Using Cross-Task Classification for Classifying Workload Levels in Complex Learning Tasks

According to Cognitive Load Theory the type and amount of workload (WL) during learning is crucial for successful learning and should be held within an optimal range of learners' memory capacity. Therefore, we aim at developing electroencephalogram (EEG) based learning environments adapting to learners individual WL online. To achieve this goal efficient classification methods are necessary. Support Vector Machines (SVMs) can accurately classify WL using within-task classification, but within-task classification is not feasible in complex learning environments. Therefore, the present study examined cross-task classification accuracies for SVMs trained on EEG-signals, recorded while participants (N= 21) had to solve three working memory tasks. While within-task classification accuracies were high for WM tasks (average: 95% - 97 %), cross-task classification performances were not significant over chance level. Since cross-task classification is a necessary step towards developing generalized classifiers, we will discuss the benefits and drawbacks as well as possible enhancements in the course of this paper to use it as an effective approach for learning environments.