首页> 外文期刊>SIGCSE bulletin >Estimating Programming Knowledge with Bayesian Knowledge Tracing
【24h】

Estimating Programming Knowledge with Bayesian Knowledge Tracing

机译:用贝叶斯知识跟踪估计编程知识

获取原文
获取原文并翻译 | 示例

摘要

In this paper we present a concept for three-phase measuring method, which can be used to obtain data on student learning. The focus of this method lies on the technical aspects of learning programming, answering questions like which programming constructs students applied and how large portion of the students understood the concepts of programming language.rnThe model is based on three consecutive measurements, which are used to observe the student errors, applied programming structures and an application of a Bayesian learning model to determine the programming knowledge. So far the model has produced results which confirm prior knowledge on student learning, indicating that the concept is feasible for further development. Despite of the early development phase of the method, it offers a straightforward way for teacher to assess the course contents and student performance.
机译:在本文中,我们提出了一种三相测量方法的概念,该方法可用于获取有关学生学习的数据。这种方法的重点在于学习编程的技术方面,回答诸如学生采用哪种编程构成以及有多少学生了解编程语言的概念之类的问题。该模型基于三个连续的测量值,用于观察学生错误,应用的编程结构以及贝叶斯学习模型在确定编程知识方面的应用。到目前为止,该模型产生的结果证实了有关学生学习的先验知识,表明该概念对于进一步发展是可行的。尽管该方法处于早期开发阶段,但它为教师提供了一种评估课程内容和学生表现的直接方法。

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