Document Type

Dissertation

Rights

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

Disciplines

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) 2019

Abstract

Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing mental workload. Additionally, a third feature set was formed as a combination of the two aforementioned feature sets and a number of other features believed to contribute to mental workload modeling in education. Models were trained with cross-validation due to the limited sample size. On the one hand, results show how the features of the NASA Task Load index are more expressive for a regression problem than the other two feature sets. On the other hand, results show how the newly formed feature set can lead to the development of models of the mental workload with a lower error when compared to models built with the other two feature sets and when employed for a classification task.

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