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.

该项目将使用机器学习来个性化有关学生作业的消息。该项目将应用谷歌智能回复所使用的技术，该功能利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。许多州标准的一个重要部分是数学学生需要通过写作来表达他们的想法。随着教室数字化的学校数量不断增加，教师被学生数据淹没。教师往往无法有效及时地进行审查并提供反馈。该项目将帮助教师提高工作效率，同时使学生学习更加有效。自愿探索性研究的对话强化基础设施 - 征求教师的有效行动 (DRIVER-SEAT) 将旨在帮助教师以一种个性化的方式更高效、更有效地与学生沟通，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，DRIVER-SEAT 为教师提供自动消息建议，可用于更个性化的反馈，从而通过以高效且富有成效的方式重新整合教师，彻底改变数字学习。该项目将招募教师来创建 DRIVER-SEAT。这些教师将使用相当于谷歌智能回复的原型来建立教师选择向学生提供的可信消息库。 Google 的智能回复背后的方法利用标准的序列到序列机器学习技术来自动生成响应，将它们分为 100 个集群（每个集群代表一个特定的语义意图），并从这些集群中选择消息来向用户建议。以类似的方式，序列到序列深度学习技术用于生成和建议消息。然而，教师将使用这些消息来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。合作教师将通过试点原型系统并选择反馈发送给学生来帮助打造图书馆。库的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，有很大潜力缩小全国数学课堂的成绩差距。这种效应可以以类似的方式延伸到科学、技术和工程课堂。拟议工作的变革方面将导致教师和学生在在线学习环境中互动方式的调整。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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