SBIR-STTR Award

This SBIR Phase 1 project proposes to demonstrate the feasibility of an advanced personalized learning system for pre-collegiate and remedial Algebra, with future extension to other scientific and mathematical disciplines. The project aims to model learning as a complex dynamics system, and to develop a user-model that incorporates student profile data with a detailed and large set of markers on usage and performance collected from an analytics engine. These markers can be derived from a student's learning and social activities, such as her interaction with various types of learning resources, participation on question and answer forums and so on. This model then guides a recommender system to select appropriate resources from a learning catalog based on the student's unique learning path. Whereas, the project uses open source recommender algorithms, the intellectual merit lies in developing a user model, identifying the specific markers that impact learning, automatically reconfiguring the learning material, and in demonstrating its viability for algebra learners. This model will be built leveraging some of the latest infrastructures in education technology including an authoring and delivery framework, a learning catalog of 10 million curated learning resources, and the latest advances in data-analytics and information filtering systems. The broader/commercial impact lies in using the proposed technology to replace textbooks and other less-advanced learning systems, not only in mathematics, but also in other STEM disciplines, making it easier, faster and more affordable for students to learn. To an extent, it will level the playing field by providing equal access to a personalized 1-on-1 type of experience, even for those students who don't have the opportunity to get high quality instruction and mentorship today. It will significantly impact students who today struggle with mathematics and STEM subjects, by providing alternate and more relevant ways for them to visualize and learn. Additionally, Zyante's technology and expanded services will enable colleges/universities to offer high-quality online courses and successfully handle larger enrollments. All this will ultimately improve national outcomes and provide a better-trained workforce in disciplines needed to drive the economic success of the US. For these reasons, it is important that the proposed technology be readily commercialized.

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.