Learning Paths and Skills Assessment in Formal Context
ZHOU Yinfeng1, LI Jinjin1,2, FENG Danlu1, YANG Taoli1
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000
Abstract In the learning process, learners may learn and master some skills with their knowledge state unchanged. In this situation, learners' skills cannot be assessed accurately due to the unchanged knowledge state. In this paper, a method of formal concept analysis is employed based on the skill function to find learning paths and conduct skills assessment. Firstly, the concepts of subsequent state, effective skill and well-formed skill function are introduced. Secondly, based on the formal context, the conditions that the skill functions satisfy the well-formedness are discussed in two situations. The results of gradual effective learning and effective assessment are obtained under the well-formedness conditions, and the algorithms for obtaining the well-formed skill contexts and the well-formed skill functions and finding learning paths are designed. Finally, the effectiveness of the proposed algorithms is verified on two datasets. The learning paths diagram obtained by the well-formed skill function can not only guide the learners to study effectively but also evaluate whether the learners master the corresponding effective skills according to the change of the learners' knowledge states.
Fund:National Natural Science Foundation of China(No.11871259), Natural Science Foundation of Fujian Province(No.2019J01748,2020J02043)
Corresponding Authors:
LI Jinjin, Ph.D., professor. His research interests include information technology, mathematical theories and methods of uncertainty.
About author:: ZHOU Yinfeng, master student. Her research interests include knowledge space theo-ry. FENG Danlu, master student. Her research interests include knowledge space theory. YANG Taoli, master student. Her research interests include knowledge space theory.
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