This research provides tools to support curriculum development for skills education. Specifically, we apply artificial intelligence neural networks and multiple linear regression models to predict a person’s annual wages based on the levels and combinations of skills that they possess. The models are developed based on governmental data for 35 job skills combined with annual wage information for over 960 occupations. Given this input data, the resulting neural network trains to above 70 percent accuracy in predicting annual wage levels. The multiple linear regression models provide somewhat lower performance. Curriculum developers and education administrators can use these models to determine what level and mix of occupational skills are most appropriate for meeting student goals and optimizing wage potentials. Job and career seekers can use these models to generate estimates of how well their skills should be compensated by the job market.
James Otto, Towson University, United States
Chaodong Han, Towson University, United States
Stream: Curriculum Design & Development
This paper is part of the IICEHawaii2020 Conference Proceedings (View)
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