Dataset Information

Self-reported data for mental workload modelling in human-computer interaction and third-level education.

ABSTRACT: Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets of tasks performed by participants under different conditions is reported. This data was gathered from different sets of questionnaires answered by these participants. These questionnaires were aimed at assessing the features believed by domain experts to influence overall mental workload. In total, 872 records are reported, each representing the answers given by a user after performing a task. On the one hand, collected data might support machine learning researchers interested in using predictive analytics for the assessment of mental workload. On the other hand, data, if exploited by a set of rules/arguments (as in [3]), may serve as knowledge-bases for researchers in the field of knowledge-based systems and automated reasoning. Lastly, data might serve as a source of information for mental workload designers interested in investigating the features reported here for mental workload modelling. This article was co-submitted from a research journal "An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems" [3]. The reader is referred to it for the interpretation of the data.

SUBMITTER: Rizzo L

PROVIDER: S-EPMC7160424 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Self-reported data for mental workload modelling in human-computer interaction and third-level education.

Rizzo Lucas L Longo Luca L

Data in brief 20200319

Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets of tasks performed by participants under different conditions is reported. This data was gathered from different sets of questionnaires answered by t ...[more]

PMID: 32322612

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Project description:Education programs have been central to reestablishing social norms, rebuilding public educational institutions, and addressing public attitudes toward mental illness in Liberia following a protracted civil war and the Ebola epidemic. The aim of this study was to determine if a program combining an understanding of neuroscience with mental health literacy content could increase teachers' awareness of students' mental health issues and produce changes in teacher attitudes and classroom practices. A tiered Training-of-Trainers approach was employed. The first workshop trained 24 Liberian secondary science teachers in the neurobiology of learning, memory, emotions, stress and adolescent brain development. A Leadership Team formed from eight of the Tier I participants then adapted the curriculum, added in more mental health literacy content and led four Tier II workshops and four follow-up Refresher sessions. Participants completed a neuroscience knowledge test and surveys assessing stigma, general perceptions of people with mental illness, and burnout. A subset of Tier II teachers participated in a structured interview at the Refresher time point. Teachers in both tiers acquired basic neuroscience knowledge. Tier I, but not Tier II teachers significantly improved their surveyed attitudes toward people with mental illness. No changes were found in overall teacher burnout. Despite these survey results, the interviewed Tier II teachers self-reported behavioral changes in how they approached their teaching and students in their classrooms. Interviewees described how they now understood social and emotional challenges students might be experiencing and recognized abnormal behaviors as having a biopsychosocial basis. Teachers reported reduced use of verbal and corporal punishment and increased positive rewards systems, such as social and emotional support for students through building relationships. Refresher discussions concurred with the interviewees. In contrast to previous teacher mental health literacy programs which did not bring about a change in helping behaviors, this pilot program may have been successful in changing teacher knowledge and self-reported behaviors, improving teacher-student relationships and decreasing harsh discipline. The combination of basic neuroscience concepts with training on how to recognize mental health issues and refer students should be investigated further as a strategy to promote teacher mental health literacy.

Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Project description:OBJECTIVE:The burden of musculoskeletal disease remains high in low-income countries, with a high rate of pediatric disease. Efforts continue for orthopedic education, but there is little guidance on local needs and desires. Our aim was to determine the specific content and modalities that would be most useful for pediatric orthopedic educational programs abroad, and we demonstrate a practical method of identifying country-specific educational deficits through a self-reported needs survey in Haiti. DESIGN:A cross-sectional survey was administered using an automated response system. We obtained demographic information as well as training and practice patterns, comfort levels with pediatric diagnoses, and desired topics for education using a 5-point Likert Scale. SETTING:Haitian Annual Assembly for Orthopaedic Trauma (HAAOT), the only national, continuing medical education conference for orthopedic providers in Haiti. PARTICIPANTS:Of 60 eligible participants, 51 were included in the final analysis. RESULTS:Time spent on pediatric orthopedics varied widely, centered at 10% to 25%. Median comfort level with pediatric orthopedics was 3 of 5. Skills with lowest self-reported competence included spica casting, clubfoot casting, and management of supracondylar humerus fractures. Skills with highest self-reported competence were long-leg casting and Salter-Harris classification. Modes of education highly requested included didactics/lectures, hands-on sessions, dedicated rotations, and exchanges with foreign peers/mentors. Diagnoses most encountered were osteomyelitis, trauma, and clubfoot; lowest comfort levels were in neuromuscular, spine, lower extremity deformity, congenital hip, and clubfoot; and most requested for future teaching were congenital hip, neuromuscular, and spine. CONCLUSIONS:Haitian orthopedic providers express a strong desire and need for ongoing pediatric orthopedic education. They describe a high prevalence of trauma and infection, but convey a requirement for more comprehensive, multimodal teaching that also includes congenital deformities/dysplasias, neuromuscular, and spine. Our results demonstrate the importance of assessing country-specific needs and involving local care providers in curriculum development.

Dataset Information

Self-reported data for mental workload modelling in human-computer interaction and third-level education.

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Self-reported data for mental workload modelling in human-computer interaction and third-level education.

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