Practical effect evaluation and learning path recommendation system and method based on cognitive diagnosis

Abstract

The invention provides a practical effect evaluation and learning path recommendation method based on cognitive diagnosis. The practical effect evaluation and learning path recommendation method comprises an expansion feature preprocessing model, a deep knowledge tracking model, a knowledge network construction model and a path recommendation algorithm based on cognitive ability. The expansion feature preprocessing model performs primary evaluation of cognitive competence according to skill attributes in a learner test process, and introduces personalized difference information into the diagnosis model. And the deep knowledge tracking model predicts the knowledge mastery ability of the learner according to the test sequence and the heterogeneous features of the implicit coding, and the knowledge mastery ability serves as a basis of learning guidance. The exercises and knowledge network construction model provides a global map of scientific thinking, and the cognitive diagnosis is combined to recommend a learning path to a learner, so that the cognitive ability difference in the learning process is considered, and the logic of a knowledge structure is followed.

Description

technical field [0001] The present invention relates to the fields of cognitive diagnosis, smart education and personalized learning, and more specifically, to a method for evaluating practical effects and recommending learning paths based on cognitive diagnosis. Background technique [0002] The Internet education model has broadened and improved the form and content of traditional education, and lowered the threshold for education, but at the same time, it has shown disadvantages in the follow-up improvement, evaluation and supervision of teaching. Traditional education ensures a relatively complete knowledge acquisition process for more advanced learners through teachers' teaching, supervision and evaluation processes. However, it is difficult to capture learners' accurate cognition of various knowledge by relying on general test scores, and teachers cannot target specific knowledge for each individual. The basic differences of students provide completely personalized gui...

AI Technical Summary

Problems solved by technology

Traditional education ensures a relatively complete knowledge acquisition process for advanced learners through teachers' teaching, supervision and evaluation processes. However, it is difficult to capture learners' accurate cognition of various knowledge by relying on test scores in general, and teachers cannot target specific knowledge for each individual. The basic differences of students provide completely personalized guidance; and online education is also due to the lack of follow-up supervision and guidance of subject learning, which greatly reduces the degree of learning completion

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