Learning resource recommendation system

Abstract

The invention provides a learning resource recommendation system. The system comprises a first receiving module, a generation module, a recommendation module and a second receiving module, wherein the first receiving module is used for receiving the curriculum, the push time of the curriculum and a question bank, and the question bank comprises the tile corresponding to each curriculum chapter; the generation module is used for generating test paper corresponding to each curriculum chapter according to the question bank, setting the recommendation time for the test paper, generating a classroom answer sheet by combining the test paper with the recommendation time and sending the classroom answer sheet to the recommendation module; the recommendation module is used for pushing the classroom answer sheet and the tiles of the curriculum chapters; the second receiving module is used for receiving search conditions, obtaining search results and receiving answers of the classroom answer sheet.

Description

technical field [0001] The invention relates to a device in the field of teaching technology, in particular to a learning resource recommendation system. Background technique [0002] With the development of personalized service technology, personalized services on the Internet have sprung up in front of people's eyes, and the research on this aspect has also made remarkable achievements. Most of the personalized recommendation methods are now mainly used in e-commerce recommendations. There are not many recommendations for learning resources, and the results of learning resources pushed by using product recommendation model algorithms are not accurate. Most college teachers only make teaching progress for students to check, but they do not use it in recommending their own course resources, and classroom tests are also in the form of paper test papers. [0003] In the teaching system in the prior art, there is a lack of identification of students' learning interests, a meth...

AI Technical Summary

Problems solved by technology

Now most of the personalized recommendation methods are mainly used in e-commerce recommendation. There are not many recommendations for learning resources, and the results of learning resources pushed by using product recommendation model algorithms are not accurate.

Most college teachers only make teaching progress for students to check, but they do not use it in recommending their own course resources, and classroom tests are also in the form of paper test papers

[0003] In the teaching system in the prior art, there is a lack of identification of students' learning interests, a method of extracting interests from students' learning content, a lack of interest prediction methods, and a better understanding of students' learning situations; and the process of teacher compilation is complicated, After students answer the questions, they still need to perform the operation of batching papers. Now, most of the course chapters in each class are provided with in-class exam papers to consolidate the learning process. The workload is very heavy; the above problems have hindered the improvement of teaching quality and teaching efficiency

Images