Formalising Human Mental Workload as Non-Montonic Concept for Adaptive and Personalised Web-Design

Web Design has been evolving with Web-based systems becoming more complex and structured due to the delivery of personalised information adapted to end-users. Although information presented can be useful and well formatted, people have little mental workload available for dealing with unusable systems. Subjective mental workload assessments tools are usually adopted to measure the impact of Web-tasks upon end-users thanks to their ease of use and are aimed at supporting design practices. The Nasa Task Load Index subjective procedure has been taken as a reference technique for measuring mental workload, but it has a background in aircraft cockpits, supervisory and process control environments. We argue that the tool is not fully appropriate for dealing with Web-information tasks, characterised by a wide spectrum of contexts of use, cognitive factors and individual user differences such as skill, background, emotional state and motivation. Furthermore, in this model, inputs are averaged without considering their mutual interactions and relations. We propose to see human mental workload as non-monotonic concept and to model it via argumentation theory. The evaluation strategy includes coparisons with the NASA-TLX in terms of statistical correlation, sensitivity, diagnosticity, selectivity and reliability

Informing Instructional Design by Cognitive Load Assessment in the Classroom.

Cognitive Load Theory is an approach that considers the limitations of the information processing system of the human mind. It is a cognitivist theory that has been conceived in the context of instructional design. One of the main open problems in the literature is the lack of reliable models and technologies to assess cognitive load of learners, thus limiting the application of the theory in practice. This project was aimed at tackling this open problem through the use of a previously developed mobile, responsive web-based prototypical technology, to assess the cognitive load of students in a typical third-level classroom. It was also aimed at exploring the impact of such a technology to instructional design and the potential benefits it can bring to lecturers to improve teaching practices and optimally align their instructional materials to learners

On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload

The Evolution of Cognitive Load Theory and the Measurement of Its Intrinsic, Extraneous and Germane Loads: A Review

Cognitive Load Theory has been conceived for supporting instructional design through the use of the construct of cognitive load. This is believed to be built upon three types of load: intrinsic, extraneous and germane. Although Cognitive Load Theory and its assumptions are clear and well-known, its three types of load have been going through a continuous investigation and re-definition. Additionally, it is still not clear whether these are independent and can be added to each other towards an overall measure of load. The purpose of this research is to inform the reader about the theoretical evolution of Cognitive Load Theory as well as the measurement techniques and measures emerged for its cognitive load types. It also synthesises the main critiques of scholars and the scientific value of the theory from a rationalist and structuralist perspective

A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload

In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the human-computer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study is aimed at comparing the classification accuracy of a set of induced models, from an existing dataset, to the mental workload indexes generated by well-known subjective mental workload assessment techniques - namely the Nasa Task Load Index and the Workload profile instruments

A defeasible reasoning framework for human mental workload representation and assessment

Human mental workload (MWL) has gained importance in the last few decades as an important design concept. It is a multifaceted complex construct mainly applied in cognitive sciences and has been defined in many different ways. Although measuring MWL has potential advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. This research contributes to the body of knowledge by providing an extensible framework built upon defeasible reasoning, and implemented with argumentation theory (AT), in which MWL can be better defined, measured, analysed, explained and applied in different human–computer interactive contexts. User studies have demonstrated how a particular instance of this framework outperformed state-of-the-art subjective MWL assessment techniques in terms of sensitivity, diagnosticity and validity. This in turn encourages further application of defeasible AT for enhancing the representation of MWL and improving the quality of its assessment

DIT Teaching Fellowships Reports 2015-2016.

https://arrow.tudublin.ie/tfreports/1005/thumbnail.jp

Evaluating the Effectiveness of the Gestalt Principles of Perceptual Observation for Virtual Reality User Interface Design

There is a lot of interest and excitement surrounding the areas of Virtual Reality and Head-Mounted Displays with the recent releases of devices such as the Oculus Rift, Sony PSVR and the HTC Vive. While much of the focus for these devices has been related to sectors of the entertainment industries, namely the cinema and video game industries, there are many more practical applications for these technologies, with potential benefits in educational, training/simulation, therapeutic and modelling/design software. Developing a set of reliable guidelines for Virtual Reality User Interface Design could play a crucial role in whether the medium successfully integrates into the mass market. The Gestalt Principles of Perceptual Organisation offer a psychological explanation of human perception, with particular reference to pattern recognition and how we subconsciously group entities together. There are seven Principles of Perceptual Organisation, nearly all of which are currently widely used in User Interface design, offering designers guidelines on what the size, shape, position and colour the different components of an interface should be. This study presents an analysis on the effects that the employment of the Gestalt Principles has on the usability and mental workloads of Virtual Reality applications

Discover Influential Mental Workload Attributes Impacting Learners Performance in Third-Level Education

Human Mental Workload is an intervening variable and a fundamental concept in the discipline of Ergonomics. It is deduced from variations in performance. High or low mental workload leads to hampering of performance. Mental workload in an educational setting has been extensively researched. It is applied in instructional design but it is obscure as to which factors are majorly driving mental workload in learners. This dissertation investigates the importance of the features used in the the NASA-Task Load Index mental workload assessment instrument and their impact on the performance of learners as assessed by multiple-choice tests conducted in classrooms of an MSc programme in a university. Model training is performed on these attributes using machine learning approaches including decision tree regression and linear regression. Montecarlo sampling was used in the training phase to ensure model stability. The identification of the importance of selected features is carried on using the permutation feature technique since it is adaptable and applicable across a variety of supervised learning methods. Empirical evidence emphasises the absence of more important features over the others tentatively suggesting their applicability in a multi-dimensional model

An Application of Natural Language Processing for Triangulation of Cognitive Load Assessments in Third Level Education

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\u27s speech supported with slides