Abstract
The transformational potential of data science and adaptive instructional systems (AISs) to radically impact the learning ecosystem is based on the following: (1) evidence suggesting that individualized instruction is generally more effective than traditional classroom instruction where fine-grain monitoring and tailored support to each individual learner is not possible (2) the capability of modern computing technologies to collect, store, access, and process fine-grain, vast and rich sets of learning data while accounting for data ownership, security, and privacy; (3) promising new advances in data science, including powerful machine learning and statistical methods for extracting useful knowledge from big educational data sets; and (4) access to affordable, powerful, and scalable distributed computing resources for processing big data, e.g., a cloud-fog-edge computing continuum. Despite these promising developments, data science and AISs have yet to exert a transformative impact on the learning ecosystem. The Learner Data Institute, a project sponsored by the US National Science Foundation, serves as the much needed catalyst for these developments to converge and transform the learning ecosystem. LDI intends a rigorous test of the hypothesis that emerging learning ecologies that incorporate AISs are capable of providing effective, engaging, equitable, and affordable individualized assistance for both learners and instructors, and that the parameters of these systems, e.g., effectiveness, can be improved over time given sufficient attention to evidence, captured as data, and expertise, provided by teams of interdisciplinary researchers like ours. |
