Putting Teachers in the Driver's Seat: Using Machine Learning to Personalize Interactions with Students (DRIVER-SEAT)

这个项目将使用机器学习来个性化关于学生家庭作业的消息。该项目将应用谷歌智能回复所使用的技术，该功能使用机器学习来生成和建议类似人类的电子邮件回复，为教师提供一种快速有效的方式来回复学生的在线作业。许多国家标准的一个重要部分是数学学生需要通过写作来交流他们的想法。随着越来越多的学校将教室数字化，教师们被学生数据淹没了。教师往往无法有效和及时地审查和提供反馈。这个项目将帮助教师更有效率，同时使学生学习更有效。意志性探索性研究的对话强化基础设施--征求教师的有效行动(驾驶座)将旨在帮助教师以一种感觉个性化的方式更有效地与学生交流，同时得到计算机科学进步的支持。通过在教育环境中应用类似于谷歌智能回复的功能，Drive-Seat向教师提供自动消息建议，可用于更个性化的反馈，从而通过以高效和富有成效的方式重新整合教师，从而彻底改变数字学习。该项目将招募教师来创造驾驶座。这些教师将使用一个相当于谷歌智能回复的原型，来建立一个可信消息库，教师选择向他们的学生提供这些消息。谷歌智能回复背后的方法利用标准的顺序到顺序的机器学习技术来自动生成回复，将它们分组到100个集群(每个集群代表特定的语义意图)，并从这些集群中选择消息向用户建议。以类似的方式，序列到序列的深度学习技术被用于生成和建议消息。然而，老师们将不再通过电子邮件进行交流，而是使用这些信息来为学生的数学作业提供反馈。根据学生的表现和系统检测到的情感和行为，选择三个适当的反馈响应来启动与每个学生的互动。合作的教师将通过试验原型系统并选择反馈发送给他们的学生来帮助制作图书馆。图书馆的发展将使机器学习能够发现如何帮助教师有效地回答他们的学生。通过实施这项技术，全国数学课堂上的成绩差距将有很大的缩小潜力。然后，这种影响可能会以类似的方式扩展到科学、技术和工程课堂。这项拟议工作的变革性方面将导致教师和学生在在线学习环境中互动的方式的调整。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This is a project that will use machine learning to personalize messages about student homework. The project will apply technologies used by Google's Smart Reply, a functionality that uses machine learning to generate and suggest human-like email responses, to provide teachers a quick and effective way to respond to student online homework. An important part of many State standards is the need for math students to communicate their ideas through writing. With the number of schools digitizing their classrooms on the rise, teachers are inundated with student data. Teachers are often unable to review and provide feedback in an effective and timely manner. This project will help teachers be more efficient and at the same time cause more effective student learning. The Dialogue Reinforcement Infrastructure for Volitional Exploratory Research - Soliciting Effective Actions from Teachers (DRIVER-SEAT) will be designed to help teachers more efficiently and effectively communicate with students in a way that feels personalized, while supported by advances in computer science. By applying a feature similar to Google's Smart Reply in an educational setting, DRIVER-SEAT offer teachers suggestions of automated messages that can be used for more personalized feedback, thereby revolutionizing digital learning by re-incorporating teachers in an efficient and productive way. The project will enlist teachers to create DRIVER-SEAT. These teachers will use a prototype equivalent to Google's Smart Reply, to establish a library of trusted messages that teachers choose to provide their students. The methodology behind Google's Smart Reply utilizes standard sequence-to-sequence machine learning techniques to automatically generate responses, grouping them into 100 clusters (with each cluster representing a specific semantic intent), and selecting messages from these clusters to suggest to users. In a similar fashion, sequence-to-sequence deep learning techniques are used to generate and suggest messages. However, instead of communicating via email, teachers will be using these messages to provide feedback for their students' math homework. Based on student performance and system-detected affect and behavior, three appropriate feedback responses are selected to initiate interaction with each student. Cooperating teachers will help craft the library by piloting the prototype system and selecting feedback to send their students. Library development will enable machine learning to discover how to help teachers efficiently reply to their students. By implementing this technology, there is great potential to narrow the achievement gap in mathematics classrooms across the nation. This effect could then extend to science, technology, and engineering classrooms in a similar fashion. The transformative aspects of the proposed work will lead to adjustments in the way teachers and students interact in online learning environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.