Formability optimization system and method for cloud data education and training process

Abstract

The invention belongs to the technical field of big data, and relates to a formability optimization system and method for a cloud data education and training process. The formability optimization system for the cloud data education and training process comprises a student feedback information acquisition module, a learning intention judgment module, a learning effect judgment module, a learning condition judgment module, an intelligent gateway A, a data processor, a learning score input module, an intelligent gateway B, a database and a machine learning module. Through the mutual communicationbetween the education terminal and the cloud server, the system can quickly reach the optimal state by utilizing the big data technology and machine learning, and the learning states of students aredivided into willingness, conditions and effects, and the hierarchical analysis enables the system evaluation to be more objective. During the course improvement process of the machine learning module, the improved partitioning feature matching algorithm is adopted to match the willingness degrees of the students with the courses, so that the correct matching of feature points can be well achieved, the use value is very high, and the matching precision and the calculation speed are improved.

Description

technical field [0001] The invention belongs to the field of big data technology, and in particular relates to a formative optimization system and method for cloud data education and training process. Background technique [0002] With the rapid development of the online education industry, how to build an online course teaching evaluation optimization system and effectively utilize the evaluation mechanism has become an urgent problem to be solved. Because online education cannot carry out face-to-face communication in the classroom and accurately locate students' abilities, there are often problems such as one-way evaluation, neglect of process evaluation, and simple evaluation forms. [0003] Traditional evaluation often focuses on students' final academic performance and ignores the learning process. The formative evaluation shifts part of the focus to the student's learning process, such as students' actions, effects, and willingness in this process, which can help stu...

AI Technical Summary

Problems solved by technology

[0005] (1) The use of big data and machine learning technology in the existing technology leads to the system not being able to quickly reach the best state; it reduces the objectivity of the students' learning status into willingness, conditions, effects, and hierarchical analysis

[0006] (2) In the process of decrypting data in the prior art, the traditional algorithm is used to decrypt the data, which cannot avoid the data being captured and intercepted by illegal elements, which increases the potential risk of the software and reduces the transmission speed

[0007] (3) In the prior art, when the data processor integrates and groups the data from the learning effect judgment module and the student achievement input module and sends them to the intelligent gateway A, the traditional algorithm is used to integrate and classify the data, which cannot solve the problem of existing problems. The phenomenon of inseparable regions and error accumulation reduces the classification accuracy and increases the classification time

[0008] (4) In the existing technology, the traditional algorithm is used to match the degree of students' willingness to the course, which cannot achieve the correct matching of feature points, and reduces the matching accuracy and calculation speed

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