Error detection through mouse movement in an online adaptive learning environment

Abstract

While response time and accuracy indicate overall performance, their value in uncovering cognitive processes, underlying learning, is limited. A promising online measure, designed to track decision-making, is computer mouse tracking, where mouse attraction towards different locations may reflect the consideration of alternative response options. Using a speedy arithmetic multiple-choice game in an online adaptive learning environment, we examined whether mouse movements could reflect arithmetic difficulties when error rates are low. Results showed that mouse movements towards alternative responses in correctly answered questions mapped onto the frequency of errors made in this online learning system. This mapping was stronger for the younger children, as well as for easy arithmetic problems. On an individual level, users showed more mouse movement towards their previously made response errors than towards other alternative options. This opens the possibility of adapting feedback and instruction on an individual basis through mouse tracking.

SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories

“…De Mooij et al integrated mouse tracking in MathGarden, a large-scale online learning environment. Their findings suggest that mouse movement analysis may contribute to the diagnosis of systematic difficulties of the students [26].…”

Computerized adaptive assessment of understanding of programming concepts in primary school children

“…Future research will have to uncover whether male and female players adopt the same strategies to solve the items. This could be done, for example, by using mouse tracking technologies to study underlying thought processes (e.g., de Mooij et al, 2020).…”