This SBIR Phase I project will develop a big-data analytics-based adaptive online learning platform founded on the principles of adaptive learning. The online learning system will provide adaptive learning strategies with real-time learner analytics. The systems will integrate learner analytics from four dimensional aspects of learning multi-media, learning strategies, interactivity, and social interaction-- to deliver a personalized learning experience for science, technology, engineering, and math (STEM) students for significant improvement in the learning outcome. The adaptive learning software technology platforms with personalized learning strategies have demonstrated high completion and satisfaction rates for online students taking post-secondary courses. In this SBIR Phase I we propose to develop a unique data driven decision support interface that will result in real-time big data analytics for both individuals and large numbers of learners. The volume, velocity, variety, and veracity (the 4 Vs of big data) will be generated by the collection of data at individual schools first, with the potential to aggregate data from district, state, and even national levels. The big-data analytics of the learner trajectories through the adaptive learning platform will uncover patterns that can improve understanding of learner behavior in education.
Big data, generated by the adaptive learning systems related to learner behavior in each learning strategy, will lead to valuable insights on efficacy of the proposed methodology and development of the product in Phase II. The learner analytics will provide the basis for intelligent feedback based on the statistical evidence. The proposed method of data driven decision process for adaptive learning is based on the real- time cross-correlation statistical analysis of the predictor variables for the an individual learner. The field trials of the proposed method will be conducted in the participating high schools. Collection of data for a group of students collected during the field trials in high schools will lead to discovery of learning patterns for the clusters of learners in each learning strategy. The proposed research will then lead to the development of an analytical model for volume, velocity, variety, and veracity of data collected at school-wide level. This data then will be used for the development of decision tree and regression analysis to find correlations (knowledge discovery) that can be used for the improvement of learning enterprise (school). The data-driven feedback to students and the group analytics for teachers will provide necessary feedback mechanism for improving competency and graduation rates for STEM education in schools and colleges.
