Characteristics of Fluent Skills in a Complex, Dynamic Problem-Solving Task

Sohn, Myeong-Ho; Douglass, Scott; Chen, Monchu; Anderson, John R.

doi:10.1518/001872005775570943

Cited by 21 publications

(19 citation statements)

References 13 publications

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“…Lee & Anderson, 2001;Sohn, Douglass, Chen, & Anderson, 2005); however, we propose several improvements to modeling subtask learning that have the potential to challenge this finding. First, previous studies have analyzed learning data at the group-level, rather than the psychologically more meaningful individual-level.…”

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confidence: 99%

Hierarchical Bayesian models of subtask learning.

Anglim¹,

Wynton²

2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Abstract. The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using DIC, posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that while learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group-and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only one parameter for scaling. The inclusion of six strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.

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mentioning

confidence: 99%

Hierarchical Bayesian models of subtask learning.

Anglim¹,

Wynton²

2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…Calculations were performed with the numerical code Fluent 12 [14]. This code has been applied in various liquid or gas engineering problems [15,16].…”

Section: The Numerical Codementioning

confidence: 99%

Evaluation of the effect of a hydraulic impeller in a flocculation basin on hydrodynamic behavior using computational fluid dynamics

Fan

Gong

et al. 2015

Desalination and Water Treatment

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2015) Evaluation of the effect of a hydraulic impeller in a flocculation basin on hydrodynamic behavior using computational fluid dynamics, Desalination and Water Treatment, 54:4-5, 1361-1374, This study investigates the hydraulic impeller in a flocculation basin, which is important to facilitate uniformity in the sedimentation basin and to improve sedimentation efficiency. The experiments were conducted in a Perspex vessel that is 100 × 100 × 700 mm (length × width × height), with an effective depth of 600 mm. A high resolution phase-resolved computational fluid dynamics (CFD) simulation technique was used. The mean velocity, root mean square velocity, and turbulent kinetic energy of different flow patterns were investigated. The commercial CFD software was used to process the data generated from the k-ε model. The velocity of the flow pattern can be predicted by k-ε model. The CFD software FLUENT was used to simulate the hydraulic impeller under various combinations. The turbulence kinetic energy k, the effective energy dissipation ε, velocity gradient G, and pressure were used as criteria to evaluate flocculation effects, to interpret the complex phenomenon in the course of flocculation, and to study the condition of the hydraulic impeller's influence on flocculation by varying the hydraulic impeller combinations. The influence of hydraulic conditions on flocculation can be interpreted by turbulence kinetic energy k, effective energy dissipation ε, velocity grad G, and pressure.

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“…Problem solver goes through the cognitive process of encoding sub-problems in working memory; searching long term memory for algorithms and heuristics; executing the appropriate algorithms or heuristics and the new sub-problem state with its goal; identifying differences between the current state and the goal state; and selecting operations that reduce these differences (Fukumoto & Kishi, 2007;Sohn, Douglass, Chen, & Anderson, 2005;Zhang, 1994) for problem solving. The acquisition process of problem solving skill depends on how the similar problem solving skills are represented to and perceived by the problem solver.…”

Section: The Cognitive Processes In Problem Solvingmentioning

confidence: 99%

An ontology-based similarity measurement for problem-based case reasoning

Lau

Tsui

Lee

2009

Expert Systems with Applications

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a b s t r a c tTraditional case-based reasoning uses a table/frame or scenario to represent a case. It assumed that similar input/event results in similar output/event state. However, similar cases may not have similar output/event states since problem solver may have different way to break down the problem. Thus, authors previously proposed problem-based case reasoning to overcome the limitation of the traditional approaches and used clustered ontology to represent the problem spaces of a case. However, synonym problem causes the mismatch of similar sub-problems of historical cases for new case. Thus, this paper proposed ontology-based similarity measurement to retrieve the similar sub-problems that overcomes the synonym problems on case retrieval. The recall and precise of ontology-based similarity measurement were higher than that of the traditional similarity measurement.

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Characteristics of Fluent Skills in a Complex, Dynamic Problem-Solving Task

Cited by 21 publications

References 13 publications

Hierarchical Bayesian models of subtask learning.

Hierarchical Bayesian models of subtask learning.

Evaluation of the effect of a hydraulic impeller in a flocculation basin on hydrodynamic behavior using computational fluid dynamics

An ontology-based similarity measurement for problem-based case reasoning

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