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

Ademar Crotti Junior; Christophe Debruyne; Luca Longo; Declan O’Sullivan

doi:10.1007/978-3-030-14273-5_10

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

Ademar Crotti Junior, Christophe Debruyne, Luca Longo, Declan O’Sullivan

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Human Mental Workload
Subtitle of host publication	Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers
Editors	Luca Longo, M. Chiara Leva
Publisher	Springer Verlag
Pages	160-179
Number of pages	20
ISBN (Print)	9783030142728
DOIs	https://doi.org/10.1007/978-3-030-14273-5_10
Publication status	Published - 2019
Event	2nd International Symposium on Mental Workload, Models and Applications, H-WORKLOAD 2018 - Amsterdam, Netherlands Duration: 20 Sep 2018 → 21 Sep 2018

Publication series

Name	Communications in Computer and Information Science
Volume	1012
ISSN (Print)	1865-0929

Conference

Conference	2nd International Symposium on Mental Workload, Models and Applications, H-WORKLOAD 2018
Country/Territory	Netherlands
City	Amsterdam
Period	20/09/18 → 21/09/18

Keywords

Linked data
Mental workload
Uplift mapping representations

Access to Document

10.1007/978-3-030-14273-5_10

https://arrow.tudublin.ie/adaptcon/3Licence: CC BY-NC-SA

Cite this

Junior, A. C., Debruyne, C., Longo, L., & O’Sullivan, D. (2019). On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. In L. Longo, & M. C. Leva (Eds.), Human Mental Workload: Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers (pp. 160-179). (Communications in Computer and Information Science; Vol. 1012). Springer Verlag. https://doi.org/10.1007/978-3-030-14273-5_10

Junior, Ademar Crotti ; Debruyne, Christophe ; Longo, Luca et al. / On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. Human Mental Workload: Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers. editor / Luca Longo ; M. Chiara Leva. Springer Verlag, 2019. pp. 160-179 (Communications in Computer and Information Science).

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abstract = "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.",

keywords = "Linked data, Mental workload, Uplift mapping representations",

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language = "English",

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Junior, AC, Debruyne, C, Longo, L & O’Sullivan, D 2019, On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. in L Longo & MC Leva (eds), Human Mental Workload: Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 1012, Springer Verlag, pp. 160-179, 2nd International Symposium on Mental Workload, Models and Applications, H-WORKLOAD 2018, Amsterdam, Netherlands, 20/09/18. https://doi.org/10.1007/978-3-030-14273-5_10

On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. / Junior, Ademar Crotti; Debruyne, Christophe; Longo, Luca et al.
Human Mental Workload: Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers. ed. / Luca Longo; M. Chiara Leva. Springer Verlag, 2019. p. 160-179 (Communications in Computer and Information Science; Vol. 1012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2019

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AB - 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.

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Junior AC, Debruyne C, Longo L, O’Sullivan D. On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. In Longo L, Leva MC, editors, Human Mental Workload: Models and Applications - 2nd International Symposium, H-WORKLOAD 2018, Revised Selected Papers. Springer Verlag. 2019. p. 160-179. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-14273-5_10

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

Abstract

Publication series

Conference

Keywords

Access to Document

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Cite this