SBIR Phase II: Increasing Student Engagement through Adaptive Instructional Video Delivery

这个SBIR第二阶段项目通过一个动态的视频传输环境解决了学生参与度低的问题，该环境具有一种新颖的实时算法，可以根据每个学生的准备程度调整教学路径。根据研究，教师面临的最大挑战是克服25-66%的学生不参与的事实。使教学适应每个学生的学习背景一直被吹捧为推动学生参与的最有效方法;当教学个性化时，参与和成果会显着增加。通过校准视频片段，以学生的准备，这个项目提供了适当的挑战性的指令，以最有效地维持参与。在第一阶段取得可喜成果的基础上，该项目旨在提高所有科学、技术、工程和数学（STEM）学生的参与度和成果，最有针对性地影响英语学习者（ELL），他们经常在STEM课程中落后，因为教学超出了他们的准备，课堂支持不足。考虑到ELL人口是美国公立学校学生中增长最快的人口，该项目具有推动STEM熟练劳动力的巨大潜力。此外，鉴于现有的强大客户群，项目小组完全有能力打入1.1亿美元的直接目标市场。到2020年，该项目团队预计将改变10%的美国中学生吸收STEM教学的方式，为采用强大的混合课堂技术开辟一条道路，这些技术可以提高参与度，改善学习，促进STEM职业发展。核心创新是一种基于研究的算法，通过所有视频片段提供适当复杂的教学。为了成功地交付适当复杂的教学视频片段，该项目（1）测量学生的复杂性准备，（2）维护一个按复杂性分类的视频片段数据库（使用语法和词汇复杂性等因素），以及（3）唯一地统一这些信息，以确定该学生的实时下一步（应该显示哪个视频片段）。其目的是通过为每个学生提供适当复杂的教学途径来提高成果。凭借领先的交互式视频解决方案，该项目通过访问标记和策划的视频（数十万）和问题（数百万）的大型数据库成为可能，并提供了前所未有的机会，为多样化的学习范围开发自适应教学途径。对于第二阶段，项目团队的目标是部署一个围绕中学物理科学单元构建的产品的功能齐全的课堂原型，并衡量对参与和学习的影响。该团队将采用刺激回忆访谈技术，大声说话访谈协议和定量使用日志来评估高度自适应视频教学的可行性，以提高学生的参与度。评估将包括形成性部分（以衡量实施和迭代改进）以及总结性部分，以评估对参与和学习成果的影响，混合方法设计包括控制和实验组的多项试点研究。该奖项反映了NSF的法定使命，并被认为值得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来支持。

This SBIR Phase II project addresses low student engagement through a dynamic video delivery environment with a novel, real-time algorithm that adjusts the instructional pathway to each student's readiness level. According to research, the greatest challenge facing teachers is overcoming the fact that between 25-66% of students are disengaged. Adapting instruction to each student's learning background has long been touted as the most effective method to drive student engagement; when instruction is individualized, engagement and outcomes increase dramatically. By calibrating video segments to student readiness, this project delivers appropriately challenging instruction to most effectively sustain engagement. Building on promising results in Phase I, the project aims to improve engagement and outcomes for all Science, Technology, Engineering and Math (STEM) students and, most pointedly, impacts English Language Learners (ELL) who often fall behind in STEM classes because instruction is beyond their readiness and in-class support is insufficient. Considering that ELL population is the fastest-growing population of public school students in the United States, this project has a significant potential to drive a STEM proficient workforce. Furthermore, given a strong, pre-existing client base, the project team is well positioned to penetrate the $110 million immediately addressable market. By 2020, the project team expects to be changing how 10% of U.S. middle schoolers absorb STEM instruction, opening a pathway to the adoption of powerful blended classroom techniques that enhance engagement, improve learning, and promote STEM careers.The core innovation is a research-founded algorithm to deliver appropriately complex instruction through all video segments. For successful delivery of appropriately complex instructional video segments, the project (1) measures student complexity readiness, (2) maintains a database of video segments categorized by complexity (using factors such as syntactic and lexical complexity), and (3) uniquely unifies that information to identify a real-time next step for that student (which video segment should be shown). The aim is to enhance outcomes by providing each student an appropriately complex instructional pathway forward. With roots in the leading interactive video solution, this project is made possible through access to a large database of tagged and curated videos (hundreds of thousands) and questions (millions) and provides an unprecedented opportunity to develop adaptive instructional pathways for a diverse learning spectrum. For Phase II, the project team's goal is to deploy a fully functioning classroom prototype of a product built around a middle school Physical Science unit and measure impact on engagement and learning. The team will employ stimulated recall interviewing techniques, speak aloud interview protocols, and quantitative usage logs to assess the feasibility of highly-adaptive video instruction to increase student engagement. Evaluation will include both formative components (to gauge implementation and iterative improvement) as well as summative components to assess impact on engagement and learning outcomes in a mixed-methods design including multiple pilot studies with control and experiment groups.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.