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An Iterative Recommendation Model of Supporting Personalized Learning Based on Schematic Patterns Mining from Schema-enhanced Contexts of Problem-solving by Lankun Guo; Zhenhua Jia; Guozhi Ma; Jinhai Li is a Computer Science article available to read on EtoBox.

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Based on schema-enhanced formal concept analysis and three-way decisions theory, this paper develops a method of supporting personalized learning by which students can benefit from instructional design over problems. Particularly, we discuss the feasibility of integrating Ausubel's meaningful learning theory and schema theory with formal concept analysis towards data analysis in problem-solving scenarios. Subsequently, we extend the classical formal concept analysis by incorporating a schematic implication structure on attributes of a context. Then, by applying three-way decisions into the context of problem-solving, we propose a strategy of classifying objects according to a certain attribute and a method of stratifying the involved attributes of a set of objects. Furthermore, as an application of three-way decisions with a greater generality, we develop an iterative recommendation model of supporting personalized learning in the context of problem-solving. Finally, we give an illustrative data experiment to demonstrate the mechanism of the model.

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Author

Lankun Guo; Zhenhua Jia; Guozhi Ma; Jinhai Li

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)