• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IIAI International Congress on Advanced Applied Informatics >Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data
【24h】

Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data

机译：基于LMS日志数据的使用机器学习的高危学生的早期检测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Analytics in education has been received much attention over the past decade. It is necessary to maintain high retention rate in any institutions of higher education, therefore several attempts on the application of analytics have been done for this problem. To detect students at high drop-out risk early and intervene them effectively, utilizing the educational big data can be useful. In this paper, an automatic detection method of academically at-risk students by using log data of learning management systems is considered. Some well-known machine learning methods are used to build a predictive model of student performance evaluated by GPA. By using actual data set, we investigate an availability of the proposed method and discuss its ability to early detection of off-task behavior. The experimental results indicated that some characteristics of behavior about learning which affect the learning outcomes can be detected with only the online log data. Furthermore, comparative importance of explanatory variables obtained by the approach would help to estimate which variable affects comparatively to the learning outcome and it can be used in institutional research.
机译：在过去的十年中，教育中的分析一直备受关注。在任何高等教育机构中都必须保持较高的保留率，因此针对该问题进行了数次尝试应用分析的尝试。为了尽早发现高辍学风险的学生并对其进行有效干预，利用教育大数据可能会很有用。本文研究了一种利用学习管理系统的日志数据自动检测高学历生的方法。一些著名的机器学习方法被用来建立GPA评估的学生表现的预测模型。通过使用实际的数据集，我们调查了所提出方法的可用性，并讨论了其早期发现任务外行为的能力。实验结果表明，只有在线日志数据才能检测到一些影响学习成果的学习行为特征。此外，通过该方法获得的解释变量的相对重要性将有助于估计哪个变量相对于学习结果会产生影响，并且可以在制度研究中使用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号