Document Type



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


Computer Sciences

Publication Details

A dissertation submitted in partial fulfilment of the requirements of Dublin Institute of Technology for the degree of M.Sc. in Computing (Data Analytics)


Human Mental Workload is an intervening variable and a fundamental concept in the discipline of Ergonomics. It is deduced from variations in performance. High or low mental workload leads to hampering of performance. Mental workload in an educational setting has been extensively researched. It is applied in instructional design but it is obscure as to which factors are majorly driving mental workload in learners. This dissertation investigates the importance of the features used in the the NASA-Task Load Index mental workload assessment instrument and their impact on the performance of learners as assessed by multiple-choice tests conducted in classrooms of an MSc programme in a university. Model training is performed on these attributes using machine learning approaches including decision tree regression and linear regression. Montecarlo sampling was used in the training phase to ensure model stability. The identification of the importance of selected features is carried on using the permutation feature technique since it is adaptable and applicable across a variety of supervised learning methods. Empirical evidence emphasises the absence of more important features over the others tentatively suggesting their applicability in a multi-dimensional model.