Intelligent adaptive operation system suitable for OMO learning scene

Abstract

The invention discloses an intelligent adaptive operation system suitable for an OMO learning scene. The intelligent adaptive operation system comprises a field device and a remote server communicating with the field device. The field device comprises an acquisition unit, a publishing unit and a submitting unit; the remote server comprises a resource module, a recommendation module and an arrangement module; the acquisition unit is used for acquiring classroom learning data; the publishing unit is used for publishing a job task; the submitting unit is used for submitting homework results of students; the resource module is used for storing job resources; the recommendation module is used for intelligently recommending an operation task sequence; and the arrangement module is used for arranging operation tasks. According to the invention, the isolated state of online and offline learning data can be broken through, the data in the whole teaching and learning process are integrated, andthe matching degree of intelligent recommendation homework is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent education, and in particular relates to an intelligent adaptive operation system suitable for OMO learning scenes. Background technique [0002] OMO is the abbreviation of Online-Merge-Offline. OMO learning scene refers to the comprehensive integration of online learning scene and offline learning scene, so that online and offline learning data can be integrated into an ecological system. The learning scene of OMO has many aspects, for example For example: 1) Students learn through face-to-face teaching by teachers in offline classrooms, and put part of the learning links online. For example, after class, teachers can practice online performance for students in offline classrooms after completing homework on the online learning platform. Feedback, 2) The dual-teacher classroom mode is that famous teaching teachers give lectures online, and at the same time, there are offline tutors in the clas...

AI Technical Summary

Problems solved by technology

[0003] The existing technology is usually that the teacher objects can automatically distribute, collect, correct and count through the assignment system. Although this method reduces the workload of the teacher object for correcting homework, it is difficult to consider individual students' learning when assigning homework. state, so as to achieve personalized consolidation exercises. The existing technology requires teachers to manually summarize and form homework papers in a large number of databases according to students' daily homework conditions; through the intelligent homework arrangement of intelligent adaptive education, students can only focus on their own weak points. Homework exercises are carried out locally, but due to the isolated existence of online and offline learning data, in the OMO learning scene, before the students complete the online homework, they must first pass the knowledge point test to obtain a list of weak knowledge points that each student needs to learn. On the contrary, it increases the amount of questions that students need to do, causing them to resist psychology. Therefore, it is necessary to fully integrate the learning process data obtained from online learning scenarios and offline learning scenarios according to OMO learning scenarios, so as to improve the matching degree of intelligent recommended tasks

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