Subjective Usability, Mental Workload Assessments and Their Impact on Objective Human Performance

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

“…In particular, this research is secondary in terms of data employed. It employs information of studies proposed in (Longo, 2018b;Longo & Orru, 2019;Longo, 2018aLongo, , 2017Longo & Dondio, 2015) for the evaluation of MWL imposed on participants who performed two types of tasks: information seeking web-based tasks and attendance to third-level classes delivered at the Technological University Dublin (a detailed description of these tasks if given in Section 3.4). The answers provided by these participants led to the creation of three different datasets evaluated simultaneously in this study.…”

“…405 valid instances were generated. Despite not being necessary in this study, the reader can obtain more information on the construction of this dataset in (Longo, 2018a(Longo, , 2017Longo & Dondio, 2015). inferences produced by the designed models and the scores produced by selected baseline instruments.…”

Design and development of sign language questionnaires based on video and web interfaces

“…Assessing the usability of interfaces in the fields of web design and interaction requires the inclusion of features related to end-users and the context of application. The perception of usability can be treated as a classification problem that employs supervised machine learning methodologies (Longo, 2017). Supervised ML classification techniques are used in usability environments and other research fields in order to produce Artificial Intelligence for Engineering Design, Analysis and Manufacturing computational, data-driven models for prediction of output features, such as mental workload measurement (Moustafa et al, 2017) or human cognitive performance in problem solving (Yoshida et al, 2014).…”

On the Reliability, Validity and Sensitivity of Three Mental Workload Assessment Techniques for the Evaluation of Instructional Designs: A Case Study in a Third-level Course

“…MWL is an important design criterion: at an early system design phase not only can a system or interface be optimised to take workload into consideration, but MWL can also guide designers in making appropriate structural changes (Xie and Salvendy, 2000). Modern technologies such as web applications have become increasingly complex (Longo, 2012;Longo and Dondio, 2015;Longo, 2017), with increments in the degree of MWL imposed on operators (Gwizdka, 2010;Longo, 2011). The assumption in design approaches is that as the difficulty of a task increases, perhaps due to interface complexity, MWL also increases and performance usually decreases (Cain, 2007).…”

“…The final MWL score is computed as a weighed average, considering the subjective rating of each attribute d i and the correspondent weights w i (averaged here, and scaled in [1..100] ∈ ℜ for comparison purposes -equation 1). For the NASA-TLX questionnaire we refer the reader to (Longo, 2017).…”