Online intelligent excitation teaching system

Abstract

The invention discloses an online intelligent incentive teaching system. The system comprises a client port and a remote server. The remote server comprises an information acquisition module, a modelprocessing module, an effect simulation module, an excitation module, a feedback module and a database module; the information acquisition module reads the client port to obtain learning data of the student user; the model processing module is used for generating a learning state value S, an action factor value A and a value factor value V; the effect simulation module is used for substituting thelearning state value S, the action factor value A and the value factor value V into a pre-stored algorithm to obtain a target score BS and target spent time BT; the excitation module substitutes theBS, BT and learning data into a pre-stored algorithm to obtain an excitation index Ex; and the feedback module calls a feedback material corresponding to the Ex and sends the feedback material to theclient port. By simulating the learning state of the student, the student user is helped to clarify the learning expectation, the learning achievement feeling of the student user is improved, and thelearning incentive effect is optimized.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an online intelligent incentive teaching system. Background technique [0002] With the advancement of technology, online intelligent teaching has become a common teaching method. In this field, setting an incentive mechanism is an important part of improving students' learning motivation. The incentive mechanism adopted by the existing online teaching system is to use the user's historical data to extract the model relationship between incentive rules and incentive trigger thresholds. The problem with this incentive method is that the regularity of its incentive mechanism is relatively obvious. After mastering the rules, students will deliberately perform pseudo-operations in order to obtain incentives, thus weakening the effect of the original intention of learning. At the same time, as the learning time continues, if the incentive rules cannot be continuo...

AI Technical Summary

Problems solved by technology

The problem with this incentive method is that the regularity of the incentive mechanism is relatively obvious. After mastering the rules, students will deliberately perform pseudo-operations in order to obtain incentives, thus weakening the effect of the original intention of learning.

At the same time, as the learning time continues, if the incentive rules cannot be continuously enriched, the incentive effect will gradually decrease

Images