Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly - A Machine Learning Approach
Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly - A Machine Learning Approach
复制标题
发现帮助学生正确回答问题的教学资源 - 机器学习方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
David E. Pritchard
中科院分区：
文献类型：
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作者：
Giora Alexandron;Qian Zhou;David E. Pritchard
本文介绍了一项研究的初步结果，在这项研究中，我们将机器学习(ML)算法应用于物理入门课程MOOC 8.MReV的数据，以发现哪些教学资源对学生最有益。首先，我们挖掘日志以构建一个数据集，对于每个问题，表示在对该问题的每个答案之前看到的资源；其次，我们将支持向量机(SVM)应用于这些数据集，以识别资源对其特别有帮助的问题。然后，我们使用Logistic回归来识别这些资源，并量化它们的辅助价值，即在看到资源后正确回答这个问题的几率的增加。辅助值可以用来向学生推荐资源，帮助他们更快地学习。此外，了解资源的援助价值可以指导改进这些资源的努力。此外，各种主题的呈现顺序可以通过首先呈现其资源对后续主题有帮助的主题来优化。因此，这项工作的贡献有两个方向。第一种是个性化和适应性学习，第二种是教学设计。
This paper describes preliminary results from a study in which we apply machine learning (ML) algorithms to the data from the introductory physics MOOC 8.MReV to discover which of the instructional resources are most beneficial for students. First, we mine the logs to build a dataset representing, for each question, the resources seen prior to each answer to this question; Second, we apply Support Vector Machines (SVMs) to these datasets to identify questions on which the resources were particularly helpful. Then, we use logistic regression to identify these resources and quantify their assistance value, defined as the increase in the odds of answering this question correctly after seeing the resource. The assistance value can be used to recommend resources to students that will help them learn more quickly. In addition, knowing the assistance value of the resources can guide efforts to improve these resources. Furthermore the order of presentation of the various topics can be optimized by first presenting those whose resources help on later topics. Thus, the contribution of this work is in two directions. The first is Personalized and Adaptive Learning, and the second is Pedagogical Design.