Document Type



This item is available under a Creative Commons License for non-commercial use only


Computer Sciences


One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning both employing subjective cognitive load assessment and natural language processing. It makes use of primary, empirical and deductive methods. In details, on one hand, cognitive load assessment is performed using well-known self-reporting instruments, borrowed from Human Factors, namely the Nasa Task Load Index and the Workload Profile. On the other hand, Natural Language Processing techniques, borrowed from Artificial Intelligence, are employed to calculate semantic similarity of textual information, provided by learners after attending a typical third-level class, and the content of the class itself. Subsequently, an investigation of the relationship of cognitive load assessment and textual similarity is performed to assess learning.