This project aims to better support student learning by adapting computer-based tutoring to individual learning phases and real-time capabilities.  The specific research goal is to explore a method for automated sensor-based learner/learning assessment in intelligent tutoring systems. For this, we apply rigorous analytics and machine learning techniques to sensor data to make models that predict, in real time, transaction-level implications related to lack of knowledge (e.g., errors) and mental workload. In particular, we study a learner’s expertise level in cognitive skill application as a key factor that varies cognitive attention switching strategies and instructional effects between individuals. We then assess to what degree expertise reversal effects are manifested in eye movement and psycho-physiological measures.