Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-driven: their distinct development has been influenced by the beliefs and intuitions of individual scholars in the disciplines of Psychology and Human Factors. This work presents a novel research that aims at reversing this tendency. Specifically, it employs a selection of learning techniques, borrowed from machine learning, to induce models of mental workload from data, with no theoretical assumption or hypothesis. These models are subsequently compared against two well-known subjective measures of mental workload, namely the NASA Task Load Index and the Workload Profile. Findings show how these data-driven models are convergently valid and can explain overall perception of mental workload with a lower error.
DOI
https://doi.org/10.1007/978-3-030-14273-5_6
Recommended Citation
Moustafa K., Longo L. (2019) Analysing the Impact of Machine Learning to Model Subjective Mental Workload: A Case Study in Third-Level Education. In: Longo L., Leva M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2018. Communications in Computer and Information Science, vol 1012. Springer, Cham. DOI: 10.1007/978-3-030-14273-5_6
Publication Details
Communications in Computer and Information Science