A personalized recommendation method for exercises based on multidimensional features in online education platforms

Abstract

The invention discloses a multidimensional feature-based personalized recommendation method for exercises in an online education platform. It includes the following steps: 1) Obtain the learning characteristics of the learners based on the learning data of the learners on the online education platform; 2) Analyze the data of the exercises, obtain the ranking of the popularity of the exercises, and record them in the question bank; Data analysis, using the cosine similarity algorithm to calculate the correlation of each knowledge point; 4) through the multi-dimensional feature algorithm, the learner's learning characteristics, exercise popularity and knowledge correlation between the learner and candidate recommended exercises Dimensional factors are linearly synthesized, and personalized exercises are recommended to learners. The present invention uses a big data platform to perform multi-dimensional statistical analysis on learner data, recommend personalized exercises, and then record the data into the next analysis data as feedback to further improve the accuracy of personalized recommendation, thereby improving the learner's learning ability. Learning ability and learning efficiency.

Description

technical field [0001] The invention relates to the field of big data analysis, in particular to a method for personalized recommendation of exercises based on multidimensional features in an online education platform. Background technique [0002] Special customization is to provide special services according to teaching and actual needs. With the development of the times, the required data is different, and different learning question banks are recommended for different learners, which increases learners' learning efficiency and learning enthusiasm. [0003] In the existing online learning platforms, on the one hand, learners often need to spend a lot of time and energy to find the exercises they are interested in; If the exercises do not suit the style of the learners, the learners' satisfaction with the learning platform will be reduced, that is, there may be a phenomenon of staff turnover. Therefore, if the learners cannot be properly guided to match the exercises wit...

AI Technical Summary

Problems solved by technology

[0003] In the existing online learning platforms, on the one hand, learners often need to spend a lot of time and energy to find the exercises they are interested in; If the exercises are not in line with the learners' style, the learners' satisfaction with the learning platform will be reduced, that is, the phenomenon of staff turnover may occur

Therefore, if the learners cannot be properly guided to match the exercises with the learners, the healthy development of the entire online learning platform will definitely be affected.

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