Cyber-learning with a Sensor Support
May 15, 2015
This project aims to better support student learning by adapting computer-based tutoring to individual learning phases and real-time capabilities. In this manner, computer-based tutors may be more effective in supporting robust learning.
The specific research goal is to explore a method for automated sensor-based learner/learning assessment in intelligent tutoring systems. In this project, 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.