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Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities in third-level sessions under different topics and instructional designs. This research employs secondary, empirical and inductive methods to investigate Cognitive Load theory, instructional designs, Mental Workload foundations and measures and Natural Language Process Techniques. Then, NASA-TLX, Workload Profile and Relative Frequency Ratios are calculated. Finally, the relationship between NASA-TLX and Workload Profile and Relative Frequency Ratios is analysed using parametric and non-parametric statistical techniques. Results show that the relationship between Mental Workload and Relative Frequency Ratios of keywords, is only medium correlated, or not correlated at all. Furthermore, it has been found out that instructional designs based on the process of hearing and seeing, and the interaction between participants, can overcome other approaches such as those that make use of videos supported with images and text, or of a lecturer's speech supported with slides.
Contreras, L. A. (2018) An application of Natural Language Processing for triangulation of Cognitive Load Assessments in Third Level Education. Masters dissertation, DIT, 2018.