Learning path planning method based on targeted knowledge-point set

Abstract

The invention provides a learning path planning method based on a targeted knowledge-point set. The learning path planning method based on the targeted knowledge-point target set includes the steps that under a complicated knowledge network, the targeted knowledge-point set for learning is planned, then according to the current learning state of a learner, a search algorithm is used for finding the knowledge network covering the whole targeted knowledge-point set, and then the best learning path is worked out. In the process of learning, the learning state of the learner is analyzed, learning path is dynamically changed, and the learning and guidance effects are reached. By the adoption of the learning path planning method based on the targeted knowledge-point set, rapid grasping of the targeted knowledge-point set is achieved, and the learning efficiency of the learner is improved.

Description

technical field [0001] The present invention relates to the field of online education and knowledge network, and more specifically, relates to a learning path planning method based on a knowledge point target set. Background technique [0002] With the continuous emergence of human research results and the advent of the Internet era, human society has entered the era of "knowledge explosion". According to the statistics of relevant departments, the scientific knowledge accumulated by human beings in the past 30 years accounts for almost 90% of the total knowledge accumulated in history. The human knowledge network is becoming more and more complex. In the process of cognition, students are prone to get lost when faced with a large number of knowledge points and the relationship between them. Learners' learning basically takes courses and stages as a large unit. In the knowledge network, learning objectives such as courses or stages are often a set of knowledge points compos...

AI Technical Summary

Problems solved by technology

These relevant studies on the formulation of learning paths under the knowledge network cannot make judgments based on the actual mastery of specific learners and formulate effective and most suitable learning trajectories on this basis.

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