Auxiliary analysis system for learning interest

Abstract

The invention discloses an auxiliary analysis system for learning interest, and belongs to the technical field of intelligent education analysis software, the system is applied to a first mobile device end and a second mobile device end or a cloud end, the system comprises functional module design applied to different mobile device ends or cloud ends, and learning conditions of users in an area are analyzed through the functional modules. Through a remote control or cloud control mode, the technical design of intelligent guide and directive guide is realized, the real-time performance of analysis can be realized, divergent expansion of knowledge points in a certain region is considered, new technical enlightenment is provided for analysis of learning interest and planning of learning paths, and the learning interest analysis and the learning path planning are more accurate. And a feasible scheme is provided for student management of large schools, middle schools and primary schools.

Description

Technical Field

[0001] The present invention relates to the technical field of smart education analysis software, and in particular to an auxiliary analysis system for learning interests. Background Art

[0002] In modern education, the key factor in truly teaching students in accordance with their aptitude lies in learning planning, that is, it is necessary to plan a learning path or learning method suitable for each student based on the learning situation, learning ability and learning preference of each student. However, in the prior art, such as the learning path planning method, device, equipment and storage medium of CN112825147B, it obtains the knowledge point set to be planned, the learning behavior data of the students to be planned and the mastery degree data of each knowledge point in the knowledge point set of the students to be planned; according to the learning behavior data and the mastery degree data of each knowledge point, at least one of the learning time prediction vector and the learning interest prediction vector and the mastery degree change prediction vector are obtained; at least one of the learning time prediction vector and the learning interest prediction vector and the mastery degree change prediction vector are vector-fused to obtain a comprehensive knowledge point evaluation vector; each knowledge point is sorted according to the size of each dimension of the comprehensive knowledge point evaluation vector to obtain the learning planning path of each knowledge point, which can improve the accuracy and comprehensiveness of the path planning and better improve students' learning efficiency and learning interest. Although this technology improves the accuracy of learning path planning, it uses too many algorithms and the model is too complex. It also requires massive data to support its calculation results. It is a difficult problem to obtain the student's learning interest through a small amount of data and then guide the student's learning plan in real time. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide an auxiliary analysis system for learning interests, aiming to overcome one of the problems in the prior art, such as the model being too complex and the data requirement being too huge.

[0004] In order to achieve the above technical objectives, the present application provides an auxiliary analysis system for learning interests, which is applied to a first mobile device, including: An identity recognition module, used to determine the identity information of a user who logs in to the first mobile device, wherein the user identity information includes the school where the user is studying and the education level of the user; A data collection module, for collecting, based on user identity information, first learning data and non-learning data generated by the user on the first mobile device, as well as a first timestamp corresponding to the first learning data and a second timestamp corresponding to the non-learning data, wherein the first learning data is used to represent data generated when the user is learning on the first mobile device, and the non-learning data is used to represent data generated when the user is performing activities other than learning on the first mobile device; The data analysis module is used to obtain non-learning data related to the first learning data as the second learning data based on the intersection of the first timestamp and the second timestamp, and at the same time obtain the third timestamp corresponding to the second learning data; and analyze the user's learning interest based on the first learning data and / or the second learning data according to the set analysis rules.

[0005] Preferably, the system is also applied to a second mobile device, including: Identity information collection module, used to obtain user identity information; A data interaction module, used to control the second mobile device to interact with the first mobile device, and send the user identity information to the first mobile device through the second mobile device; The control module is used to control the second mobile device to unlock the first mobile device and collect the learning interest of the user generated by the first mobile device.

[0006] Preferably, the system applied to the second mobile device further includes: A data planning module is used to generate third learning data based on the first learning data and user identity information, send the third learning data to the first mobile device, and modify the learning interest according to the impact of the third learning data on the analysis result and the impact analysis result. In the process of modifying the learning interest, the first relationship between the third learning data and the first learning data and / or the second learning data, and the second relationship between the third learning data and non-learning data are analyzed; according to the analysis results of the first relationship and the second relationship, the third learning data is added to the first learning data or the second learning data to modify the learning interest.

[0007] Preferably, the data planning module is also used to add non-learning data associated with the third learning data as fourth learning data to the first learning data or the second learning data according to the analysis result of the second relationship, so as to correct the learning interest.

[0008] Preferably, the data planning module is also used to obtain a target user corresponding to the first learning data based on the first target area corresponding to the user identity information, obtain the third learning data based on the target user, and correct the third learning data based on the first learning data corresponding to the second target area corresponding to the third learning data, wherein in the process of correcting the third learning data, differentiated data between the learning data of the second target area and the third learning data is obtained based on the first learning data, and the third learning data is corrected based on the differentiated data.

[0009] Preferably, the system applied to the second mobile device further includes: An update module is selected to provide optional third learning data and / or fourth learning data, and send the selected third learning data and / or fourth learning data to the first mobile device, and control the data acquisition module to update the first learning data or the second learning data through the third learning data and / or the fourth learning data, wherein the update represents the addition or overwriting of data.

[0010] Preferably, the system applied to the first mobile device further includes: The learning planning module is used to make learning plans for users based on their learning interests. During the learning planning process, the user's learning interests are analyzed based on the updated first learning data and second learning data to determine the impact of non-learning data on the learning interests, and the user's learning plan is adjusted by adjusting the non-learning data.

[0011] Preferably, the learning planning module is also used to analyze the user's learning interest based on the third learning data and the fourth learning data to determine the impact of the learning data in the non-first target area on the user's learning interest, and adjust the user's learning plan by adjusting the first learning data.

[0012] Preferably, the system applied to the second mobile device can be applied to the cloud.

[0013] Preferably, the learning planning module can also be applied in the cloud.

[0014] The present invention discloses the following technical effects: The present invention realizes the technical design of intelligent guidance and directional guidance by remote control or cloud control based on the learning situation of users in the area, and can achieve real-time analysis, while taking into account the divergent expansion of knowledge points in a certain area, providing new technical inspiration for the analysis of learning interests and the planning of learning paths; and providing a feasible solution for the management of students in universities, middle schools and primary schools. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0017] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0018] like Figure 1 As shown, the present invention provides an auxiliary analysis system for learning interests, which is applied to a first mobile device, including: An identity recognition module, used to determine the identity information of a user who logs in to the first mobile device, wherein the user identity information includes the school where the user is studying and the education level of the user; A data collection module, for collecting, based on user identity information, first learning data and non-learning data generated by the user on the first mobile device, as well as a first timestamp corresponding to the first learning data and a second timestamp corresponding to the non-learning data, wherein the first learning data is used to represent data generated when the user is learning on the first mobile device, and the non-learning data is used to represent data generated when the user is performing activities other than learning on the first mobile device; The data analysis module is used to obtain non-learning data related to the first learning data as the second learning data based on the intersection of the first timestamp and the second timestamp, and at the same time obtain the third timestamp corresponding to the second learning data; and analyze the user's learning interest based on the first learning data and / or the second learning data according to the set analysis rules.

[0019] Applying the system on the first mobile device can ensure the user experience. At the same time, the designed functional modules can effectively capture the user's learning situation and judge the user's interest in learning based on the viewing time of learning materials and non-learning materials during the learning process. At the same time, for learning content, since non-learning materials need to be defined based on learning materials, there must be some divergent thinking scenario applications that are not taken into account, that is, the "creative thinking" part, and part of this part is limited to regional cognition, and part of it depends entirely on the user's ability to mobilize his own learning interests. Therefore, for the definition of non-learning materials, correction and verification processes should be adopted to make up for it.

[0020] Further preferably, the auxiliary analysis system for learning interests provided by the present invention is also applied to a second mobile device, including: Identity information collection module, used to obtain user identity information; A data interaction module, used to control the second mobile device to interact with the first mobile device, and send the user identity information to the first mobile device through the second mobile device; The control module is used to control the second mobile device to unlock the first mobile device and collect the learning interest of the user generated by the first mobile device.

[0021] The purpose of setting up an auxiliary analysis system based on learning interest applied to the second mobile device is to realize remote monitoring of users, facilitate remote control personnel to analyze and evaluate users, and facilitate testing of users in different dimensions to explore the true reflection of users' learning interests. The following correction part, that is, multi-dimensional and multi-angle data mining of users, so as to correct non-learning data or update learning data in multiple dimensions. At the same time, it can facilitate the remote control personnel to choose the correction part, that is, to explore the user's learning interest according to the remote control personnel's own will, thereby realizing user learning path planning guided by the remote control personnel and avoiding users from taking more detours in learning.

[0022] Further preferably, the auxiliary analysis system based on learning interest applied to the second mobile device provided by the present invention further includes: A data planning module is used to generate third learning data based on the first learning data and user identity information, send the third learning data to the first mobile device, and modify the learning interest according to the impact of the third learning data on the analysis result and the impact analysis result. In the process of modifying the learning interest, the first relationship between the third learning data and the first learning data and / or the second learning data, and the second relationship between the third learning data and non-learning data are analyzed; according to the analysis results of the first relationship and the second relationship, the third learning data is added to the first learning data or the second learning data to modify the learning interest.

[0023] Further preferably, the data planning module of the auxiliary analysis system based on learning interest applied to the second mobile device provided by the present invention is also used to add the non-learning data associated with the third learning data as the fourth learning data to the first learning data or the second learning data according to the analysis result of the second relationship, so as to correct the learning interest.

[0024] Further preferably, the data planning module of the auxiliary analysis system based on learning interest applied to the second mobile device provided by the present invention is also used to obtain the target user corresponding to the first learning data according to the first target area corresponding to the user identity information, obtain the third learning data according to the target user, and correct the third learning data according to the first learning data corresponding to the second target area corresponding to the third learning data, wherein, in the process of correcting the third learning data, the differentiated data of the learning data of the second target area and the third learning data is obtained according to the first learning data, and the third learning data is corrected according to the differentiated data.

[0025] Further preferably, the auxiliary analysis system based on learning interest applied to the second mobile device provided by the present invention further includes: An update module is selected to provide optional third learning data and / or fourth learning data, and send the selected third learning data and / or fourth learning data to the first mobile device, and control the data acquisition module to update the first learning data or the second learning data through the third learning data and / or the fourth learning data, wherein the update represents the addition or overwriting of data.

[0026] Further preferably, the auxiliary analysis system based on learning interest applied to the first mobile device provided by the present invention further includes: The learning planning module is used to make learning plans for users based on their learning interests. During the learning planning process, the user's learning interests are analyzed based on the updated first learning data and second learning data to determine the impact of non-learning data on the learning interests, and the user's learning plan is adjusted by adjusting the non-learning data.

[0027] Further preferably, the learning planning module of the auxiliary analysis system based on learning interest applied to the first mobile device provided by the present invention is also used to analyze the user's learning interest based on the third learning data and the fourth learning data to determine the impact of the learning data of the non-first target area on the user's learning interest, and adjust the user's learning plan by adjusting the first learning data.

[0028] Further preferably, the auxiliary analysis system based on learning interest applied to the second mobile device provided by the present invention can be applied to the cloud.

[0029] It is understandable that applying the auxiliary analysis system applied on the second mobile device to the cloud can perform data processing and calculation through the cloud, saving the inconvenience on the mobile device, and also helping to analyze and collect big data to improve the iteration speed of analysis accuracy.

[0030] Further preferably, the learning planning module of the auxiliary analysis system based on learning interest applied to the first mobile device provided by the present invention can also be applied to the cloud.

[0031] It is understandable that the function of the learning planning module is a real-time planning based on the calculation results. It is not only real-time, but also has a certain amount of data calculation requirements. At the same time, since the second mobile device or the "auxiliary analysis system" applied to the cloud part will select and update the data at irregular intervals, it brings a lot of unknown calculation results. Therefore, for planning, the factors brought about by the instability of the data should be taken into account. Therefore, applying this module to the cloud can alleviate the real-time adjustments caused by the ordinary changes in data iteration and update, which brings about the poor user experience. In addition, processing in the cloud also saves space on the mobile terminal.

[0032] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0033] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0034] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A learning interest auxiliary analysis system, characterized in that: The system is applied to a first mobile device, including: An identity recognition module, used to determine the identity information of a user logging into the first mobile device, wherein the user identity information includes the school where the user is studying and the education level of the user; A data collection module, configured to collect, based on the user identity information, first learning data and non-learning data generated by the user on the first mobile device, as well as a first timestamp corresponding to the first learning data and a second timestamp corresponding to the non-learning data, wherein the first learning data is used to represent data generated when the user is learning on the first mobile device, and the non-learning data is used to represent data generated when the user is performing activities other than learning on the first mobile device; A data analysis module is used to obtain the non-learning data related to the first learning data as the second learning data based on the intersection of the first timestamp and the second timestamp, and simultaneously obtain the third timestamp corresponding to the second learning data; and analyze the user's learning interest based on the first learning data and / or the second learning data according to the set analysis rules.

2. The auxiliary analysis system for learning interest according to claim 1, characterized in that: The system is also applied to a second mobile device, including: An identity information acquisition module, used to obtain the user identity information; A data interaction module, used to control the second mobile device to interact with the first mobile device, and send the user identity information to the first mobile device through the second mobile device; The control module is used to control the second mobile device to unlock the first mobile device and collect the learning interest of the user generated by the first mobile device.

3. The auxiliary analysis system for learning interest according to claim 2, characterized in that: The system applied to the second mobile device further includes: A data planning module is used to generate third learning data based on the first learning data and the user identity information, send the third learning data to the first mobile device, and modify the learning interest according to the impact of the third learning data on the analysis result and the impact analysis result, wherein, in the process of modifying the learning interest, the first relationship between the third learning data and the first learning data and / or the second learning data, and the second relationship between the third learning data and the non-learning data are analyzed; according to the analysis results of the first relationship and the second relationship, the third learning data is added to the first learning data or the second learning data to modify the learning interest.

4. The auxiliary analysis system for learning interest according to claim 3 is characterized in that: The data planning module is also used to add the non-learning data associated with the third learning data as fourth learning data to the first learning data or the second learning data according to the analysis result of the second relationship, so as to correct the learning interest.

5. The auxiliary analysis system for learning interest according to claim 4, characterized in that: The data planning module is also used to obtain a target user corresponding to the first learning data based on the first target area corresponding to the user identity information, obtain the third learning data based on the target user, and correct the third learning data based on the first learning data corresponding to the second target area corresponding to the third learning data, wherein in the process of correcting the third learning data, differentiated data between the learning data of the second target area and the third learning data is obtained based on the first learning data, and the third learning data is corrected based on the differentiated data.

6. The auxiliary analysis system for learning interest according to claim 5, characterized in that: The system applied to the second mobile device further includes: A selection and update module is used to provide the third learning data and / or the fourth learning data for selection, and send the selected third learning data and / or the fourth learning data to the first mobile device, and control the data acquisition module to update the first learning data or the second learning data through the third learning data and / or the fourth learning data, wherein updating means adding or overwriting data.

7. The auxiliary analysis system for learning interest according to claim 6, characterized in that: The system applied to the first mobile device further includes: A learning planning module is used to make learning plans for users based on their learning interests. During the learning planning process, the learning interests of the users are analyzed based on the updated first learning data and the second learning data to determine the impact of the non-learning data on the learning interests. The learning plan of the user is adjusted by adjusting the non-learning data.

8. The auxiliary analysis system for learning interests according to claim 7, characterized in that: The learning planning module is also used to analyze the user's learning interest based on the third learning data and the fourth learning data to determine the impact of learning data in non-first target areas on the user's learning interest, and adjust the user's learning plan by adjusting the first learning data.

9. The auxiliary analysis system for learning interests according to claim 8, characterized in that: The system applied to the second mobile device can be applied to the cloud.

10. The auxiliary analysis system for learning interests according to claim 9, characterized in that: The learning planning module can also be applied to the cloud.

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