Document Type



Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Computer Sciences

Publication Details

A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics), 2021.


This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design while an experimental group received the latter design. A performance score was extracted for evaluation. The models of efficiency compared were based upon both a unidimensional and a multidimensional measure of mental workload, which were acquired through self-reporting from the participants. These mental load measures in conjunction with the performance score contribute to the calculation of efficiency scores for each model. The aim of this study is to determine whether the novel model is able to better differentiate between the control and experimental groups based on the resulting efficiency when compared to the other models. The models were analysed and compared using various statistical tests and techniques. Empirical evidence partially supports the proposed hypothesis that parabolic model demonstrates validity, however lacks sufficient statistical evidence to suggest that the model has better sensitivity and its capacity to differentiate between the two groups.