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: 1940236
• 负责人: Neil Heffernan
• 金额: $ 70.83万
• 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: 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=1940236&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.

这是一个研究的项目，可帮助学生在辅助系统的背景下更有效地学习。助理是一个在线系统，可为学生提供帮助，并向教师提供实时评估数据。现在，助力支持已完成超过1200万数学问题的100,000名学生。该系统使用教师的意见和人工智能为试图解决数学问题的学生提供帮助。该项目将通过合并视频建议（例如Kahn Academy制作的视频建议）来增加教师和机器学习提供的帮助，该建议针对学生的需求。该实验将从三本公开许可并免费获得学校的开放性教育资源教科书中获取内容。更具体地说，研究人员将确定大量视频，这些视频集中在教科书中的数学技能上，并将提取这些视频的功能，包括语言复杂性，口语率和其他功能。这些视频和功能将由教师和机械土耳其人的可用性进行检查，然后才能向学生展示。此外，该项目将开发一套新型技术，用于精确学习，包括细粒度视频功能提取，学生功能从异质原始数据，因果建模以及公平意识和因果关系增强的增强优化的个性化的个性化建议。这项研究将提高对与个性化学习有关的基本问题的理论理解，并将实现数据驱动的学习理论实验。因果建模将使研究人员能够学习与学习有效性相关的视频功能。该项目是国家科学基金会利用数据革命大型思想活动的一部分，并由本科教育和学习研究部门共同资助。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。

项目成果

Automatic Interpretable Personalized Learning

自动可解释的个性化学习

• 发表时间: 2022
• 期刊: Learning at Scale
• 作者: Prihar, Ethan;Haim, Aaron;Sales, Adam;Heffernan, Neil
• 通讯作者: Heffernan, Neil

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

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

Exploring Common Trends in Online Educational Experiments

探索在线教育实验的共同趋势

• 发表时间: 2022
• 期刊: Proceedings of the 15th International Educational Data Mining Conference.
• 作者: Prihar, E.