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

这是一个研究如何在ASSISTments系统的背景下帮助学生更有效地学习的项目。ASSISTments是一个在线系统，既为学生提供帮助，也为教师提供实时评估数据。ASSISTments现在支持10万名学生，他们完成了1200多万道数学题。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。该项目将通过纳入视频建议，如卡恩学院制作的视频建议，针对学生的需求，增加教师和机器学习提供的帮助。这项实验将从三本开放教育资源教科书中获取内容，这三本教科书都是开放许可的，免费向学校开放。更具体地说，研究人员将收集大量涉及教科书中数学技能的视频，并提取这些视频的特征，包括语言复杂性、口语速度和其他特征。在呈现给学生之前，这些视频和功能将由教师和通过机械土耳其程序进行可用性检查。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、从异质原始数据中学习学生特征、因果建模以及公平意识和因果关系优化的个性化推荐。这项研究将促进对与个性化学习相关的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了解与学习效率相关的视频特征。该项目是国家科学基金会利用数据革命大创意活动的一部分，由本科生教育部门和学习研究部门共同资助。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 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.