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This investigation sought to understand whether performance in naturalistic virtual reality tasks for cognitive assessment relates to the cognitive domains that are supposed to be measured. The Shoe Closet Test (SCT) was developed based on a simple visual search task involving attention skills, in which participants have to match each pair of shoes with the colors of the compartments in a virtual shoe closet. The interaction within the virtual environment was made using the Microsoft Kinect. The measures consisted of concurrent paper-and-pencil neurocognitive tests for global cognitive functioning, executive functions, attention, psychomotor ability, and the outcomes of the SCT. The results showed that the SCT correlated with global cognitive performance as measured with the Montreal Cognitive Assessment (MoCA). The SCT explained one third of the total variance of this test and revealed good sensitivity and specificity in discriminating scores below one standard deviation in this screening tool. These findings suggest that performance of such functional tasks involves a broad range of cognitive processes that are associated with global cognitive functioning and that may be difficult to isolate through paper-and-pencil neurocognitive tests. [ABSTRACT FROM AUTHOR]

Sophisticated educational technologies are evolving rapidly, and online courses are becoming more easily available, generating interest in innovating lightweight data-driven adaptive approaches that foster responsive teaching and improving the overall learning experience. However, in most existing adaptive educational systems, the black-box modeling of learner and instructional models based on the views of a few designers or experts tended to drive the adaptation of learning content. However, different sources of uncertainty could affect these views, including how accurately the proposed adaptive educational methods actually assess student responses and the corresponding uncertainties associated with how students receive and comprehend the resulting instruction. E-learning environments contain high levels of linguistic uncertainties, whereby students can interpret and act on the same terms, words, or methods (e.g., course difficulty, length of study time, or preferred learning style) in various ways according to varying levels of motivation, pre-knowledge, cognition, and future plans. Thus, one adaptive instructional model does not fit the needs of all students. Basing the instruction model on determining learners' interactions within the learning environment in interpretable and easily read white-box models is crucial for adapting the model to students' needs and understanding how learning is realized. This paper presents a new zSlices-based type-2 fuzzy-logic-based system that can learn students' preferred knowledge delivery needs based on their characteristics and current levels of knowledge to generate an adaptive learning environment. We have evaluated the proposed system's efficiency through various large-scale, real-world experiments involving 1871 students from King Abdulaziz University. These experiments demonstrate the proposed zSlices type-2 fuzzy-logic-based system's capability for handling linguistic uncertainties to produce better performance, particularly in terms of enhanced student performance and improved success rates compared with interval type-2 fuzzy logic, type-1 fuzzy systems, adaptive, instructor-led systems, and non-adaptive systems. [ABSTRACT FROM AUTHOR]

The area of ambient-assisted living (AAL) focuses on developing new technologies, which can improve the quality of life and care provided to elderly and disabled people. In this paper, we propose a novel system based on 3-D RGB-D vision sensors and interval type-2 fuzzy-logic-based systems (IT2FLSs) employing the big bang–big crunch algorithm for the real-time automatic detection and summarization of important events and human behaviors from the large-scale data. We will present several real-world experiments, which were conducted for AAL-related behaviors with various users. It will be shown that the proposed BB-BC IT2FLSs outperform the type-1 fuzzy logic system counterparts as well as other conventional nonfuzzy methods, and the performance improves when the number of subjects increases. [ABSTRACT FROM PUBLISHER]