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

让教师掌握主动权：利用机器学习实现与学生的个性化互动 (DRIVER-SEAT)

• 批准号: 1822830
• 负责人: Neil Heffernan
• 金额: $ 74.43万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822830&HistoricalAwards=false
• 关键词: Putting; Teachers; Driver; Seat; Using

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.

这个项目将使用机器学习来个性化有关学生作业的消息。该项目将应用Google Smart答复使用的技术，该功能使用机器学习来生成并建议类似人类的电子邮件回复，为老师提供一种快速有效的方法来响应学生在线家庭作业。许多州标准的重要组成部分是数学学生需要通过写作来传达自己的想法。随着学校数字化的数字数量不断增加，教师被学生数据淹没了。教师通常无法有效，及时地审查并提供反馈。该项目将帮助教师提高效率，同时引起更有效的学生学习。意志探索性研究的对话加强基础设施 - 征求教师（驾驶员座位）的有效行动将旨在帮助教师更有效，有效地与学生进行个性化的方式，同时在计算机科学方面的进步。通过在教育环境中应用类似于Google的智能答复的功能，驾驶员可以为教师提供有关自动化消息的建议，这些消息可用于更个性化的反馈，从而彻底改变了以高效和生产力的方式重新组合教师的数字学习。该项目将邀请教师创建驾驶员座位。这些老师将使用相当于Google的智能答复的原型，以建立一个可信赖的消息库，教师选择为学生提供。 Google的SMART回复背后的方法利用标准序列到序列的机器学习技术自动生成响应，将它们分组为100个集群（每个群集代表特定的语义意图），并从这些群集中选择消息以向用户建议。以类似的方式，序列到序列深度学习技术用于生成和建议信息。但是，教师将使用这些消息为学生的数学作业提供反馈，而不是通过电子邮件进行交流。根据学生的绩效和系统检测的情感和行为，选择了三个适当的反馈响应来启动与每个学生的互动。合作教师将通过试行原型系统并选择反馈来派遣学生来帮助制作图书馆。图书馆开发将使机器学习能够发现如何帮助教师有效地回复学生。通过实施这项技术，有很大的潜力可以缩小全国数学课堂上的成就差距。然后，这种效果可以以类似的方式扩展到科学，技术和工程教室。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进行互动的方式进行调整。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。

项目成果

Considerate, Unfair, or Just Fatigued? Examining Factors that Impact Teacher

体贴，不公平，还是只是疲劳？

• 发表时间: 2022
• 期刊: Proceedings of the 30th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education
• 作者: Gurung, Ashish;Botelho, Anthony;Thompson, Russell;Sales, Adam;Baral, Sami;Heffernan, Neil
• 通讯作者: Heffernan, Neil

Automatic Short Math Answer Grading via In-context Meta-learning

通过上下文元学习自动对简短数学答案进行评分

• 发表时间: 2022
• 期刊: Proceedings of the 15th International Conference on Educational Data Mining
• 作者: Zhang, Mengxue;Baral, Sami;Heffernan, Neil;Lan, Andrew
• 通讯作者: Lan, Andrew

MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: J. Shen;Michiharu Yamashita;Ethan Prihar;N. Heffernan;Xintao Wu;Dongwon Lee

The automated grading of student open responses in mathematics

学生数学开放式回答的自动评分

• DOI: 10.1145/3375462.3375523
• 发表时间: 2020
• 期刊: Tenth International Conference on Learning Analytics & Knowledge
• 作者: Erickson, John A.;Botelho, Anthony F.;McAteer, Steven;Varatharaj, Ashvini;Heffernan, Neil T.
• 通讯作者: Heffernan, Neil T.

Enhancing Auto-scoring of Student Open Responses in the Presence of Mathematical Terms and Expressions

在存在数学术语和表达式的情况下增强学生开放式回答的自动评分

• 发表时间: 2022
• 期刊: AIED 2022: The 23rd International Conference on Artificial Intelligence in Education
• 作者: Baral, Sami;Seetharaman, Karthik;Botelho, Anthony;Wang, Anzhou
• 通讯作者: Wang, Anzhou