User subject learning state analysis method and system and storage medium

Abstract

The invention provides a user subject learning state analysis method and system, and a storage medium. The method comprises the steps of obtaining learning data of a target user for a target subject in a unit period; calculating a target popularity value of the target user for the target subject in each unit period according to the learning data; calculating a target interest value of the target user for the target subject according to the heat value of each unit period in the statistical period; obtaining the average number of learning times of the target subject of all users in the statistical period and the number of learning times of the target subject of the target user in the statistical period; and calculating a target preference value of the target user for the target subject according to the target interest value, the average learning frequency and the learning frequency. According to the method, the real-time preference state of the user can be quickly calculated, dynamic evolution of the subject learning state is realized, and scientific data support is provided for parents and teachers to know scores in real time and further check missing and fill vacancy.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to an analysis method, system and storage medium for user subject learning status. Background technique [0002] In the education industry, how to effectively identify the user's learning status has always been a major problem in the industry. Parents and teachers are also concerned about the shortcomings of which subjects are preferred by students at the current learning stage. The traditional subject status evaluation methods are generally static evaluation methods. The static evaluation method uses simple statistics on test scores and time to evaluate the student's skill level. However, this quantitative evaluation method of skill level only considers the final test scores and ignores Without the analysis and utilization of the training situation before the final test, the student's skill level is often only determined by the final test score, and the description of the...

AI Technical Summary

Problems solved by technology

The traditional subject status evaluation methods are generally static evaluation methods. The static evaluation method uses simple statistics on test scores and time to evaluate the student's skill level. However, this quantitative evaluation method of skill level only considers the final test scores and ignores The analysis and utilization of the training situation before the final test, the student's skill level is often only determined by the final test score, and the description of the dynamic evolution characteristics of the skill level is lacking

[0003] There are two major flaws in the above method. On the one hand, the test cycle is too long. Parents and even teachers often obtain subject preferences after the exam is completed, or even at the end of the semester, so it is impossible to track the academic situation in real time.

On the other hand, the conclusions obtained are very rough. Parents cannot obtain more accurate preference data from the detailed state of students' learning, and cannot quickly tap the potential of advantageous subjects, and it is also difficult to check for omissions and make up for non-preferred subjects.

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