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

该SBIR第二阶段项目通过动态视频交付环境解决学生参与度低的问题，并采用一种新颖的实时算法来调整教学路径以适应每个学生的准备程度。研究人员认为，不敬业的学生是教师面临的最大挑战，25%至66%的学生被认为是不敬业的(Taylor和Parsons，2011)。作为一种解决方案，调整教学以适应每个学生的学习背景，长期以来一直被吹捧为推动学生参与的最有效方法之一(Como；&&Snow 1986)。毕竟，当教学根据学生的背景进行个性化时，参与度和结果会显著增加(Corbett 2001)。通过将视频片段(使用句法和词汇复杂性等因素)调整到学生的准备程度，该项目将保证适当的具有挑战性的教学，以最有效地维持参与。这将非常有助于提高所有STEM学生的参与度和成果，最有针对性的是，将影响英语学习者(ELL)，他们经常在STEM课堂上落后，因为教学超出了他们的准备，课堂上的支持不足。考虑到，根据美国国家教育协会的数据，ELL人口是美国公立学校学生中增长最快的人口，该项目具有巨大的潜力来推动STEM熟练的劳动力。该项目将产生一个原型教学解决方案，使用算法来匹配视频片段的复杂性和学生的准备。这种新颖的、教学上合理的方法凭借其K12 STEM重点和专有框架使其有别于其他自适应解决方案。该项目植根于领先的互动视频解决方案，通过访问标记和整理的视频(400,000+)和问题(250,000+)的大型数据库而成为可能，并提供了一个前所未有的机会，为不同的学习领域开发自适应的教学途径。本研究的目的是证明这一项目可以有效地增加理科学生的学习投入或心理投入，并引发下一代适应性教学。在第一阶段，项目团队将在教室里部署一个全功能的产品原型，该产品围绕着一个10节课的物理科学单元而构建。该团队将使用模拟回忆面试技术、大声说话面试协议和量化使用日志来评估高度适应性视频教学的可行性，以提高学生的参与度。

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. Researchers consider disengaged pupils as the largest challenge facing teachers, as between 25% and 66% of students are considered to be disengaged (Taylor and Parsons 2011). As a solution, adapting instruction to each student's learning background has long been touted as one of the most effective methods to drive student engagement (Como & Snow 1986). After all, when instruction is individualized to the background of the student, engagement and outcomes increase dramatically (Corbett 2001). By calibrating video segments (using factors such as syntactic and lexical complexity) to the student's readiness, this project will guarantee appropriately challenging instruction to most effectively sustain engagement. This will be extremely useful for improving engagement and outcomes for all STEM students and, most pointedly, will impact 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, according to the National Education Association, ELL population is the fastest-growing population of public school students in the U.S., this project has a huge potential to drive a STEM proficient workforce.This project will result in a prototype instructional solution that uses algorithms to match video segment complexity with student readiness. This novel, pedagogically-sound approach sets itself apart from other adaptive solutions with its K12 STEM focus and proprietary frameworks. With its roots in the leading interactive video solution, this project is made possible through access to a large database of tagged and curated videos (400,000+) and questions (2.5 million+) and provides an unprecedented opportunity to develop adaptive instructional pathways for a diverse learning spectrum. The objective of this research is to prove this project can feasibly increase engagement, or psychological investment in learning, of science students and spark the next generation of adaptive instruction. For Phase I, the project team will deploy in classrooms a fully functioning prototype of a product built around a 10 lesson Physical Science unit. The team will employ stimulated recall interviewing technique, speak aloud interview protocols, and quantitative usage logs to assess the feasibility of highly-adaptive video instruction to increase student engagement.