SBIR Phase II: Developing a Structured Student-guided Personalized Learning System for Mathematics

SBIR第二阶段项目为基于网络的学习材料开发新的自适应技术，并使用这些技术创建代数和统计内容。大学教科书和家庭作业正在被基于网络的学习材料所取代，这些学习材料具有高度的互动性，包括动画、学习问题和自动生成的自动评分的家庭作业/测验练习。该项目开发这些练习来调整（适应）学习者的表现以及学习者的偏好，提供一种新的适应性结构形式，最大限度地提高学习效率，同时减少学生的焦虑，与许多其他提出的适应性技术相比。这个项目为代数和统计学这两个让许多大学生头疼的关键学科创造了新的内容。结果将是年轻大学生在代数和统计课程上取得更大的成功（而不是失败），从而导致更多的STEM（科学、技术、工程和数学）领域的毕业生，这对国家的生产力和竞争力做出了巨大贡献。这些技术可以应用于许多其他STEM和非STEM科目，也可以用于大学课程以外的学习。SBIR第二阶段项目为基于网络的学习材料开发了新的自适应技术，称为结构化学生引导自适应（SSGA）技术。与最近的一些适应性商业产品相比，SSGA保留了教师通过材料保持结构化路径的能力，这对于保持学生与讲座/讨论会议同步，使学生能够与同学一起学习等等至关重要。适应性有几种形式，包括在正确完成早期问题的基础上自动连续生成更难的问题，并仔细整合解释和原始材料，以确保学生学习基本概念。此外，这种适应性在一定程度上是由学生指导的，学生可以选择从简单或困难的问题开始，也可以根据表现、选定的材料等自动生成自测。与其他产品相比，学生引导的适应性让学生对自己的学习有适当的控制，产生一种赋权感，减少可能抑制学习的焦虑。该项目构建了支持SSGA材料创建所需的创作平台，建立在先前开发的交互式web材料创作框架之上。该项目还为大学代数和统计课程创建了新材料，通过用SSGA材料取代传统的教科书/作业，可以降低这些课程的高流失率。

This SBIR Phase II project develops novel adaptive techniques for web-based learning materials, and creates Algebra and Statistics content using those techniques. College textbooks and homework are being replaced with web-based learning materials that are highly-interactive, involving animations, learning questions, and auto-generated auto-graded homework/quiz exercises. The project develops those exercises to adjust (adapt) to the learner's performance as well as to the learner's preferences, providing a novel structured form of adaptivity that maximizes learning efficiency while reducing student anxiety, in contrast to many other proposed adaptive techniques. The project creates new content for the topics of Algebra and for Statistics, two critical subjects with which many college students struggle. The result will be greater success (and less failure) in Algebra and Statistics courses by young college students, leading to more graduates in STEM (science, technology, engineering, and math) fields, which contribute greatly to the nation's productivity and competitiveness. The techniques can be applied to many other STEM and non-STEM subjects, and for learning beyond college courses too. This SBIR Phase II project develops novel adaptive techniques for web-based learning materials, called structured student-guided adaptive (SSGA) techniques. In contrast to some recent adaptive commercial products, SSGA preserves the ability of an instructor to maintain a structured path through the material, which is critical for keeping students in synch with lecture/discussion sessions, for enabling students to study with classmates, and more. Adaptivity comes in several forms, including auto-generating successively-harder problems based on correct completion of earlier problems, with explanations and source material carefully integrated to ensure students learn underlying concepts. Also, the adaptivity is in part guided by the student, who can choose to start with simpler or harder problems, or can auto-generate self-quizzes based on performance, selected material, and more. In contrast with other products, student-guided adaptivity gives students appropriate control over their learning, yielding a sense of empowerment and reducing anxiety that can inhibit learning. The project builds the authoring platform necessary to support SSGA material creation, building upon a previously-developed authoring framework for interactive web material. The project also creates new material for college algebra and statistics courses, whose high attrition rates can be reduced by replacing traditional textbooks/homework with SSGA material.