Human Problem Solving in Dynamic Environments. Understanding and Supporting Operators in Large-Scale, Complex Systems

1987 ◽  
Author(s):  
Richard L. Henneman ◽  
William B. Rouse
Keyword(s):  
Problem Solving ◽  
Complex Systems ◽  
Large Scale ◽  
Dynamic Environments ◽  
Human Problem

System Failures

2019 ◽  
pp. 91-112
Author(s):  
William B. Rouse
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Complex Systems ◽  
Mental Models ◽  
Social Phenomena ◽  
Team Coordination ◽  
System Failures ◽  
Operations And Maintenance ◽  
Problem Solving Strategies ◽  
Human Problem

This chapter focuses on the operations and maintenance of new product and service offerings once they have been deployed; in particular, it addresses dealing with system failures. Addressing system failures is an important aspect of operating and maintaining complex systems, particularly when laced with behavioral and social phenomena. Despite advances in technology and automation, humans will inevitably have roles in addressing failures when detection, diagnosis, and compensation cannot be automated. Human problem-solving involves a mix of pattern recognition and structural sleuthing based on mental models for taskwork and teamwork. Training and aiding can enhance human problem-solving performance by fostering problem-solving strategies and tactics, as well as team coordination.

A Model of Human Problem Solving in Dynamic Environments

1983 ◽  
Vol 27 (8) ◽  
pp. 695-699 ◽  
Author(s):  
Annette Knaeuper ◽  
William B. Rouse
Keyword(s):  
Problem Solving ◽  
Process Control ◽  
Computer Program ◽  
General Structure ◽  
Dynamic Environments ◽  
Control Task ◽  
Rule Based ◽  
The Impact ◽  
Human Problem

Human problem solving is considered in terms of the effects of the environment being dynamic. For example, the impact of having to operate a system while also trying to diagnose failures is discussed as well as several related issues. A general structure is proposed for modeling human problem solving in such environments. A realization of this general structure within a particular rule-based computer program is discussed. Results are presented from applying this program to modeling human problem solving in a process control task.

Simulation of human problem-solving

1959 ◽  
Author(s):  
W. G. Bouricius ◽  
J. M. Keller
Keyword(s):  
Problem Solving ◽  
Human Problem

scholarly journals Using process data to understand problem-solving strategies and processes for drag-and-drop items in a large-scale mathematics assessment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yang Jiang ◽  
Tao Gong ◽  
Luis E. Saldivia ◽  
Gabrielle Cayton-Hodges ◽  
Christopher Agard
Keyword(s):  
Problem Solving ◽  
Time Allocation ◽  
Large Scale ◽  
Solution Process ◽  
Process Data ◽  
Mathematics Assessments ◽  
Assessment Design ◽  
Eighth Grade Students ◽  
Problem Solving Strategies ◽  
Drag And Drop

AbstractIn 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to study the cognitive and behavioral processes that underlie test-takers’ performances in ways that are not otherwise possible with the response data alone. In this exploratory study, we investigated the problem-solving processes and strategies applied by the nation’s fourth and eighth graders by analyzing the process data collected during their interactions with two technology-enhanced drag-and-drop items (one item for each grade) included in the first digital operational administration of the NAEP’s mathematics assessments. Results from this research revealed how test-takers who achieved different levels of accuracy on the items engaged in various cognitive and metacognitive processes (e.g., in terms of their time allocation, answer change behaviors, and problem-solving strategies), providing insights into the common mathematical misconceptions that fourth- and eighth-grade students held and the steps where they may have struggled during their solution process. Implications of the findings for educational assessment design and limitations of this research are also discussed.

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