Collaborative Research: Precision Learning: Data-Driven Experimentation of Learning Theories using Internet-of-Videos

协作研究：精准学习：使用视频互联网进行数据驱动的学习理论实验

• 批准号: 1940093
• 负责人: Xintao Wu
• 金额: $ 40万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940093&HistoricalAwards=false
• 关键词: Collaborative; Research; Precision; Learning; Data

This is a project to study what works to help students learn more effectively in the context of the ASSISTments system. ASSISTments is an online system that provides both assistance to students and real time assessment data to teachers. ASSISTments now supports 100,000 students who have completed more than 12 million mathematics problems. The system uses teacher input and artificial intelligence to provide assistance to students who are attempting to solve mathematics problems. This project will increase the assistance provided by the teacher and machine learning by incorporating video suggestions, such as those produced by the Kahn academy, targeted to the needs of the student. The experimentation will take content from three Open Educational Resource textbooks that are openly licensed and free to schools.More specifically, the researchers will identify a large collection of videos that address mathematics skills in the textbooks and will extract features of these videos including language complexity, speaking rate, and other features. These videos and features will be checked by both teachers and through a Mechanical Turk process for usability before they are presented to students. Additionally, the project will develop a suite of novel technologies for precision learning including fine grained video feature extraction, student feature learning from heterogeneous raw data, causal modeling, and fairness aware and causal relationship enhanced optimized personalized recommendation. The research will advance theoretical understanding of fundamental issues related to personalized learning and will enable data-driven experimentation of learning theories. Causal modeling will enable the researchers to learn the features of video that are correlated with learning effectiveness. This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity and is co-funded by the Division of Undergraduate Education and the Division of Research on Learning.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.

这是一个项目，研究什么工作，以帮助学生更有效地学习的背景下，ASSISTments系统。ASSISTments是一个在线系统，既为学生提供帮助，又为教师提供真实的实时评估数据。ASSISTments现在支持10万名学生，他们已经完成了1200多万个数学问题。该系统使用教师输入和人工智能为试图解决数学问题的学生提供帮助。该项目将增加教师和机器学习提供的帮助，通过结合视频建议，例如Kahn学院针对学生需求制作的视频建议。该实验将从三本开放教育资源教科书中选取内容，这些教科书是公开授权的，对学校免费。更具体地说，研究人员将识别大量教科书中涉及数学技能的视频，并提取这些视频的特征，包括语言复杂度、语速和其他特征。这些视频和功能将由两位教师进行检查，并通过Mechanical Turk流程进行可用性检查，然后再向学生展示。此外，该项目还将开发一套用于精确学习的新技术，包括细粒度视频特征提取、从异构原始数据中学习学生特征、因果建模以及公平感知和因果关系增强的优化个性化推荐。该研究将推进对个性化学习相关基本问题的理论理解，并将使学习理论的数据驱动实验成为可能。因果建模将使研究人员能够学习与学习效率相关的视频特征。该项目是美国国家科学基金会利用数据革命大创意活动的一部分，由本科教育部和学习研究部共同资助。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

Enhancing personalized modeling via weighted and adversarial learning

• DOI: 10.1007/s41060-021-00263-3
• 发表时间: 2021-05
• 期刊: International Journal of Data Science and Analytics
• 影响因子: 2.4
• 作者: Wei Du;Xintao Wu

Achieving User-Side Fairness in Contextual Bandits

在上下文强盗中实现用户端公平

• DOI: 10.1007/s44230-022-00008-w
• 发表时间: 2022
• 期刊: Human-Centric Intelligent Systems
• 作者: Huang, Wen;Labille, Kevin;Wu, Xintao;Lee, Dongwon;Heffernan, Neil
• 通讯作者: Heffernan, Neil

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

PC-Fairness：衡量基于因果关系的公平性的统一框架

• 发表时间: 2019
• 期刊: NeurIPS 2019
• 作者: Wu, Yongkai;Zhang, Lu;Wu, Xintao;Tong, Hanghang
• 通讯作者: Tong, Hanghang

Transferable Contextual Bandits with Prior Observations

具有先前观察的可转移上下文强盗

• DOI: 10.1007/978-3-030-75765-6_32
• 发表时间: 2021
• 期刊: Proceedings of 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
• 作者: Labille, Kevin;Huang, Wen;Wu, Xintao
• 通讯作者: Wu, Xintao

Achieving Counterfactual Fairness for Causal Bandit

实现因果强盗的反事实公平

• DOI: 10.1609/aaai.v36i6.20653
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Huang, Wen;Zhang, Lu;Wu, Xintao
• 通讯作者: Wu, Xintao