Adaptive Cyber-learning with a Sensor Support (CHI WIP 2014, QoLT Center)
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.
In a Work-In-Progress study, I have investigated differences in the approach patterns that novice and expert learners use to manage their visual attention (Figure 8). We collected data from 21 novices and 20 experts during geometry problem-solving tasks. Initial results showed transactional and perceptual correlations between geometry expertise and task complexity. The results further suggested that eye tracking could reveal distinguishable patterns in perceptual and cognitive activities between expert and novice learners, and can help identify quantifiable metrics for future learner modeling. I have obtained research grants from a Carnegie Mellon institutional fund called ProSEED (“Sensor-based Assessment of Student In-situ States in Attention and Cognition during Computer-based Geometry Problem-Solving Tasks”), and I am preparing to resubmit an Institute of Education Science (IES) proposal (“Supporting Computer-based Geometry Tutoring through Learners’ Eye Tracking Patterns and Real-time Cognitive Load”) about this project.
(a) Experimental setup during geometry problem-solving tasks.
(b) Solution steps in a high-complex problem in which a series of theorems should be used in correct order. Selective switching of visual attention is crucial for successful problem solving.
(C) Expert-novice differences in visual attention management in terms of interaction with task complexity and expertise level.
Figure 8. Understanding Expert-Novice Differences in Geometry Problem-Solving Tasks: A Sensor-based Approach.