Teaching evaluation data analysis method

Abstract

The invention relates to a teaching evaluation data analysis method, and the method comprises the steps that a user terminal receives the user information of a first user and the evaluation data of the first user, and transmits the user information of the first user and the evaluation data of the first user to a server; the server determines an evaluation derivation model corresponding to the evaluation type data according to the evaluation type data; the user information of the first user and the evaluation data of the first user is analyzed according to an evaluation derivation model to obtain evaluation result data; and the evaluation result data is sent to the user terminal for the user terminal to display the evaluation result data. According to the teaching evaluation data analysis method provided by the embodiment of the invention, the evaluation derivation model is determined according to the evaluation type, and the collected data is input into the evaluation derivation modelto obtain the evaluation result of teaching and learning, so that the selection of the evaluation derivation model is more objective and scientific, and the evaluation result of teaching and learningcan truly reflect the teaching situation.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a teaching evaluation data analysis method. Background technique [0002] At present, most of the software used by the education department is EXCEL or immature analysis software in the evaluation and evaluation, and there are more or less the following problems: 1. The evaluation indicators are very limited, such as only the average score, excellent rate 1, rankings and standard scores calculated by the formula method; 2, the data information cannot be shared, and the use area is very narrow; 3, the use does not have humanized operation, which makes the statistical analysis work very complicated, and even requires technical personnel who understand programming. Use; 4. It can only be aimed at the ultimate evaluation, and cannot meet the developmental and formative evaluation; 5. It lacks the guidance of advanced evaluation concepts. Although there are many indicators, it...

AI Technical Summary

Problems solved by technology

[0002] At present, most of the software used by the education department is EXCEL or immature analysis software in the evaluation and evaluation, and there are more or less the following problems: 1. The evaluation indicators are very limited, such as only the average score, the excellent rate 1, rankings and standard scores calculated by the formula method; 2, the data information cannot be shared, and the scope of use is very narrow; 3, the use does not have user-friendly operations, resulting in complicated statistical analysis work, and even requires technical personnel who understand programming. Use; 4. It can only be aimed at the ultimate evaluation and cannot meet the developmental and formative evaluation; 5. It lacks the guidance of advanced evaluation concepts. Although there are many indicators, it is not systematic and cannot meet the needs of long-term monitoring and evaluation.

[0003] Moreover, due to the above-mentioned problems in the evaluation, analysis and evaluation, the following phenomena exist in the current educational evaluation: 1. Use words such as good, bad or average to make vague evaluations, or make one-sided evaluations based on impressions and feelings, lacking and ignoring scientific quantitative data Analysis; 2. The channels and means to obtain and feedback evaluation information are limited, and the evaluation information can only serve a small number of people, and the true value of the evaluation information cannot be brought into play; 3. The evaluation information is chaotic and the evaluation standards are not uniform, resulting in insufficient relevance of the evaluation information But it cannot be tracked; 4. A large number of data reports are only a reflection of superficial phenomena, without deeper data mining, it is impossible to judge the causal relationship between teaching quality and data; 5. The evaluation work lacks integrity, such as emphasizing results rather than processes , only up-to-down evaluation, etc.

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