Abstract
Implementing competency-based training, education, and talent management requires models that compute and apply competencies to answer three questions: Does a person or team possess a given competency, how likely are they to successfully apply or demonstrate it, and what is the best way to acquire it? This paper argues that the underlying mathematical models should be treated as separate, although interrelated, and presents a framework for creating and computing such models that includes notions such as the level of a competency and conditions under which it is performed and that takes experience, practice, knowledge and skill decay, and the spacing effect into account. The source data for the computations outlined in this paper, which are described in the most detail for the model that ascribes possession of a competency, consists of assertions that in practice are derived from results reported by training systems. These computations reflect current practice in competency modeling and include “rollup rules” that respect relations among competencies. This paper includes an analysis of how these models appear and are used in intelligent tutoring systems.
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Robson, E., Robson, R., Buskirk, T., Ray, F., Owens, K.P. (2021). An Experiential Competency Application Framework. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_9
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