Self-reported data for mental workload modelling in human-computer interaction and third-level education
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 assessme...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340920303279 |
| _version_ | 1828419637204746240 |
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| author | Lucas Rizzo Luca Longo |
| author_facet | Lucas Rizzo Luca Longo |
| author_sort | Lucas Rizzo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T14:55:55Z |
| format | Article |
| id | doaj.art-8c7cd2e1b1df49c1bba20dc9188d3286 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-10T14:55:55Z |
| publishDate | 2020-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-8c7cd2e1b1df49c1bba20dc9188d32862022-12-22T01:44:17ZengElsevierData in Brief2352-34092020-06-0130105433Self-reported data for mental workload modelling in human-computer interaction and third-level educationLucas Rizzo0Luca Longo1Technological University Dublin, Republic of IrelandTechnological University Dublin, Republic of Ireland; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2352340920303279Knowledge-based systemsFuzzy reasoningExpert systemsMental workloadAutomated reasoningArgumentation theory |
| spellingShingle | Lucas Rizzo Luca Longo Self-reported data for mental workload modelling in human-computer interaction and third-level education Data in Brief Knowledge-based systems Fuzzy reasoning Expert systems Mental workload Automated reasoning Argumentation theory |
| title | Self-reported data for mental workload modelling in human-computer interaction and third-level education |
| title_full | Self-reported data for mental workload modelling in human-computer interaction and third-level education |
| title_fullStr | Self-reported data for mental workload modelling in human-computer interaction and third-level education |
| title_full_unstemmed | Self-reported data for mental workload modelling in human-computer interaction and third-level education |
| title_short | Self-reported data for mental workload modelling in human-computer interaction and third-level education |
| title_sort | self reported data for mental workload modelling in human computer interaction and third level education |
| topic | Knowledge-based systems Fuzzy reasoning Expert systems Mental workload Automated reasoning Argumentation theory |
| url | http://www.sciencedirect.com/science/article/pii/S2352340920303279 |
| work_keys_str_mv | AT lucasrizzo selfreporteddataformentalworkloadmodellinginhumancomputerinteractionandthirdleveleducation AT lucalongo selfreporteddataformentalworkloadmodellinginhumancomputerinteractionandthirdleveleducation |