Identifying objective criterion to determine a complicated task – A comparative study

Park, Jinkyun; Jung, Woo‐Sik

doi:10.1016/j.anucene.2015.05.012

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The relationship between complexity and operator performance has been addressed in multiple studies in the past (Campbell, 1991;Chan, Song, & Yao, 2015;Park & Jung, 2015). Task complexity has been found to have a profound effect on task performance (Topi, Valacich, & Hoffer, 2005).…”

Section: Task Level Complexities In Proceduresmentioning

confidence: 99%

Application of Natural Language Processing in Procedure Design

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Procedures play a vital role in high-risk systems in ensuring that the required level of system performance is achieved while maintaining the associated risks below an acceptable level. An effective procedure has a low level of task complexities i.e., elements contributing to a multiplicity of paths/outcomes, uncertainty in information, interdependency, a multiplicity of instructions/object of instructions and an excess amount of information. As a part of this study, we have developed a Natural Language Processing algorithm that can identify these elements in a procedure. The algorithm was tested for a dataset consisting of 20 procedures and the results were found to be promising.

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Section: Task Level Complexities In Proceduresmentioning

confidence: 99%

Application of Natural Language Processing in Procedure Design

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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“…The results also show that step complexity metrics and sentiment may also play a substantial role in procedure step performance. Although there has been some previous research investigating the relationships between complexity and performance (Campbell, 1991; Chan et al, 2015; Park & Jung, 2015), the findings regarding the relationship between a type of complexity, worker experience, and attributes of the procedure design are novel and need to be pursued further to be more clearly understood. Similarly, the significance of the sentiment findings must be explored further in future work.…”

Section: Discussionmentioning

confidence: 99%

Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine Learning-Driven Procedure Design

2020

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Objective The goal of this study is to assess machine learning for predicting procedure performance from operator and procedure characteristics. Background Procedures are vital for the performance and safety of high-risk industries. Current procedure design guidelines are insufficient because they rely on subjective assessments and qualitative analyses that struggle to integrate and quantify the diversity of factors that influence procedure performance. Method We used data from a 25-participant study with four procedures, conducted on a high-fidelity oil extraction simulation to develop logistic regression (LR), random forest (RF), and decision tree (DT) algorithms that predict procedure step performance from operator, step, readability, and natural language processing-based features. Features were filtered using the Boruta approach. The algorithms were trained and optimized with a repeated 10-fold cross-validation. After training, inference was performed using variable importance and partial dependence plots. Results The RF, DT, and LR algorithms with all features had an area under the receiver operating characteristic curve (AUC) of 0.78, 0.77, and 0.75, respectively, and significantly outperformed the LR with only operator features (LROP), with an AUC of 0.61. The most important features were experience, familiarity, total words, and character-based metrics. The partial dependence plots showed that steps with fewer words, abbreviations, and characters were correlated with correct step performance. Conclusion Machine learning algorithms are a promising approach for predicting step-level procedure performance, with acknowledged limitations on interpolating to nonobserved data, and may help guide procedure design after validation with additional data on further tasks. Application After validation, the inferences from these models can be used to generate procedure design alternatives.

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“…In the task component, we referred to Park and Jung's study (2015) and chose five indicators (large amounts of information, large number of steps, complex logic within steps, need for broad knowledge, and high decision levels) to reflect the task complexity because during the interview, participants affirmed the influence of task complexity but did not provide sufficiently detailed indicators to characterize the complexity of commissioning tasks. Park and Jung's study (2015) aimed at developing an objective criterion for assessing the task complexity, and the complexity scores calculated by these five indicators showed a significant correlation with the scores of subjective scaling. Note that many PSF sets include the PSF “time pressure,” but our PSF framework does not contain it as a PSF factor.…”

Section: Discussionmentioning

confidence: 99%

“…Task 8.1. Task complexity Indicators for task complexity were originally borrowed from Park and Jung (2015), and five indicators were chosen to reflect how effort demanding a task is for a staff to perform.…”

Section: Content Qualitymentioning

confidence: 99%

Identifying and clustering performance shaping factors for nuclear power plant commissioning tasks

Yin

Liu

2020

Hum Ftrs & Erg Mfg Svc

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The commissioning of a nuclear power plant (NPP) is the last stage in the life cycle of an NPP before commercial operation. Human errors in this stage cannot only harm workers' safety but can also cause damage to system facilities, postpone the commissioning schedule, and become latent problems in the system if the errors are not discovered during the commissioning stage. Therefore, identifying and controlling potential human errors is of great importance at this stage. This study first reviewed

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Identifying objective criterion to determine a complicated task – A comparative study

Cited by 9 publications

References 28 publications

Application of Natural Language Processing in Procedure Design

Application of Natural Language Processing in Procedure Design

Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine Learning-Driven Procedure Design

Identifying and clustering performance shaping factors for nuclear power plant commissioning tasks

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