论文标题
使用机器学习和自然语言处理技术来分析和支持学生图书讨论的适度
Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions
论文作者
论文摘要
越来越多地采用技术来增强甚至取代传统的面对面学习，这导致了旨在吸引学生并促进教师提供新信息的能力的无数工具和平台的开发。 IMAPBOOK项目旨在通过向小学时代儿童展示交互式电子书，并让他们参加主持的书籍讨论，从而提高小学时代儿童的扫盲和阅读理解能力。这项研究旨在开发和说明一种基于机器学习的消息分类方法，该方法可自动通知讨论主持人可能需要进行干预，并收集有关正在进行的讨论的其他有用信息。我们的目的是预测讨论中发布的消息是否与讨论的书相关，该消息是陈述，问题还是答案，以及可以将其分类的广泛类别。我们从逐步丰富了使用的功能子集，并使用标准分类算法以及新型功能堆叠方法对其进行了比较。我们使用标准分类性能指标以及贝叶斯相关的t检验，以表明在讨论中使用所描述的方法是可行的。向前迈进，我们试图通过专注于提取更多信息的强大时间相互依存中发现的更多重要信息来获得更好的性能。
The increasing adoption of technology to augment or even replace traditional face-to-face learning has led to the development of a myriad of tools and platforms aimed at engaging the students and facilitating the teacher's ability to present new information. The IMapBook project aims at improving the literacy and reading comprehension skills of elementary school-aged children by presenting them with interactive e-books and letting them take part in moderated book discussions. This study aims to develop and illustrate a machine learning-based approach to message classification that could be used to automatically notify the discussion moderator of a possible need for an intervention and also to collect other useful information about the ongoing discussion. We aim to predict whether a message posted in the discussion is relevant to the discussed book, whether the message is a statement, a question, or an answer, and in which broad category it can be classified. We incrementally enrich our used feature subsets and compare them using standard classification algorithms as well as the novel Feature stacking method. We use standard classification performance metrics as well as the Bayesian correlated t-test to show that the use of described methods in discussion moderation is feasible. Moving forward, we seek to attain better performance by focusing on extracting more of the significant information found in the strong temporal interdependence of the messages.
