东北大学学报(社会科学版) ›› 2014, Vol. 16 ›› Issue (3): 309-314.DOI: -

• 教育研究 • 上一篇    下一篇

基于教育数据挖掘的大学生学习成果分析

舒忠梅,屈琼斐   

  1. (中山大学教育学院,广东广州510275)
  • 收稿日期:2013-12-13 修回日期:2013-12-13 出版日期:2014-05-25 发布日期:2014-12-30
  • 通讯作者: 舒忠梅等
  • 作者简介:舒忠梅(1974-),女,湖北荆门人,中山大学讲师,工学博士,主要从事教育数据挖掘、高等教育管理研究;屈琼斐(1973-),女,浙江台州人,中山大学副研究员,法学博士,主要从事高等教育、教育社会学研究。
  • 基金资助:
    国家自然科学基金资助项目(61202345);全国教育科学“十二五”规划教育部重点课题资助项目(DIA130303)。

An Analysis of University Students Learning Outcome Based on Educational Data Mining

SHU Zhongmei, QU Qiongfei   

  1. (Education School, Sun Yatsen University, Guangzhou 510275, China)
  • Received:2013-12-13 Revised:2013-12-13 Online:2014-05-25 Published:2014-12-30
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摘要: 基于经典的大学生发展理论,通过实证分析,从大学生学习经历角度,采用逐步回归和神经网络等教育数据挖掘技术,在学生个体和学校两大层面构建大学生学习成果的预测和评价模型,对大学生学习成果及其影响因素进行分析。分析发现,在学生个体方面,学生学习投入是影响学习成果的最重要因素;在学校方面,学习资源和校园文化是影响学习成果的重要因素;在学校因素和学生因素融合方面,在校学习满意度对学习成果具有一定的影响。另外,学生在其学习经历中,与学校系统内部的学术系统和社交系统相融合则可取得较好的学习成果。

关键词: 学生学习成果, 教育数据挖掘, 学习分析, 学习经历, 学生发展

Abstract: Based on the classic development theory and from the perspective of learning experience, regression and neural network analysis are combined to empirically explore the affecting mechanism of university students learning outcome. Taking account of such factors as individual students and universities, a predication model of student learning outcome is constructed. The findings indicate that student engagement is the most important factor for learning outcome when it comes to individual students, learning resources and campus culture are two major factors for learning outcome at the university level, and learning satisfaction exerts a certain effect on learning outcome in terms of both students and universities. In addition, an integration of academic system and social system could contribute to a better learning outcome for university students.

Key words: student learning outcome, educational data mining, learning analysis, learning experience, student development

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