Abstract
Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.
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Webb, G.I., Kuzmycz, M. Feature Based Modelling: A methodology for producing coherent, consistent, dynamically changing models of agents' competencies. User Model User-Adap Inter 5, 117–150 (1995). https://doi.org/10.1007/BF01099758
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DOI: https://doi.org/10.1007/BF01099758