A concept fringe-based concept-cognitive learning method in skill context

Concept-cognitive learning has achieved remarkable results in simulating the learning of concepts. However, the existing concept-cognitive learning models mainly focus on how knowledge is acquired, but ignore the fact that knowledge transfer and knowledge forgetting may occur during the process of learning skills and solving items. This limits the application of concept-cognitive learning in predicting knowledge states and assessing competence states in skill contexts. To overcome this limitation, this paper provides anew concept-cognitive learning method for property-oriented concepts and object-oriented concepts in skill context. Corresponding to the conjunctive model and the disjunctive model, the inner and outer fringes of property-oriented concept and object-oriented concept are first defined, respectively. In this way, items or skills that are easily forgotten and those that are in the zone of proximal development can be found under both models. Furthermore, the Jaccard similarity coefficient is used to diversify the learning outcomes by finding items and skills that are most likely to occur knowledge forgetting or knowledge transfer. Thus, based on the fringes of concepts, the algorithms to learn property-oriented concepts and object-oriented concepts are provided, respectively. Finally, the case study on areal world example and the experimental evaluation on six data sets from UCI demonstrate that the proposed method is of practical significance and effective in terms of running time.