SBIR Phase I: The Smart Study Recommendations Engine

SBIR 第一阶段项目的重点是持续开发一个软件平台，供学生用于同伴作业和学习帮助。放学后的帮助历来是由导师和家庭作业热线提供的，但事实证明，这些途径不足以缩小学生离开课堂后的学习差距。拟议的技术预计将进一步使家庭作业和学习帮助民主化。通过点对点的方式，所提出的技术可以从根本上降低学生按照自己的时间和节奏进行个性化作业和学习帮助的成本，尤其是那些来自经济或社会背景更困难的学生。该项目还将探索使用创新的商业模式来优化商业可行性，影响广泛的学生，无论他们的背景如何。预计该技术成熟后将成为一种可扩展的低成本选择，帮助提高国内和全球学生的学习成果。该项目的关键智力优势在于开发智能学习推荐引擎。这包括从学生上传的课堂笔记中收集数据，对其进行分析以得出预测见解，并根据学生用户的学习风格、学习能力和学习差距自动将笔记或广泛的同行评审学习材料直接发送给学生用户，而无需他们执行搜索。它还将使他们能够与能够提供额外支持的同行建立联系。因此，该项目旨在根据学生档案提供个性化的学习播放列表，并将其与可以根据需要提供更深入说明的同伴导师配对。要证明的关键成果是，在能够根据需要提供清晰度和辅导的同伴的帮助下，五分之一的学生对自动提供的数学笔记表现出良好的态度。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

This SBIR Phase I project is focused on continued development of a software platform that is used by students for peer to peer homework and studying help. After school help has historically been delivered by tutors and homework hotlines but those avenues have been proven to be inadequate in closing learning gaps for students after they exit the classroom. The proposed technology is expected to further democratize homework and studying help. Being peer to peer, the proposed technology can radically shrink the cost of personalized homework and studying help for students at their own time and pace - especially the ones from economically or socially more challenged backgrounds. The project will also explore use of an innovative business model to optimize commercial viability with impacting a broad range of students irrespective of their backgrounds. It is expected that when fully developed, the technology would emerge as a scalable low-cost option to help improve student learning outcomes both nationally and globally.The key intellectual merit of this project is in the development of a Smart Study Recommendations Engine. This involves harvesting the data from the class notes uploaded by students, analyze it to surface predictive insights and automatically deliver the notes or wide-ranging peer-reviewed study materials directly to the student users based on their learning styles, learning abilities, and learning gaps, without requiring them to perform a search. It will also provide them with an ability to connect them with peers who can provide additional support. Thus, this project seeks to provide personalized study playlists based on a student profile and pair them with a peer mentor who can provide deeper clarifications as required. The key outcome to be demonstrated is that 1 in 5 students engages favorably with the auto delivered math notes, helped by a peer who can provide clarity and tutoring as needed.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.