TY - GEN
T1 - A Novel Parabolic Model of Instructional Efficiency Grounded on Ideal Mental Workload and Performance
AU - Longo, Luca
AU - Rajendran, Murali
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model that is grounded on ideal mental workload and performance, namely the parabolic model of instructional efficiency. A comparative empirical investigation has been constructed to demonstrate the potential of this model for instructional design evaluation. Evidence demonstrated that this model achieved a good concurrent validity with the well-known likelihood model of instructional efficiency, treated as baseline, but a better discriminant validity for the evaluation of the training and learning phases. Additionally, the inferences produced by this novel model have led to a superior information gain when compared to the baseline.
AB - Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model that is grounded on ideal mental workload and performance, namely the parabolic model of instructional efficiency. A comparative empirical investigation has been constructed to demonstrate the potential of this model for instructional design evaluation. Evidence demonstrated that this model achieved a good concurrent validity with the well-known likelihood model of instructional efficiency, treated as baseline, but a better discriminant validity for the evaluation of the training and learning phases. Additionally, the inferences produced by this novel model have led to a superior information gain when compared to the baseline.
UR - https://www.scopus.com/pages/publications/85121738589
U2 - 10.1007/978-3-030-91408-0_2
DO - 10.1007/978-3-030-91408-0_2
M3 - Conference contribution
AN - SCOPUS:85121738589
SN - 9783030914073
T3 - Communications in Computer and Information Science
BT - Human Mental Workload: Models and Applications
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Symposium on Human Mental Workload, Models and Applications, H-WORKLOAD 2021
Y2 - 24 November 2021 through 26 November 2021
ER -