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

这个SBIR第二阶段项目通过动态视频交付环境解决了学生参与度低的问题，并采用了一种新颖的实时算法，根据每个学生的准备程度调整教学路径。研究人员认为，学生的脱离是教师面临的最大挑战，因为25%至66%的学生被认为是脱离（泰勒和帕森斯2011）。作为一种解决方案，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之一（科莫·斯诺1986）。毕竟，当教学是个性化的学生的背景，参与和成果显着增加（科贝特2001）。通过校准视频片段（使用句法和词汇复杂性等因素），以学生的准备，该项目将保证适当的挑战性教学，以最有效地维持参与。这将对提高所有STEM学生的参与度和成果非常有用，最明显的是，将影响英语学习者（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，根据国家教育协会，ELL人口是美国公立学校学生增长最快的人口，这个项目有巨大的潜力来推动STEM熟练的劳动力。2这个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与学生的准备。这种新颖的教学方法以其K12 STEM重点和专有框架与其他自适应解决方案区分开来。凭借其领先的互动视频解决方案，该项目通过访问标记和策划的视频（40万+）和问题（250万+）的大型数据库成为可能，并提供了一个前所未有的机会，为多样化的学习范围开发自适应教学途径。本研究的目的是证明该项目可以切实提高理科学生的参与度或学习心理投资，并引发下一代适应性教学。对于第一阶段，项目团队将在教室中部署一个功能齐全的产品原型，该产品是围绕10节课的物理科学单元构建的。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.