Network Models of Expertise in the Complex Task of Operating Particle Accelerators
Roussel Rahman, Jane Shtalenkova, Aashwin Ananda Mishra, Wan-Lin Hu

TL;DR
This study models expertise in operating particle accelerators using network analysis of operator logs, revealing common strategies in task decomposition across different expertise levels.
Contribution
It introduces a novel network-based method to analyze how expertise develops in complex real-world tasks through natural language processing of operational logs.
Findings
Operators change their subtask focus with expertise
Subtask groupings are remarkably consistent across expertise levels
Operators adopt a divide-and-conquer approach to complex tasks
Abstract
We implement a network-based approach to study expertise in a complex real-world task: operating particle accelerators. Most real-world tasks we learn and perform (e.g., driving cars, operating complex machines, solving mathematical problems) are difficult to learn because they are complex, and the best strategies are difficult to find from many possibilities. However, how we learn such complex tasks remains a partially solved mystery, as we cannot explain how the strategies evolve with practice due to the difficulties of collecting and modeling complex behavioral data. As complex tasks are generally networks of many elementary subtasks, we model task performance as networks or graphs of subtasks and investigate how the networks change with expertise. We develop the networks by processing the text in a large archive of operator logs from 14 years of operations using natural language…
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Taxonomy
TopicsEngineering Education and Technology
