“Data from educational technologies are commonly used to evaluate correctness of answers. However, these data offer exciting opportunities to look beyond isolated answers and study students’ decisions and trajectories within the learning environment, shedding light on students’ motivations, strategies, and attitudes. In this talk I propose a paradigm shift from learning states to engagement processes. I argue that developing data-driven models of engagement enables a vision of personalized instruction that adapts to individual students and informs theories of learning. I will demonstrate the value of process-oriented thinking by describing adaptive support for self-regulated learning, grounded in examples across multiple time-scales, from milliseconds to months. Last, I will suggest ways in which the focus on learning processes can inform analytics in other data-intensive domains. ”
This is a presentation of SLAIS Assistant Professor candidate, Ido Roll’s research.