SBIR Phase II: Actionable Learning Analytics for the Classroom

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is the development of practical methods of collecting, analyzing and utilizing student online learning activity data to improve teaching and learning. COVID-19 caused an accelerated adoption of educational technologies. The new normal of education may be much more infused with technology. This project provides the analytic tools for educators to get the data insights necessary to design effective digital learning experiences and fully support students. Data analytics provide immense value to many online commercial organizations and businesses. These commercial analytics are used to better understand customers, promote desired customer behavior, predict future interactions and support many organizational decisions. This project seeks to make these insights available to educators and students. There are over three million teachers in the United States who currently have no practical way of reaping the value of analytics to gain insight into the online experiences that they are designing for their own students. The objective of this project is to provide teachers with the analytics and insights necessary to hold students accountable, provide instructional support, and design effective digital lessons. This Small Business Innovation Research (SBIR) Phase II project will explore ways to create automated processes that extract key data from student online learning activity records. In turn, the project seeks to translate this data into useful analytics that will offer teachers insights into students’ online learning experience. This project may improve the collection and display of targeted online learning analytics that provide educators with critical information about how students interact with online content. These advances will be coupled with doctoral research studies at East Stroudsburg University to collect and analyze student online learning data in order to create automated “Key Learning Indicators” and “Learning Experience Scores.” This project will work toward creating an industry standard for how student online learning data should be ethically collected, analyzed, and presented to educators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.