SBIR Phase I: Positive Effects of Feedback and Intervention for Engagement in Online Learning

这个SBIR第一阶段项目将研究交流反馈循环如何影响在线学习，特别是在小组环境中。近年来，在线学习经历的爆炸性增长--许多课程未能吸引学习者，或帮助他们实现学术和专业目标--将注意力集中在参与度问题上。对在线学习的兴趣仍然很高，因为它提供了一个扩大机构和公司覆盖面的机会，但广泛可用的学习经验的成功率很低。该项目以计算社会科学的基础研究为基础，将使用专门的视频和文本聊天工具从从事同步在线学习的小组中收集数据，分析和归类交流模式，并向学生提供反馈，帮助他们在网上拥有更好、更成功的体验。为了与NSF促进科学进步和增进国家福利的使命保持一致，该项目将人工智能的新兴创新应用到大规模提供高质量学习的问题上。虽然大多数应用程序侧重于个人学习者和创建新的个性化教育途径，但该项目强调了集体学习的重要性和同行参与的力量，以帮助学习社区的所有成员共同实现他们的目标。该项目介绍了数据捕获和分析方面的几项创新，以及在学习者在线协作时向他们提供反馈。虽然许多学生使用讨论板、聊天和视频等在线协作工具，但该项目的重点是提供干预措施，以实时改变行为。在对话中，例如当人们集思广益地解决问题或共同完成任务时，某些发声模式表示参与，揭示未陈述的一致和不一致，并暴露个人偏见。使用深度学习和数据建模，可以生成关于会话优势的实时反馈，并在学习者说话时显示给他们。该项目进一步探索了机器生成的对协作期间发生的事情的洞察的影响，并根据预测模型提出了关于行为修改的建议。为了获得更细微的洞察力，这些数据与面部手势数据配对，比如点头或扬起眉毛？S。总而言之，这些创新将以前从未大规模收集的数据与独特应用于在线学习的社会科学数据模型结合在一起。该研究项目的目标是证明这样的见解对学习结果具有净积极的影响，并提高对纳入这些工具的在线学习体验的满意度。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。

This SBIR Phase 1 project will study how communications feedback loops effect online learning, especially in group settings. The explosion of online learning experiences in recent years, in which many courses fail to engage learners or help them achieve their academic and professional goals, has put focus on the problem of engagement. Interest in online learning remains high, as it presents an opportunity to broaden the reach of institutions and companies, but success rates in broadly available learning experiences are low. Built upon foundational research in computational social science, the project will use specialized video and text chat tools to collect data from groups engaged in synchronous online learning, analyze and categorize patterns of communication, and give feedback to students to help them have better, more successful experiences online. In keeping with NSF's mission to promote the progress of science and advance national welfare, the project applies emerging innovations in artificial intelligence to the problem of delivering high-quality learning at scale. While most applications focus on individual learners and creating new personalized educational pathways, this project highlights the importance of collective learning and the power of peer engagement to help all members of a learning community reach their goals together.This project introduces several innovations in data capture and analytics, as well as feedback to learners while they are collaborating online. While many students use online collaboration tools such as discussion boards, chat, and video, this project focuses on providing interventions to change behavior in real-time. During conversations, such as those happening when people brainstorm to solve a problem or work on shared assignments, certain vocal patterns indicate engagement, reveal unstated agreements and discords, and expose individual biases. Using deep learning and data modeling, real-time feedback about conversational dominance is generated and shown to learners while they are speaking. The project further explores the effect of machine-generated insights about what happened during the collaboration, and makes recommendations about behavior modifications, based on predictive models. For more nuanced insights, these data are paired with facial-gestural data, such as nodding or raising one?s eyebrows. Together, these innovations combine data that has never been collected at scale before with social science data models uniquely applied to online learning. The goal of the research project is to prove that insights like these have a net positive effect on learning outcomes and raise the level of satisfaction with online learning experiences that incorporate these tools.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.