A theoretical framework and operational model for skills acquisition and learning processes in advanced manufacturing systems

Kuo-Hao Tang, Purdue University

Abstract

The objective of this research is to find a more efficient way for predicting skill acquisition requirements and mental workload for cognitive oriented tasks. A variety of theoretical approaches are reviewed as basis of skill and knowledge assessment, and a hybrid model is presented. These model components include taxonomy of abilities, knowledge structure, learning level, cognitive resources, and automatization. In order to integrate these components into a coherent framework, the relationships between the components are analyzed and a theoretical framework is presented. The framework provides a complete view of the learning processes as well as the dynamic interactions between model components.^ Among all the components in the theoretical framework, the interactions between taxonomy of abilities and cognitive resources are identified as the most important factors associated with skills acquisition and learning processes. Therefore, validation of the operational model is based on these two factors. The relationship between ability margin, defined as the margin between ability levels possessed by an operator and ability levels required by a task, and required cognitive resources is depicted and mathematically modeled.^ Two hypotheses are developed to validate the model. The first hypothesis states that in order to perform a task, the required cognitive resources are an exponential function of ability margins. This function is termed as an ability-resource curve. The second hypothesis is to determine whether additivity exists among different abilities in terms of the resource allocation policy for each identified ability-resource curve. Although the exponential relationship is proposed in the hypothesis one, the regression analysis suggests that both exponential and linear relationships can be found and no significant difference exists in terms of their predictive power with regard to mental workload. Result from the ANOVA further suggests that abilities are independent to each other. Therefore, several ability-resource curves can be used in conjunction to predict a task requiring these abilities.^ These results validate a method by which the total mental workload of a task, based on each operator's abilities, can be determined a priori through a series of predetermined ability-resource curves. This research shows that it may be fruitful to investigate the interactions between existing skill acquisition models. Particularly, the results of this research suggest that cognitive abilities and cognitive resources should be considered together since cognitive resource is a function of ability margins. The establishment of the linkage between the two independent research areas is the most important theoretical implication provided by this research. ^

Degree

Ph.D.

Advisors

Major Professor: Richard J. Koubek, Purdue University.

Subject Area

Education, Educational Psychology|Engineering, Industrial|Psychology, Industrial|Education, Vocational

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