CN-122027818-A - Multi-terminal collaborative remote academic counseling resource scheduling method and system

CN122027818ACN 122027818 ACN122027818 ACN 122027818ACN-122027818-A

Abstract

The application provides a multi-terminal collaborative remote learning coaching resource scheduling method and a system, wherein in the remote learning coaching process, the cognitive load of a current student is identified based on interaction data and coaching scene information in a coaching session request initiated by a coaching terminal to obtain the cognitive load level of the current student, and the cognitive load level and a coaching strategy template of the remote learning coaching are subjected to label matching to obtain a hierarchical scheduling instruction of a main coaching terminal; and carrying out differentiated resource distribution on remote academic coaching resources among the auxiliary coaching terminals through the resource adaptation characteristics in the interactive feedback data of the auxiliary coaching terminals to obtain resource distribution schemes of the auxiliary coaching terminals, and carrying out real-time adjustment on the distribution proportion and the terminal adaptation format in the hierarchical scheduling instruction according to the resource distribution schemes until the remote academic coaching session is ended. Based on the scheme, the differential resource scheduling of cognitive load driving in remote learning coaching can be realized.

Inventors

WANG WEI
HUANG YUXIN
Xu Ziduo
Zhuo Zhenyue

Assignees

海道(深圳)教育科技有限责任公司

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. A multi-terminal collaborative remote academic coaching resource scheduling method is characterized by comprising the following steps: In the remote academic coaching process, a coaching session request initiated by a main coaching terminal is acquired, and interactive feedback data of each auxiliary coaching terminal are periodically acquired; Identifying the cognitive load of the current student based on the interaction data and the coaching scene information in the coaching session request to obtain the cognitive load level of the current student, and performing tag matching on the cognitive load level and a coaching strategy template for remote learning coaching to obtain a hierarchical scheduling instruction of a main coaching terminal; Differential resource distribution is carried out on remote academic coaching resources among the coaching terminals through the resource adaptation characteristics in the interactive feedback data to obtain a resource distribution scheme of the coaching terminals; And adjusting the distribution proportion and the terminal adaptation format in the hierarchical scheduling instruction in real time according to each resource distribution scheme until the remote learning coaching session is ended.
2. The method of claim 1, wherein identifying the current student's cognitive load based on the interaction data and the coaching context information in the coaching session request, the obtaining the current student's cognitive load level specifically comprises: Extracting multi-mode interaction data and coaching scene information from the coaching session request, wherein the interaction data comprises voice frequency spectrum characteristics of a student end, writing track curvature and a screen fixation point thermodynamic distribution diagram; performing multidimensional evaluation on the cognitive load of the current student based on the interaction data, the topic difficulty coefficient in the coaching scene information and the knowledge point association map to obtain probability distribution vectors of multiple cognitive load dimensions; And performing cross-dimension fusion on all probability distribution vectors, and mapping to a preset cognitive load level interval according to a fusion result to obtain the current cognitive load level of the student.
3. The method of claim 1, wherein the performing tag matching on the cognitive load level and a remotely-academic coaching policy template to obtain the hierarchical scheduling instruction of the master coaching terminal specifically comprises: A coaching strategy template library is constructed in advance, and each coaching strategy template in the coaching strategy template library is associated with a cognitive load level interval, an academic tag type and a corresponding resource scheduling priority sequence; performing joint coding on the cognitive load level of the current student and the academic label to obtain the matching degree of each coaching strategy template in the coaching strategy template library, and selecting a target template with the highest matching degree; Analyzing a predefined resource scheduling priority sequence in a target template, generating resource allocation weights of different auxiliary and auxiliary terminal categories, and further obtaining a hierarchical scheduling instruction of the main and auxiliary terminals.
4. The method of claim 1, wherein the performing differential resource distribution on the remote academic tutorial resources between the auxiliary tutorial terminals through the resource adaptation feature in the interactive feedback data to obtain the resource distribution scheme of the auxiliary tutorial terminals specifically comprises: Extracting the resource adaptation characteristics of each auxiliary and auxiliary terminal from the interactive feedback data; clustering and grouping all auxiliary and auxiliary terminals according to the resource adaptation characteristics, wherein the terminals in the same group adopt the same resource encapsulation format, and different groups adopt different resource transmission strategies; And generating a corresponding resource coding code rate, resource slicing time and resource presentation style for each group, thereby obtaining a resource distribution scheme of each auxiliary and auxiliary terminal.
5. The method of claim 1, wherein the real-time adjustment of the distribution ratio and the terminal adaptation format in the hierarchical scheduling instruction according to each resource distribution scheme until the end of the remote academic tutorial session specifically comprises: Extracting resource receiving feedback parameters of each auxiliary and auxiliary terminal from the resource distribution scheme of each auxiliary and auxiliary terminal; When the resource receiving feedback parameter of any auxiliary terminal triggers a preset adjustment threshold, recalculating the optimal distribution proportion and the terminal adaptation format of the resource receiving feedback parameter based on the resource adaptation characteristics of the corresponding auxiliary terminal; And synchronizing the adjusted distribution proportion and the terminal adaptation format to the hierarchical scheduling instruction, updating the resource scheduling parameters of the corresponding terminal, and continuously monitoring feedback data of the next period until the session is ended.
6. The method of claim 1, wherein the primary and secondary terminals are teacher-identity-based teaching master terminals.
7. The method of claim 1, wherein the secondary tutoring terminal is a collaborative participant terminal based on student identity and secondary tutoring identity.
8. The utility model provides a remote academic or vocational study of multi-terminal cooperation is coached and is used resource scheduling system, this remote academic or vocational study of multi-terminal cooperation is coached and is used resource scheduling system including resource distribution unit, characterized in that, resource distribution unit includes: the acquisition module is used for acquiring a coaching session request initiated by the main coaching terminal in the remote learning coaching process and periodically acquiring interactive feedback data of each coaching terminal; The processing module is used for identifying the cognitive load of the current student based on the interaction data and the coaching scene information in the coaching session request to obtain the cognitive load grade of the current student, and carrying out tag matching on the cognitive load grade and a coaching strategy template coaching in a remote learning industry to obtain a hierarchical scheduling instruction of the main coaching terminal; The processing module is also used for carrying out differentiated resource distribution on remote academic coaching resources among the coaching terminals through resource adaptation characteristics in the interactive feedback data to obtain a resource distribution scheme of the coaching terminals; And the execution module is used for adjusting the distribution proportion and the terminal adaptation format in the hierarchical scheduling instruction in real time according to each resource distribution scheme until the remote learning coaching session is ended.
9. A computer device, characterized in that the computer device comprises a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the multi-terminal collaborative tele-academic coaching resource scheduling method according to any one of claims 1 to 7.
10. A computer readable storage medium, wherein instructions or code are stored in the computer readable storage medium, which when executed on a computer, cause the computer to implement the multi-terminal collaborative tele-academic coaching resource scheduling method of any one of claims 1 to 7.

Description

Multi-terminal collaborative remote academic counseling resource scheduling method and system Technical Field The application relates to the technical field of resource scheduling, in particular to a multi-terminal collaborative remote learning coaching resource scheduling method and system. Background The remote learning and guidance means that teaching resources are transmitted in real time to a plurality of auxiliary and guidance terminals at the student end by a teacher through a main and auxiliary guidance terminal by means of an Internet communication technology and intelligent terminal equipment, so that the learning and guidance activities of the teacher and the student in different places and the cooperation of multiple screens are realized, the teacher can conduct knowledge point explanation, homework answering and learning progress tracking, students can receive the explanation resources through different terminals, complete training tasks and obtain real-time feedback, and therefore space-time limitation is broken through, and continuity and individuation of learning and guidance are guaranteed. In the prior art, a uniform resource pushing mode is generally adopted, the allocation strategy of the coaching resources cannot be dynamically adjusted according to the real-time cognitive load state of students, in the remote learning coaching process, often, coaching materials are pushed to all students in a fixed content organization mode and a uniform resource format, the cognitive load born by the students in the learning process is not distinguished, when the students are in a high cognitive load state, the coaching resources with high difficulty and high density are still continuously pushed, the attention of the students is reduced, the understanding is difficult, even learning frustration is caused, when the students are in a low cognitive load state, the resource pushing of a conventional rhythm is still maintained, challenges cannot be timely increased or the expansion content is supplemented, the learning efficiency is low, and therefore, the accurate matching of the resource scheduling strategy and the actual learning state of the students is difficult to realize, and finally the individuation level and the teaching effect of the remote coaching are influenced. Therefore, how to realize the differential resource scheduling of cognitive load driving in remote learning coaching, so that the adaptive efficiency of multi-terminal collaborative coaching can be improved, and the problem facing the industry is solved. Disclosure of Invention The application provides a multi-terminal collaborative remote learning coaching resource scheduling method and a system, which can realize differentiated resource scheduling of cognitive load driving in remote learning coaching, thereby improving the adaptive efficiency of multi-terminal collaborative coaching. In a first aspect, the present application provides a multi-terminal collaborative remote learning tutoring resource scheduling method, including: In the remote academic coaching process, a coaching session request initiated by a main coaching terminal is acquired, and interactive feedback data of each auxiliary coaching terminal are periodically acquired; Identifying the cognitive load of the current student based on the interaction data and the coaching scene information in the coaching session request to obtain the cognitive load level of the current student, and performing tag matching on the cognitive load level and a coaching strategy template for remote learning coaching to obtain a hierarchical scheduling instruction of a main coaching terminal; Differential resource distribution is carried out on remote academic coaching resources among the coaching terminals through the resource adaptation characteristics in the interactive feedback data to obtain a resource distribution scheme of the coaching terminals; And adjusting the distribution proportion and the terminal adaptation format in the hierarchical scheduling instruction in real time according to each resource distribution scheme until the remote learning coaching session is ended. In some embodiments, identifying the cognitive load of the current student based on the interaction data and the coaching scene information in the coaching session request, and obtaining the cognitive load level of the current student specifically includes: Extracting multi-mode interaction data and coaching scene information from the coaching session request, wherein the interaction data comprises voice frequency spectrum characteristics of a student end, writing track curvature and a screen fixation point thermodynamic distribution diagram; performing multidimensional evaluation on the cognitive load of the current student based on the interaction data, the topic difficulty coefficient in the coaching scene information and the knowledge point association map to obtain probability distribution vectors of multiple