SBIR Phase II: A Question of Numbers: Numeracy, Learning, and Learning about Learning

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This Small Business Innovation Research (SBIR) Phase II project aims to answer the presidential call to "create digital tutors that are as effective as personal tutors." More than any other subject, mathematical learning is cumulative, and as students fall behind their classmates, new material becomes less comprehensible and they can face an ever-widening gap to their peers. Formative assessment (FA) practices have been well established as effective in closing these gaps and informing teacher decision-making. While the influx of mobile computing devices has enormous potential to help facilitate change in education, the potential is heavily dependent on the availability of proven, research-backed software and services. This project will help close achievement gaps by providing students with adaptive, personalized instruction and also providing teachers with valuable FA techniques, data, and suggestions. More broadly, the data will yield opportunities to research and model student understanding and to analyze the learning process, enabling additional research into effective practices for the teaching and learning of mathematics. With its strong customer value propositions and innovations that will enable new forms of software-enhanced teaching and learning, this project will also create significant commercial value within the educational market. This project will create digital learning environments that go beyond current state-of-the-art systems to deliver adaptively selected instructional video segments and highly interactive problems, and do so while maintaining a flow of content that feels natural - as if the learning was occurring in the presence of an actual tutor. The ability of the system to adapt to the needs of an individual student is based upon a real-time assessment of student understanding and leverages cutting edge research from the fields of formative assessment, machine learning, artificial intelligence and big data. The ability to model student understanding and analyze the learning process will lead to the creation of new learning analytics tools and enable additional research into effective practices for the teaching and learning of mathematics. The proposed research will seek to demonstrate, in a randomized crossover trial, the effectiveness of the adaptive, online system over a control treatment. The researched solutions will also employ novel FA implementations such as collaborative review and white-boarding (via wireless communication), record and playback of teacher work, use of student sentiment, groupings of peers for collaborative work, and models of student understanding that incorporate teacher input (teacher plus software in the FA loop).