Affiliations: [a] School of Mathematical Sciences, Huaqiao University, Quanzhou, Fujian, P. R. China | [b] School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, P. R. China | [c] Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou, P. R. China
Abstract: As an important mathematical theory in intelligent learning and assessment system, knowledge space theory merely cares about items are mastered or non-mastered. Thus it needs to be further explored to achieve more precise and interpretable analysis. To this end, this paper mainly focuses on knowledge structures in corporate with Solo taxonomy. Then, fuzzy knowledge structure and fuzzy learning space are gradually developed. The corresponding knowledge base and surmise relation are explored respectively as well. In such case, the induced maximal knowledge space and its properties are further studied sufficiently. And three kinds of skill models are put forward based on skill proficiency. Finally, a case study is presented to illustrate the advantage in learning description.
