Measuring and Predicting Students' Effort: A Study on the Feasibility of Cognitive Load Measures to Real-Life Scenarios

Students' effort is often considered to be a key element in the learning process. As such, it can be a relevant element to integrate in learning analytics tools, such as dashboards, intelligent tutoring systems, adaptive hypermedia systems, and recommendation systems. A prerequisite to do so is to measure and predict it from learning data, which poses some challenges. We propose to rely on the cognitive load theory to infer the students' perceived effort using subjective, performance, behavioral and physiological data collected from 120 seventh grade students. We also estimate students' effort in future tasks using the data from previous tasks. Our results show a high relevance of interaction data to measure students' effort, especially when compared to physiological data. Moreover, we also found that using the data collected on previous tasks allows us to achieve slightly higher accuracy values than the data collected during the task execution. Finally, this approach also allowed us to predict students' perceived effort in future tasks, which, to the best of our knowledge, is one of the first attempts towards this goal.