Online learning service engine based on dynamic recommendation learning

Abstract

The invention relates to an online learning service engine based on dynamic recommendation. The online learning service engine comprises a knowledge point evaluation module, a learning time module anda knowledge point weight module, and is characterized in that the knowledge point evaluation module acquires evaluation scores of students, and calculates according to a standard knowledge graph to obtain a personal knowledge graph; the learning time module acquires online learning time formulated by a student; and the knowledge point weight module sets a weight factor of each knowledge point according to the personal knowledge graph, calculates a learning duration corresponding to each knowledge point through a weight algorithm, and configures the corresponding knowledge point for students to learn according to the online learning time acquired by the learning time module. Compared with the prior art, the online learning service engine has the advantages that repeated learning of the same knowledge point is avoided, the time utilization rate is increased, and the knowledge point reviewing efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of online education, in particular to an online learning service engine based on dynamic recommendation learning. Background technique [0002] The existing online learning in the field of finance and economics has only one syllabus and learning catalog compared with traditional learning. Everyone learns the same knowledge content and learns according to the established catalog and knowledge structure. Since each person's knowledge structure and level of knowledge mastery are different, and the focus of special learning is also inconsistent, the existing learning methods lack the ability to customize themselves according to each person's different situations (learning goals, learning time, knowledge mastery level) The learning method, the same knowledge point is equivalent to repeated learning for students who have already mastered it, resulting in a waste of this part of the time for repeated learning, addi...

AI Technical Summary

Problems solved by technology

Because each person's knowledge structure and knowledge mastery level are different, the focus of special learning is also inconsistent, and the existing learning methods lack the ability to customize themselves according to each person's different situations (learning goals, learning time, knowledge mastery level) The same learning method, the same knowledge point is equivalent to repeated learning for students who have already mastered it, resulting in a waste of this part of the time for repeated learning, adding unnecessary time costs. The requirements are high, and it is easy to cause a lack of motivation in the middle of the study, causing most students to not persist until the end and give up halfway, so that they cannot complete the set learning goals and tasks

[0003] Prior art CN202010581312.1 discloses a teaching method and system based on a cross-media dynamic knowledge map, which obtains feedback information from students and knowledge points, calculates the recommendation degree of knowledge points and the rating of learning resource recommendation values, and constructs a knowledge point recommendation list The learning resource recommendation list is returned to the learner, but the knowledge point recommendation list and the learning resource recommendation list are fixed after they are generated. The learners are not tracked and followed up, and there is a lack of real-time updates based on the learner's learning effect.

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