Self-consistent system-based self-adaptive exam sprint online learning method and system

Abstract

The invention relates to the technical field of online learning, in particular to a method and system for self-adaptive pre-examination sprint online learning based on a self-consistent system. The method comprises the following steps: the server sends a set of grading test questions to the user terminal; the user terminal receives and displays the grading test questions, receives user feedback on the grading test questions and sends it to the server; the server generates Grading and evaluation results to determine the initial learning module; the server determines the set of weak points of knowledge ability under the initial learning module according to the preset special diagnosis algorithm, and conducts targeted training on the determined set of weak points of knowledge ability according to the targeted training algorithm , according to the effect evaluation algorithm to evaluate the effect of the user's targeted training, generate the effect evaluation results, and provide accurate interpretation and feedback. The present invention is applied to online learning for exam sprints, and performs grading evaluation, special diagnosis, targeted training and effect evaluation for users, so as to realize in-depth individualized independent learning.

Description

technical field [0001] The invention relates to the technical field of online learning, in particular to an adaptive pre-test sprint online learning method and system based on a self-consistency system. Background technique [0002] Online education refers to the use of computer network technology to carry out online learning, teaching, training and other activities. With the further development of computer network technology and the continuous improvement of people's demand for various knowledge and skills, online education has developed rapidly in recent years. [0003] Conventional online education is mainly based on distance teaching. Teachers and students can teach online in different places through the network. During the teaching process, students can also interact with teachers in question and answer, which is almost the same as traditional face-to-face teaching. However, there are also many problems to be solved in the existing online education model: (1) Students' ...

AI Technical Summary

Problems solved by technology

However, there are also many problems to be solved in the existing online education mode: (1) Students' mastery of subject knowledge and ability depends on excellent teacher resources, and China's excellent teacher resources will be in a state of shortage for a long time; (2) Traditional large classes The systemic teaching mode is difficult to meet the increasingly personalized learning needs of students, resulting in excessive waste of students' learning resources and learning time, and the learning effect and learning efficiency cannot be maximized; (3) Once social emergencies occur, such as the SARS virus , COVID-19 and other similar health events, students need to rely on machines to complete independent learning at home, which can achieve or even exceed the effect of on-site learning in schools; (4) School administrators and teachers lack timely, convenient and accurate learning data and statistical information to comprehensively evaluate The level of learning progress, knowledge mastery, and non-intellectual development of each grade, class, and student

However, the existing technology does not provide a good solution for how to realize the personalization of students' online autonomous deep learning. Students' online autonomous deep learning is usually realized through sea problem tactics or completing tasks uniformly assigned by teachers, ignoring the needs of students. Due to individual differences, targeted intensive training cannot be achieved, and the efficiency is low

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