Adaptive personalized learning guidance model based on the perspective of synergistic dependence between performance level and ability level
The competence-based knowledge space theory provides a scientific and effective theoretical framework for educational assessment and learning guidance.In this theory,the purpose of educational assessment is to understand students'competence levels(competence state)in specific domains based on their answers(knowledge state),and provide personalized learning guidance based on the assessment results.However,there is no one-to-one correspondence between competence state and knowledge state.Solely relying on knowledge state only determines a category of competence state,without accurately judging the students'competence state.Therefore,this paper proposes a general method for the direct calculation of the master fringe of the knowledge state and personalized learning guidance based on the ceiling or floor of the same category of competence.The first step is to obtain the master fringe of the current knowledge state,the second step is to choose the target knowledge state based on the master fringe,the third step is to attain the ceiling or floor of the competence state corresponding to the current knowledge state and the target knowledge state,and the fourth step is to push the skill set to be learned soon based on the ceiling or floor,to achieve the target knowledge state.This paper also provides two characterization theorems,using the skill function to describe the ceiling or floor of the competence state,and using the problem function to describe the master fringe of the knowledge state.Obtaining the ceiling or floor of the competence state,and the master fringe of the knowledge state without establishing the knowledge structure improves acquisition efficiency.Finally,this paper presents algorithms to obtain the master fringe based on definitions and characterization theorems,and comparative practice demonstrates that the latter has shorter time consumption and smaller memory usage.
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