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Quantitative Modeling of User Perfor...
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ProQuest Information and Learning Co.
Quantitative Modeling of User Performance in Multitasking Environments.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Quantitative Modeling of User Performance in Multitasking Environments./
作者:
Liu, Shijing.
面頁冊數:
1 online resource (172 pages)
附註:
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:
Dissertation Abstracts International78-10B(E).
標題:
Industrial engineering. -
電子資源:
click for full text (PQDT)
ISBN:
9781369856378
Quantitative Modeling of User Performance in Multitasking Environments.
Liu, Shijing.
Quantitative Modeling of User Performance in Multitasking Environments.
- 1 online resource (172 pages)
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Thesis (Ph.D.)
Includes bibliographical references
Multitasking is the human ability to engage in a variety of tasks simultaneously through task switching. This is one of the most important skills required for human operators to perform highly-complex and safety-critical jobs, such as air traffic controllers concurrently performing navigation, communication, and coordination, and commercial vehicle drivers talking on the phone and looking at the radio while driving. However, with the increase of the complexity of tasks, the required mental workload tends to increase and maintaining task performance within an acceptable level becomes more challenging. Individual differences in working memory capacity (WMC) have been estimated as predictors of varying cognitive abilities. The increased demand of cognitive resources may exceed the limitation of the human cognitive system, therefore leading to performance degradation and an increased occurrence of errors.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369856378Subjects--Topical Terms:
679492
Industrial engineering.
Index Terms--Genre/Form:
554714
Electronic books.
Quantitative Modeling of User Performance in Multitasking Environments.
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Quantitative Modeling of User Performance in Multitasking Environments.
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Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
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Adviser: Chang S. Nam.
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Thesis (Ph.D.)
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North Carolina State University
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2017.
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Includes bibliographical references
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Multitasking is the human ability to engage in a variety of tasks simultaneously through task switching. This is one of the most important skills required for human operators to perform highly-complex and safety-critical jobs, such as air traffic controllers concurrently performing navigation, communication, and coordination, and commercial vehicle drivers talking on the phone and looking at the radio while driving. However, with the increase of the complexity of tasks, the required mental workload tends to increase and maintaining task performance within an acceptable level becomes more challenging. Individual differences in working memory capacity (WMC) have been estimated as predictors of varying cognitive abilities. The increased demand of cognitive resources may exceed the limitation of the human cognitive system, therefore leading to performance degradation and an increased occurrence of errors.
520
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Previous studies have demonstrated different approaches for evaluating and improving user performance in a multitasking environment. Nevertheless, studies in the area of multitasking still present significant gaps: (1) few studies have quantitatively analyzed the information processing and user performance in multitasking, and there is a general lack of a quantitative approach to improve multitasking performance; and (2) the relationship between WMC, task difficulty, and user performance in multitasking still remains open.
520
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The main goals of this study were to (1) propose and validate a quantitative model for user performance and improvement in a multitasking environment; and (2) investigate the relationship between WMC, task difficulty, and multitasking performance.
520
$a
In this study, a quantitative model for user performance in multitasking was proposed. The Multi-Attribute Task Battery-II (MATB-II) was used in the experiments as a multitasking platform. The proposed model included quantification of stimuli from each MATB-II subtask as baud rate (bits per second), selection of task difficulty and task weight, as well as the rearrangement of task weights. This research followed a two-phase experimental approach.
520
$a
The first phase consisted of two sessions: practice and MATB-II experiment sessions. The objectives of the first phase were to apply the proposed model and identify a performance baseline for each participant performing MATB-II tasks. Before the experiment, participants were asked to complete a span test to estimate their WMC. The automated operation span task (OSPAN) was applied in this study. The task involves a computerized span test requiring participants to remember items (letters) and solve math problems. At the beginning of Phase I experiment, all participants were required to complete a two-hour practice session in MATB-II. If their performance met the predefined criteria, they were selected to undertake the MATB-II experiment session. During the experiment session, selected participants completed a set of equally weighted MATB-II tasks with different levels of task difficulty. A performance baseline was calculated for each participant. Twenty-five participants were qualified and completed all sessions in Phase I.
520
$a
The second phase consisted of three sessions: rearrangement of task weights, MATB-II experiment, and comparison. The objectives of the second phase were to validate the proposed model through a rearranged set of multitasks and performance comparison, and to investigate the relationship of WMC, task difficulty, and multitasking performance. All twenty-five participants from Phase I were invited to the second phase. During the second phase, a revised set of multitasks was designed for each participant according to their performance baseline and the proposed model. Then, the revised set of multitasks was tested by the select participants. Comparisons of user performance were made between two phases to validate the proposed model. Results from the comparisons indicated improvement of user performance in the second phase. Statistical analyses were performed to investigate the effects of WMC and task difficulty. Results demonstrated the relationship between WMC, task difficulty and user performance that higher level of task difficulty led to decreased performance and participants with high WMC had an overall high level of performance.
520
$a
The proposed model attempted to offer an approach to quantify the information from machines and the response from human operators in a multitasking environment. From a practical point of view, the current research may make significant contributions to improve the design of multitasking systems and training procedures for human operators. From a theoretical perspective, this research provides a framework to quantitatively evaluate multitasking systems and human performance in order to understand the interaction between systems and human operators.
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Electronic reproduction.
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Ann Arbor, Mich. :
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ProQuest,
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2018
538
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Mode of access: World Wide Web
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Industrial engineering.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10610754
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click for full text (PQDT)
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