首页> 外文会议>Adaptive Hypermedia and Adaptive Web-Based Systems >Analysing High-Level Help-Seeking Behaviour inITSs
【24h】

Analysing High-Level Help-Seeking Behaviour inITSs

机译:分析ITS中的高级求助行为

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

In this paper, we look at initial results of data mining students' help-seeking behaviour in two ITSs: SQL-Tutor and EER-Tutor. We categorised help given by these tutors into high-level (HLH) and low-level help (LLH), depending on the amount of help given. Each student was grouped into one of ten groups based on the frequency with which they used HLH. Learning curves were then plotted for each group. We asked the question, "Does a student's help-seeking behaviour (especially the frequency with which they use HLH) affect learning?" We noticed similarities between results for both tutors. Students who were very frequent users of HLH showed the lowest learning, both in learning rates and depth of knowledge. Students who were low to medium users of HLH showed the highest learning rates. Least frequent users of HLH had lower learning rates but showed higher depth of knowledge and a lower initial error rate, suggesting higher initial expertise. These initial results could suggest favouring pedagogical strategies that provide low to medium HLH to certain students.
机译:在本文中,我们考察了两种ITS中数据挖掘学生的求助行为的初步结果:SQL-Tutor和EER-Tutor。根据提供的帮助量,我们将这些教师提供的帮助分为高级(HLH)和低级帮助(LLH)。根据他们使用HLH的频率,将每个学生分为十组之一。然后为每组绘制学习曲线。我们问了一个问题:“学生的求助行为(尤其是他们使用HLH的频率)会影响学习吗?”我们注意到两位老师的结果之间存在相似之处。经常使用HLH的学生在学习率和知识深度方面学习程度最低。 HLH中低级用户的学生学习率最高。 HLH的最少用户使用率较低,但显示的知识深度较高,并且初始错误率较低,这表明初始专业知识较高。这些初步结果可能表明,偏向于为某些学生提供中低水平的HLH的教学策略。

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