Competence-Based Knowledge Space Theory from the Perspective of Formal Concept Analysis

作     者：Huang, Baokun Li, Jinjin Li, Qifang Zhou, Yinfeng Chen, Huiqin 

作者机构：School of Mathematics and Statistics Minnan Normal University Fujian Zhangzhou China Fujian Key Laboratory of Granular Computing and Applications Fujian Zhangzhou China School of Mathematics and Statistics Shaanxi Normal University Shaanxi Xi’an China 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Formal concept analysis 

摘      要：In this paper, competence-based knowledge space theory (CbKST) is approached from the perspective of formal concept analysis (FCA). The aim is to visualize the relationship between knowledge structure and competence structure. The skill map is transformed into a formal context, and the methodology for constructing the concept lattice is provided. Within a concept lattice, each node, referred to as a concept, embodies a knowledge state and a competence state as its extension and intention, respectively. It is demonstrated that the family of sets formed by the extensions of all concepts is exactly the knowledge structure delineated by the skill map via the corresponding model. Additionally, the difference set of the extensions of the proper sbuconcept-superconcept in a concept lattice reflects the master fringe of the individual knowledge state. A concept lattice is considered favorable if an individual can solve the items of the master fringe only by mastering one skill, and the sufficient and necessary conditions for favorable concept lattices are given. When skills have no inherent surmise relationship, the order in which they are learned can be arbitrary. From the perspective of effective learning, we propose a precedence relation between skills to guide the recommendation of personalized learning schemes. Moreover, it is proved that on a favorable concept lattice, there is a learning path that adheres to the principle of step-by-step and easy-to-difficult. © 2023, The Authors. All rights reserved.