Cross-domain multi-source data evaluation model training method, system and device and medium

Abstract

The invention discloses a cross-domain multi-source data evaluation model training method, system and device and a medium. The method comprises the steps: determining key index factors of a plurality of teaching fields; determining a plurality of data sources in each teaching field, and carrying out point burying on the data sources matched with the key index factors; constructing a multi-source data evaluation model and a corresponding prediction model according to the key index factors; obtaining user behavior data through the data source after point burying; and inputting the user behavior data into the multi-source data evaluation model and the prediction model to obtain an evaluation prediction result, and optimizing the multi-source data evaluation model according to the evaluation prediction result to obtain a trained multi-source data evaluation model. The method is suitable for complex application scenes such as modular teaching and cross-domain combined teaching, solves the problems of single data source, single evaluation index and lagging evaluation result of the evaluation model, improves the accuracy and reliability of the evaluation model, and can be widely applied to the technical field of teaching evaluation.

Description

technical field [0001] The invention relates to the technical field of teaching evaluation, in particular to a cross-field multi-source data evaluation model training method, system, device and medium. Background technique [0002] With the advent of the Internet and the era of big data, data-driven has become the main theme of the new era of digital economy. Evaluating teaching effectiveness through the establishment of a data analysis system has become an important tool for educators. How to build an effective data evaluation model and effectively obtain key data has become an important issue. [0003] Traditional evaluation systems bury points through pre-data, then collect data, and finally output evaluation results based on evaluation indicators. This method often has a lag in the evaluation results, which leads to the inability to guide the improvement of the teaching process in a timely manner; at the same time, the data embedding usually needs to be collected under...

AI Technical Summary

Problems solved by technology

[0006] Therefore, an object of the embodiments of the present invention is to provide a cross-domain multi-source data evaluation model training method, which can effectively solve the problems of single source of evaluation model data, single evaluation index, and lagging evaluation results.

Images