrete Mathematics are the highest score of weighted degree centrality as Mathematics information technology is the highest score of betweenness centrality and English for informatics Mathematics influence over the whole courses.
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