摘要
随着互联网+教育信息化程度的不断发展,在线学习者的行为受到普遍关注.文章依托Mooc平台的某大学数学课程学习数据和调查问卷数据,利用k-means算法对学生显性行为进行数据分类,对隐性行为数据进行信效度分析,并通过建立模糊综合评价模型,更全面地刻画学生的学习行为.分析表明:根据聚类分析划分的追赶型,上升型和稳定型学生在学习能力和学习动机等非认知上均有差异,其结果可为线上教学模式改革或个性化学习服务提供参考.
With the development of Internet and educational informatization,the behavior of online learners has been paid much attention.Based on the mathematic course learning data of Mooc platform and questionnaire,this paper uses kmeans algorithm to classify students'explicit behavior,analyzes the reliability of implicit behavior,and establishes a fuzzy comprehensive evaluation model to describe students'learning behavior more fully.The results show that catch-up,ascending and stable students have differences in non-cognitive aspects such as learning ability and learning motivation.The results can provide reference for the reform of online teaching mode or personalized learning service.
作者
金玲玲
朱厚锐
Jinlingling;Zhuhourui(College of Mathematics and Statistics,Hainan Normal University,Haikou,Hainan 571158)
出处
《科教导刊》
2023年第30期145-148,共4页
The Guide Of Science & Education
基金
海南省高等学校教育教学改革研究资助项目“深度学习视角下高等数学课程教学模式构建”(HNJG2022―40)
高等学校大学数学教学研究与发展中心教学改革项目“大学数学课程教学新模式的研究与实践”(CMC20220207)
