Using touchscreen interaction data to predict cognitive workload

Although a great number of today’s learning applications run on devices with an interactive screen, the high-resolution interaction data which these devices provide have not been used for workload-adaptive systems yet. This paper aims at exploring the potential of using touch sensor data to predict user states in learning scenarios. For this purpose, we collected touch interaction patterns of children solving math tasks on a multi-touch device. 30 fourth-grade students from a primary school participated in the study. Based on these data, we investigate how machine learning methods can be applied to predict cognitive workload associated with tasks of varying difficulty. Our results show that interaction patterns from a touchscreen can be used to significantly improve automatic prediction of high levels of cognitive workload (average classification accuracy of 90.67% between the easiest and most difficult tasks). Analyzing an extensive set of features, we discuss which characteristics are most likely to be of high value for future implementations. We furthermore elaborate on design choices made for the used multiple choice interface and discuss critical factors that should be considered for future touch-based learning interfaces.