使用机器学习和自然语言处理技术分析和支持学生书籍讨论的适度性
Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions
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，导致开发了无数旨在吸引学生并促进教师呈现新信息的工具和平台。IMapBook项目旨在提高小学学龄儿童的识字和阅读理解技能，向他们提供互动电子书，让他们参加有主持的书籍讨论。本研究旨在开发和说明一种基于机器学习的消息分类方法，该方法可用于自动通知讨论主持人可能需要干预，并收集有关正在进行的讨论的其他有用信息。我们的目标是预测讨论中发布的消息是否与讨论的书相关，该消息是否是一个声明，一个问题或一个答案，以及它可以被分类在哪个大类中。我们逐步丰富我们使用的特征子集，并使用标准的分类算法以及新的特征堆叠方法进行比较。我们使用标准的分类性能指标，以及贝叶斯相关的t检验表明，使用所描述的方法在讨论缓和是可行的。展望未来，我们寻求通过专注于提取在消息的强时间依赖性中发现的更多重要信息来获得更好的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
