Internet art teaching demonstration system

Abstract

The invention relates to the technical field of internet online education, in particular to an internet art teaching demonstration system, which comprises a teaching resource management module, an online interaction module, a personalized recommendation module, a data analysis module and a safety management module, wherein the teaching resource management module is used for storing, classifying and managing art teaching resources; the online interaction module is used for realizing real-time interaction communication; the personalized recommendation module is used for matching art teaching resources and recommending the art teaching resources to corresponding students; the data analysis module is used for analyzing learning conditions and requirements of the students; and the security management module is used for performing authority management and encryption processing on data interaction among the modules. According to the system and the method, the teaching resource management module, the personalized recommendation module, the data analysis module and the safety management module are integrated, so that efficient management, personalized recommendation, real-time interaction and data safety guarantee of art teaching resources are realized, and the quality and the safety of online art teaching are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet online education, and in particular to an Internet art teaching demonstration system. Background Art

[0002] With the development of Internet technology and online education, more and more subject teaching has begun to shift to online mode, and art teaching is no exception. Art teaching not only focuses on the cultivation of students' artistic skills, but also emphasizes improving students' aesthetic ability and creative thinking through interaction, demonstration and resource sharing. In traditional offline art teaching methods, teachers convey knowledge points intuitively through physical displays, interactive demonstrations, etc., but in Internet art teaching, the interaction method between teachers and students is limited by technology. How to make full use of multimedia resources, real-time interactive tools and data analysis methods to achieve high-quality online art teaching has become an important research direction.

[0003] However, the existing Internet art teaching system has technical difficulties in many aspects: first, the resource management lacks systematization, making it difficult to achieve efficient storage and management of art resources; second, the system lacks in-depth analysis of students' personalized needs and cannot dynamically recommend resources based on students' learning history, behavior data, and interest preferences, resulting in poor personalized teaching effects; third, the real-time interaction between teachers and students and between students is limited, lacking rich forms of interaction; finally, the system is generally weak in data security and privacy protection, and fails to effectively prevent unauthorized access and data leakage. These problems have seriously affected the quality of Internet art teaching and hindered students' effective learning. Therefore, an innovative Internet art teaching demonstration system is urgently needed to solve the above problems. Summary of the invention

[0004] Based on the above objectives, the present invention provides an Internet art teaching demonstration system.

[0005] An Internet art teaching demonstration system includes a teaching resource management module, an online interaction module, a personalized recommendation module, a data analysis module and a security management module:

[0006] Teaching resource management module: used to store, classify and manage art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials;

[0007] Online interactive module: connected to the teaching resource management module, used to obtain art teaching resources for use by the online interactive module, so as to realize real-time interactive communication between teachers and students and between students;

[0008] Personalized recommendation module: connected with the online interaction module and the teaching resource management module, it is used to select matching art teaching resources from the teaching resource management module according to the students' learning history, interest preferences and behavior data, and recommend them to the corresponding students through the online interaction module;

[0009] Data analysis module: connected to the online interaction module, personalized recommendation module and teaching resource management module, used to collect students' learning data and interaction data in the online interaction module, analyze students' learning situation and needs, generate learning reports and teaching evaluations, and also feed back the analysis results to the personalized recommendation module to optimize the recommendation strategy;

[0010] Security management module: connected with each module, used for permission management and encryption of data interaction between modules to ensure the security of user information and art teaching resources.

[0011] Optionally, the teaching resource management module includes a resource storage unit, a resource classification unit and a resource management unit; wherein:

[0012] Resource storage unit: used to store art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials, where text teaching materials are stored in PDF format, picture materials are stored in JPEG or PNG format, audio explanations are stored in MP3 format, and video tutorials are stored in MP4 format. Various resources are stored in corresponding databases and can be quickly retrieved through an indexing mechanism;

[0013] Resource classification unit: connected to the resource storage unit, used to classify and store art teaching resources according to their content types, applicable learning stages and teaching objectives, and to mark classification labels, wherein the classification labels include content type labels, learning stage labels and teaching objective labels;

[0014] Resource management unit: connected to the resource classification unit and the resource storage unit, used for updating, deleting and authority management of art teaching resources;

[0015] The resource management unit comprises:

[0016] Upload review subunit: used to review the upload requests for new teaching resources to ensure that the resource content meets the platform standards;

[0017] Resource update subunit: used to update existing resources to ensure the real-time and accuracy of teaching content;

[0018] Permission control subunit: used to set access rights for different users, ensuring that only authorized users can access, modify or delete resources, thereby protecting the security of resources.

[0019] Optionally, the online interaction module includes a resource calling unit, a teacher-student interaction unit and a student interaction unit; wherein:

[0020] Resource calling unit: used to obtain the specified art teaching resources from the teaching resource management module, including text teaching materials, picture materials, audio explanations and video tutorials, and transmit the obtained resources to the teacher-student interaction unit and the student interaction unit in real time to ensure that the teaching resources are called synchronously during the interaction process;

[0021] Teacher-student interaction unit: connected to the resource calling unit, used to support real-time interactive communication between teachers and students;

[0022] The teacher-student interaction unit includes an audio and video call subunit and a screen sharing subunit;

[0023] Audio and video call subunit: used to realize real-time audio or video communication between teachers and students;

[0024] Screen sharing subunit: used by teachers to share screens, demonstrate teaching resources or make real-time annotations during real-time teaching;

[0025] Student interaction unit: connected to the resource calling unit and the teacher-student interaction unit, used to support real-time interactive communication between students;

[0026] The student interaction unit includes a text communication subunit and a collaborative drawing board subunit:

[0027] Text communication subunit: used for students to send text messages to communicate learning content;

[0028] Collaborative Sketchpad Subunit: Allows multiple students to collaboratively draw or annotate on the same sketchpad.

[0029] Optionally, the personalized recommendation module includes a data collection unit, a recommendation algorithm unit and a resource push unit; wherein:

[0030] Data collection unit: used to collect and store students' learning history, interest preferences and behavior data. The learning history includes records of art teaching resources browsed, downloaded and studied by students; interest preferences are generated according to the frequency of students' access to different types of resources; behavior data includes students' interaction records and resource usage habits in the system; the data collection unit also includes aggregating the collected data into personalized learning files for students;

[0031] Recommendation algorithm unit: connected with the data collection unit and the teaching resource management module, it is used to select the art teaching resources that best suit the students' learning needs from the teaching resource management module according to the students' personalized learning profiles. Specifically, it adopts a multi-factor analysis model to combine learning history, interest preferences and behavior data, calculate the matching degree of each teaching resource, and generate a recommendation list based on the matching degree;

[0032] Resource push unit: connected to the recommendation algorithm unit and the online interactive module, used to push the art teaching resources in the recommendation list to the online interactive module and present them to the corresponding students. The resource push unit pushes recommended resources in real time when students log in to the system or enter interactive learning.

[0033] Optionally, the data collection unit includes an interest preference calculation subunit and a learning profile generation subunit; wherein:

[0034] The interest preference calculation subunit is used to generate an interest preference score according to the frequency of students' access to different resource types. The interest preference score is calculated based on the following formula: Among them, P i Score the student's interest preference for the i-th resource type; F i is the number of times a student accesses the i-th resource type in a given time period; n is the total number of times a student accesses all resource types in a given time period;

[0035] Learning profile generation subunit: used to summarize the collected learning history, interest preferences and behavior data into students' personalized learning profiles.

[0036] Optionally, the recommendation algorithm unit includes:

[0037] Data weighting subunit: used to assign weight coefficients to students’ learning history, interest preferences, and behavior data. The weight coefficients are W h , W p and W b , represents the importance of learning history, interest preferences and behavior data in matching calculation;

[0038] Matching degree calculation subunit: used to calculate the matching degree of each teaching resource based on the weighted learning history, interest preference and behavior data; the formula is: M r =W h ·H r +W p ·P r +W b ·B r , where M r is the matching score of the rth teaching resource; W h is the weight coefficient of learning history; H rThe score of the rth resource in the student's learning history; W p is the weight coefficient of interest preference; P r Score the interest preference of the rth resource; W b is the weight coefficient of behavioral data; B r Score the behavioral data of the rth resource;

[0039] Recommendation list generation subunit: used to sort the calculated matching scores of teaching resources, specifically sort all teaching resources according to the matching score M r Sort from high to low, select the first n1 resources with the highest matching degree from the sorted teaching resources to generate a recommendation list, and send the recommendation list to the resource push unit, where n1 is the number of recommended resources.

[0040] Optionally, the system includes a data collection unit, a demand analysis unit, a report generation unit and a result feedback unit; wherein:

[0041] Data collection unit: connected to the online interactive module, used to collect students' learning data and interaction data in the online interactive module; learning data includes records of teaching resources visited by students, usage time and access frequency; interaction data includes the frequency of communication between students and teachers and other students, interaction forms and operation records on the collaborative drawing board; the data collection unit also includes aggregating learning data and interaction data to form an interaction behavior data set;

[0042] Demand analysis unit: connected to the data collection unit, used to perform statistical processing on the interactive behavior data set to identify students' learning preferences, knowledge mastery and learning needs; and to determine students' learning strengths and weaknesses, and generate targeted learning needs assessment results;

[0043] Report generating unit: connected to the demand analysis unit, used to generate a learning report according to the targeted learning demand assessment results, the learning report includes the student's learning progress, resource usage, interaction frequency and knowledge point mastery;

[0044] Result feedback unit: connected with the report generation unit and the personalized recommendation module, it is used to extract students' latest learning needs, resource preferences and behavioral trends in the learning report, and feed them back to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation and realize dynamic optimization of personalized resource recommendations.

[0045] Optionally, the demand analysis unit includes:

[0046] Learning preference identification subunit: used to identify students’ access preferences for different types of art teaching resources based on the interactive behavior data set; count the frequency of students’ access to various resources, and calculate the students’ preference score P for the predetermined resource type based on the proportion of the number of accesses to the total number of accesses i , the calculation formula is: Among them, P i The score for the student's preference for the i-th resource type ranges from 0 to 1; F i is the number of times a student accesses the i-th resource type in a given time period; F total The total number of times students accessed all resource types during the time period;

[0047] Knowledge mastery assessment subunit: used to assess the students' mastery of predetermined knowledge points. The knowledge mastery assessment subunit compares the students' correct answer rate for each knowledge point in the test or exercise with the average correct rate. The calculation formula is: Among them, G k C is the score of the student's mastery of the kth knowledge point; k is the correct rate of students’ answers on the kth knowledge point; C avg is the average correct rate of all students on the kth knowledge point; when the mastery score G k When it is greater than 1, it means that the student's mastery level is above the average; when it is less than 1, it means that it is below the average;

[0048] Learning needs assessment subunit: used to combine learning preference scores and knowledge mastery to judge students' learning strengths and weaknesses. When a student's preference score for a certain type of resource or knowledge point is higher than 0.7, and the mastery score is greater than 1, the corresponding resource type or knowledge point is judged as the student's learning advantage. When a student's preference score for a certain type of resource or knowledge point is higher than 0.7, but the mastery score is less than 1, the corresponding resource type or knowledge point is judged as the student's learning weakness.

[0049] Optionally, the result feedback unit includes:

[0050] Demand extraction subunit: used to extract students' latest learning needs, resource preferences and behavior trends from the generated learning reports;

[0051] The learning needs extraction is to identify the weak knowledge points shown by students in the learning process from the learning reports and take them as the latest learning needs; the resource preference extraction is to calculate the access frequency of each resource type to obtain the resource preference score, and then identify the resource type currently preferred by the students; the resource preference score formula is: Among them, R i Score the student's preference for the i-th resource type; V iis the number of times students visit the i-th resource type; V total is the total number of visits to all resource types by students. The resource type with a higher preference score is the resource type that the student currently prefers.

[0052] The behavior trend analysis is to identify the changing trend of students' learning habits by calculating their interaction frequency and learning progress in the recent learning stage, including the increase or decrease of learning frequency and the tendency of resource use; the behavior trend score calculation formula is: Where T is the behavior trend score; I current The interaction frequency or learning progress of the current learning stage; I previous It is the interaction frequency or learning progress in the previous learning stage; when the T value is greater than 0, it indicates an upward trend in behavior, and when the value is less than 0, it indicates a downward trend in behavior;

[0053] Dynamic feedback subunit: used to feed back the extracted learning needs, resource preferences and behavioral trends to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation.

[0054] Optionally, the security management module includes an authority control unit and an encryption processing unit; wherein:

[0055] Permission control unit: used to manage the permissions of data interactions between modules, ensuring that only authorized modules and users can access the predetermined data; specifically, by defining access control lists, assigning corresponding access permissions to different modules and users to prevent unauthorized access;

[0056] Encryption processing unit: used to encrypt data transmitted between modules to ensure the confidentiality and integrity of data during transmission; specifically, a symmetric encryption algorithm is used to encrypt data and decrypt it at the receiving end to ensure that data is not stolen or tampered with during transmission.

[0057] Beneficial effects of the present invention:

[0058] The present invention realizes the efficient storage, classification and management of art teaching resources through the organic combination of teaching resource management, personalized recommendation, data analysis and security management modules, and meets the personalized learning needs of students at different levels. Through the in-depth analysis of students' learning behaviors and preferences, the system can accurately identify students' learning strengths and weaknesses, and dynamically recommend art resources that meet their needs, which significantly improves the quality of personalized teaching. In addition, the real-time interactive function ensures efficient communication between teachers and students and between students, so that the sense of participation and interactivity of online art teaching is enhanced.

[0059] The present invention provides comprehensive security protection for data interaction by adopting authority management and encryption processing, thereby preventing unauthorized access and data leakage, and improving the overall security and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of an Internet art teaching demonstration system according to an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of a personalized recommendation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0064] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0065] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0066] like Figure 1-Figure 2 As shown, an Internet art teaching demonstration system includes a teaching resource management module, an online interaction module, a personalized recommendation module, a data analysis module and a security management module: wherein:

[0067] Teaching resource management module: used to store, classify and manage art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials;

[0068] Online interactive module: connected with the teaching resource management module, used to obtain art teaching resources for use by the online interactive module, so as to realize real-time interactive communication between teachers and students and between students;

[0069] Personalized recommendation module: connected with the online interaction module and the teaching resource management module, it is used to select matching art teaching resources from the teaching resource management module according to the students' learning history, interest preferences and behavior data, and recommend them to the corresponding students through the online interaction module;

[0070] Data analysis module: connected with the online interaction module, personalized recommendation module and teaching resource management module, used to collect students' learning data and interaction data in the online interaction module, analyze students' learning situation and needs, generate learning reports and teaching evaluations, and also feed back the analysis results to the personalized recommendation module to optimize the recommendation strategy;

[0071] Security management module: connected with each module, used for permission management and encryption of data interaction between modules to ensure the security of user information and art teaching resources.

[0072] The teaching resource management module includes a resource storage unit, a resource classification unit and a resource management unit; wherein:

[0073] Resource storage unit: used to store art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials. Text teaching materials are stored in PDF format, picture materials are stored in JPEG or PNG format, audio explanations are stored in MP3 format, and video tutorials are stored in MP4 format. All kinds of resources are stored in the corresponding database and can be quickly retrieved through the index mechanism;

[0074] Resource classification unit: connected to the resource storage unit, used to classify and store art teaching resources according to their content type, applicable learning stage and teaching objectives, and mark classification labels. The classification labels include content type labels, learning stage labels and teaching objective labels, so as to facilitate subsequent resource retrieval and call;

[0075] Resource management unit: connected with resource classification unit and resource storage unit, used for updating, deleting and authority management of art teaching resources;

[0076] The resource management unit includes:

[0077] Upload review subunit: used to review the upload requests for new teaching resources to ensure that the resource content meets the platform standards;

[0078] Resource update subunit: used to update existing resources to ensure the real-time and accuracy of teaching content;

[0079] Permission control subunit: used to set access rights for different users, ensuring that only authorized users can access, modify or delete resources, and protecting the security of resources; through the specific settings of the above-mentioned resource storage unit, resource classification unit and resource management unit, the teaching resource management module can achieve efficient storage, detailed classification and precise management of art teaching resources, ensuring rapid retrieval of resources, timeliness of content updates and security of permission control, thereby improving the system's resource management efficiency and security assurance capabilities.

[0080] The online interactive module includes a resource calling unit, a teacher-student interaction unit and a student interaction unit; among which:

[0081] Resource calling unit: used to obtain the specified art teaching resources from the teaching resource management module, including text teaching materials, picture materials, audio explanations and video tutorials, and transmit the obtained resources to the teacher-student interaction unit and the student interaction unit in real time to ensure that the teaching resources are called synchronously during the interaction process;

[0082] Teacher-student interaction unit: connected to the resource calling unit to support real-time interactive communication between teachers and students;

[0083] The teacher-student interaction unit includes an audio and video call subunit and a screen sharing subunit;

[0084] Audio and video call subunit: used to realize real-time audio or video communication between teachers and students;

[0085] Screen sharing subunit: used by teachers to share screens, demonstrate teaching resources or make real-time annotations during real-time teaching, so that students can understand and grasp the teaching content;

[0086] Student interaction unit: connected with the resource calling unit and the teacher-student interaction unit to support real-time interactive communication between students;

[0087] The student interaction unit includes a text communication sub-unit and a collaborative drawing board sub-unit:

[0088] Text communication subunit: used for students to send text messages to communicate learning content;

[0089] Collaborative drawing board sub-unit: allows multiple students to collaborate on painting or marking on the same drawing board, which facilitates learning discussions and collaborative practice among students; through the above-mentioned resource calling unit, teacher-student interaction unit and student interaction unit, it can efficiently realize real-time interactive communication between teachers and students and between students, and provide synchronous resource calling and diversified interactive methods, thereby enhancing the participation and interactive effect in the teaching process, and improving the real-time and interactivity of art teaching.

[0090] The personalized recommendation module includes a data collection unit, a recommendation algorithm unit and a resource push unit; among which:

[0091] Data collection unit: used to collect and store students' learning history, interest preferences and behavior data. Learning history includes records of art teaching resources browsed, downloaded and studied by students; interest preferences are generated based on the frequency of students' access to different types of resources; behavior data includes students' interaction records and resource usage habits within the system; the data collection unit also includes aggregating the collected data into students' personalized learning profiles;

[0092] Recommendation algorithm unit: connected with the data collection unit and the teaching resource management module, it is used to select the art teaching resources that best suit the students' learning needs from the teaching resource management module according to the students' personalized learning profiles. Specifically, it adopts a multi-factor analysis model to combine learning history, interest preferences and behavior data, calculate the matching degree of each teaching resource, and generate a recommendation list based on the matching degree;

[0093] Resource push unit: connected to the recommendation algorithm unit and the online interactive module, used to push the art teaching resources in the recommendation list to the online interactive module and present them to the corresponding students. The resource push unit pushes recommended resources in real time when students log in to the system or enter interactive learning, ensuring that personalized recommended resources reach the corresponding students' online interactive interface in a timely manner, thereby improving the accuracy and real-time nature of the recommendation; through the settings of the data collection unit, the recommendation algorithm unit and the resource push unit, it is possible to accurately select art teaching resources that meet their needs based on their learning history, interest preferences and behavioral data, and push them to students in real time through the online interactive module, thereby realizing personalized teaching resource recommendations and improving students' learning efficiency and the system's teaching effect.

[0094] The data collection unit includes an interest preference calculation subunit and a learning profile generation subunit; wherein:

[0095] Interest preference calculation subunit: used to generate interest preference scores based on the frequency of students' access to different resource types. The interest preference scores are calculated based on the following formula: Among them, P i Score the student's interest preference for the i-th resource type; F iis the number of times a student accesses the i-th resource type in a given time period; n is the total number of times a student accesses all resource types in a given time period;

[0096] Learning profile generation subunit: used to summarize the collected learning history, interest preferences and behavior data into students' personalized learning profiles.

[0097] The steps of summarization include:

[0098] Data aggregation: The learning history of students in the system (including the teaching resources learned, access time and frequency), interest preference scores generated by the interest preference calculation sub-unit, and behavioral data (such as the number of interactions and activity) are aggregated into a data set;

[0099] Normalization: Normalize the aggregated data set and map each data item to the [0,1] interval for subsequent unified processing and analysis. The normalization formula is as follows: in, is the normalized value of the i-th data; D i is the original value of the i-th data; D min and D max They are the minimum and maximum values ​​in the data set, respectively, and are used to determine the range of the data;

[0100] Learning profile generation: The normalized data is classified by type to generate personalized learning profiles including student learning preferences, resource usage frequency, and interaction status, which are used for resource matching and push in the subsequent personalized recommendation module; through the setting of the interest preference calculation subunit and the learning profile generation subunit, the data collection unit can calculate the interest preference score based on the student's access frequency to the resource type, and generate a complete personalized learning profile, providing accurate data support for the personalized recommendation module, thereby improving the system's recommendation accuracy and students' learning experience.

[0101] The recommended algorithm unit includes:

[0102] Data weighting subunit: used to assign weight coefficients to students’ learning history, interest preferences, and behavior data. The weight coefficients are W h , W p and W b , represents the importance of learning history, interest preferences and behavior data in the matching calculation; the weight assignment is adjusted according to the student's learning characteristics and the system default parameters to ensure that the impact of each factor on the recommendation is balanced;

[0103] Matching degree calculation subunit: used to calculate the matching degree of each teaching resource based on the weighted learning history, interest preference and behavior data; the formula is: M r =W h ·Hr +W p ·P r +W b ·B r , where M r is the matching score of the rth teaching resource; W h is the weight coefficient of learning history; H r The score of the rth resource in the student's learning history is determined by the student's past frequency of use of the resource or similar resources; W p is the weight coefficient of interest preference; P r Score the interest preference of the rth resource, which is determined based on the frequency of students' access to this type of resource; W b is the weight coefficient of behavioral data; B r The behavioral data score of the rth resource is determined according to the student's behavioral data in the interactive module (such as interaction frequency and operation duration); the matching degree calculation subunit calculates the matching degree score M for each teaching resource in turn. r , and store the score results for subsequent recommendation list generation;

[0104] Recommendation list generation subunit: used to sort the calculated matching scores of teaching resources, specifically sort all teaching resources according to the matching score M r Sort from high to low, select the top n1 resources with the highest matching degree from the sorted teaching resources to generate a recommendation list, and send the recommendation list to the resource push unit, where n1 is the number of recommended resources; through the setting of the data weighting subunit, the matching degree calculation subunit and the recommendation list generation subunit, the recommendation algorithm unit can conduct a comprehensive analysis of the learning history, interest preferences and behavior data and assign weights, so as to accurately calculate the matching degree of each teaching resource, and then generate an optimized recommendation list, which improves the accuracy of personalized recommendations and the effectiveness of learning resource recommendations.

[0105] It includes a data collection unit, a demand analysis unit, a report generation unit and a result feedback unit; among which:

[0106] Data collection unit: connected to the online interactive module, used to collect students' learning data and interaction data in the online interactive module; learning data includes records of teaching resources visited by students, usage time and access frequency; interaction data includes the frequency of communication between students and teachers and other students, interaction forms (such as text, voice, video, etc.) and operation records on the collaborative drawing board; the data collection unit also includes summarizing learning data and interaction data to form an interactive behavior data set to provide a basis for subsequent analysis;

[0107] Demand analysis unit: connected to the data collection unit, used to perform statistical processing on the interactive behavior data set to identify students' learning preferences, knowledge mastery and learning needs; and to determine students' learning strengths and weaknesses, and generate targeted learning needs assessment results;

[0108] Report generation unit: connected to the demand analysis unit, used to generate learning reports based on the results of targeted learning demand assessment. The learning reports include students' learning progress, resource usage, interaction frequency, and knowledge points mastered. The generated reports are used as references for teachers and students for personalized learning guidance.

[0109] Result feedback unit: connected with the report generation unit and the personalized recommendation module, it is used to extract students' latest learning needs, resource preferences and behavioral trends in the learning report, and feed them back to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation and realize dynamic optimization of personalized resource recommendations.

[0110] The requirements analysis unit includes:

[0111] Learning preference identification subunit: used to identify students’ access preferences for different types of art teaching resources based on the interactive behavior data set; count the frequency of students’ access to various resources, and calculate the students’ preference score P for the predetermined resource type based on the proportion of the number of accesses to the total number of accesses i , the calculation formula is: Among them, P i The score for the student's preference for the i-th resource type ranges from 0 to 1; F i is the number of times a student accesses the i-th resource type in a given time period; F total The total number of times students accessed all resource types during the time period;

[0112] Knowledge mastery assessment subunit: used to assess students' mastery of predetermined knowledge points. The knowledge mastery assessment subunit is based on the student's correct answer rate for each knowledge point in the test or exercise, and compares it with the average correct rate. The calculation formula is: Among them, G k C is the score of the student's mastery of the kth knowledge point; k is the correct rate of students’ answers on the kth knowledge point; C avg is the average correct rate of all students on the kth knowledge point; when the mastery score G k When it is greater than 1, it means that the student's mastery level is higher than the average; when it is less than 1, it means that it is lower than the average. This mastery score provides an important reference for learning needs assessment;

[0113] Learning needs assessment subunit: used to combine learning preference scores and knowledge mastery to judge students' learning strengths and weaknesses. When students' preference scores for a certain type of resource or knowledge point are higher than 0.7, and their mastery scores are greater than 1, the corresponding resource type or knowledge point is judged as the student's learning advantage. When students' preference scores for a certain type of resource or knowledge point are higher than 0.7, but their mastery scores are less than 1, the corresponding resource type or knowledge point is judged as the student's learning weakness. By setting up the learning preference identification subunit, the knowledge mastery assessment subunit and the learning needs assessment subunit, the needs analysis unit can comprehensively identify students' learning preferences and knowledge mastery, accurately judge their learning strengths and weaknesses, generate targeted learning needs assessment results, and provide accurate reference for the personalized recommendation module, thereby improving personalized recommendation effects and learning efficiency.

[0114] The result feedback unit includes:

[0115] Demand extraction subunit: used to extract students' latest learning needs, resource preferences and behavior trends from the generated learning reports;

[0116] Learning needs extraction is to identify the weak points of students in the learning process from the learning reports and take them as the latest learning needs; resource preference extraction is to calculate the access frequency of each resource type to obtain the resource preference score, and then identify the resource type currently preferred by the students; the resource preference score formula is: Among them, R i Score the student's preference for the i-th resource type; V i is the number of times students visit the i-th resource type; V total is the total number of visits to all resource types by students. The resource type with a higher preference score is the resource type that the student currently prefers.

[0117] Behavior trend analysis is to identify the changing trend of students' learning habits by calculating their interaction frequency and learning progress in the recent learning stage, including the increase or decrease of learning frequency and the tendency of resource use; the behavior trend score calculation formula is: Among them, T is the behavior trend score, which is used to describe the changes in learning behavior; I current The interaction frequency or learning progress of the current learning stage; I previous It is the interaction frequency or learning progress in the previous learning stage; when the T value is greater than 0, it indicates an upward trend in behavior, and when the value is less than 0, it indicates a downward trend in behavior;

[0118] Dynamic feedback subunit: used to feed back the extracted learning needs, resource preferences and behavioral trends to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation.

[0119] Specific operations include:

[0120] Learning needs feedback: The latest extracted learning needs are passed to the personalized recommendation module to prioritize the resources that meet the needs;

[0121] Resource preference feedback: Based on the resource preference score, the personalized recommendation module is fed back with the student's current preferred resource type to adjust the ratio of recommended resource types;

[0122] Behavioral trend feedback: Based on the results of the behavioral trend scoring, the student’s current learning frequency and tendency are transmitted to the personalized recommendation module, so that the recommendation module can match the student’s current learning rhythm and realize dynamic optimization of the recommended content.

[0123] The security management module includes an authority control unit and an encryption processing unit; wherein:

[0124] Permission control unit: used to manage the permissions of data interactions between modules, ensuring that only authorized modules and users can access the predetermined data; specifically, by defining access control lists (ACLs), corresponding access permissions are assigned to different modules and users to prevent unauthorized access; for example, the teaching resource management module only allows the online interaction module and the personalized recommendation module to access the art teaching resources stored in it under specific permissions;

[0125] Encryption processing unit: used to encrypt data transmitted between modules to ensure the confidentiality and integrity of data during transmission; specifically, a symmetric encryption algorithm (such as AES) is used to encrypt data and decrypt it at the receiving end to ensure that data is not stolen or tampered with during transmission; for example, data transmission between the online interactive module and the data analysis module is encrypted by the encryption processing unit before sending and decrypted by the encryption processing unit after receiving to ensure secure data transmission.

[0126] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0127] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An Internet art teaching demonstration system, characterized in that: It includes teaching resource management module, online interaction module, personalized recommendation module, data analysis module and security management module: Teaching resource management module: used to store, classify and manage art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials; Online interactive module: connected to the teaching resource management module, used to obtain art teaching resources for use by the online interactive module, so as to realize real-time interactive communication between teachers and students and between students; Personalized recommendation module: connected with the online interaction module and the teaching resource management module, it is used to select matching art teaching resources from the teaching resource management module according to the students' learning history, interest preferences and behavior data, and recommend them to the corresponding students through the online interaction module; Data analysis module: connected to the online interaction module, personalized recommendation module and teaching resource management module, used to collect students' learning data and interaction data in the online interaction module, analyze students' learning situation and needs, generate learning reports and teaching evaluations, and also feed back the analysis results to the personalized recommendation module to optimize the recommendation strategy; Security management module: connected with each module, used for permission management and encryption of data interaction between modules to ensure the security of user information and art teaching resources.

2. An Internet art teaching demonstration system according to claim 1, characterized in that: The teaching resource management module includes a resource storage unit, a resource classification unit and a resource management unit; wherein: Resource storage unit: used to store art teaching resources, including text teaching materials, picture materials, audio explanations and video tutorials, where text teaching materials are stored in PDF format, picture materials are stored in JPEG or PNG format, audio explanations are stored in MP3 format, and video tutorials are stored in MP4 format. Various resources are stored in corresponding databases and can be quickly retrieved through an indexing mechanism; Resource classification unit: connected to the resource storage unit, used to classify and store art teaching resources according to their content types, applicable learning stages and teaching objectives, and to mark classification labels, wherein the classification labels include content type labels, learning stage labels and teaching objective labels; Resource management unit: connected to the resource classification unit and the resource storage unit, used for updating, deleting and authority management of art teaching resources; The resource management unit comprises: Upload review subunit: used to review the upload requests for new teaching resources to ensure that the resource content meets the platform standards; Resource update subunit: used to update existing resources to ensure the real-time and accuracy of teaching content; Permission control subunit: used to set access rights for different users, ensuring that only authorized users can access, modify or delete resources, thereby protecting the security of resources.

3. An Internet art teaching demonstration system according to claim 1, characterized in that: The online interaction module includes a resource calling unit, a teacher-student interaction unit and a student interaction unit; wherein: Resource calling unit: used to obtain the specified art teaching resources from the teaching resource management module, including text teaching materials, picture materials, audio explanations and video tutorials, and transmit the obtained resources to the teacher-student interaction unit and the student interaction unit in real time to ensure that the teaching resources are called synchronously during the interaction process; Teacher-student interaction unit: connected to the resource calling unit, used to support real-time interactive communication between teachers and students; The teacher-student interaction unit includes an audio and video call subunit and a screen sharing subunit; Audio and video call subunit: used to realize real-time audio or video communication between teachers and students; Screen sharing subunit: used by teachers to share screens, demonstrate teaching resources or make real-time annotations during real-time teaching; Student interaction unit: connected to the resource calling unit and the teacher-student interaction unit, used to support real-time interactive communication between students; The student interaction unit includes a text communication subunit and a collaborative drawing board subunit: Text communication subunit: used for students to send text messages to communicate learning content; Collaborative Sketchpad Subunit: Allows multiple students to collaboratively draw or annotate on the same sketchpad.

4. An Internet art teaching demonstration system according to claim 1, characterized in that: The personalized recommendation module includes a data collection unit, a recommendation algorithm unit and a resource push unit; wherein: Data collection unit: used to collect and store students' learning history, interest preferences and behavior data. The learning history includes records of art teaching resources browsed, downloaded and studied by students; interest preferences are generated according to the frequency of students' access to different types of resources; behavior data includes students' interaction records and resource usage habits in the system; the data collection unit also includes aggregating the collected data into personalized learning files for students; Recommendation algorithm unit: connected with the data collection unit and the teaching resource management module, it is used to select the art teaching resources that best suit the students' learning needs from the teaching resource management module according to the students' personalized learning profiles. Specifically, it adopts a multi-factor analysis model to combine learning history, interest preferences and behavior data, calculate the matching degree of each teaching resource, and generate a recommendation list based on the matching degree; Resource push unit: connected to the recommendation algorithm unit and the online interactive module, used to push the art teaching resources in the recommendation list to the online interactive module and present them to the corresponding students. The resource push unit pushes recommended resources in real time when students log in to the system or enter interactive learning.

5. An Internet art teaching demonstration system according to claim 4, characterized in that: The data collection unit includes an interest preference calculation subunit and a learning profile generation subunit; wherein: The interest preference calculation subunit is used to generate an interest preference score according to the frequency of students' access to different resource types. The interest preference score is calculated based on the following formula: Among them, P i Score the student's interest preference for the i-th resource type; F i is the number of times a student accesses the i-th resource type in a given time period; n is the total number of times a student accesses all resource types in a given time period; Learning profile generation subunit: used to summarize the collected learning history, interest preferences and behavior data into students' personalized learning profiles.

6. An Internet art teaching demonstration system according to claim 5, characterized in that: The recommendation algorithm unit comprises: Data weighting subunit: used to assign weight coefficients to students’ learning history, interest preferences, and behavior data. The weight coefficients are W h , W p and W b , represents the importance of learning history, interest preferences and behavior data in matching calculation; Matching degree calculation subunit: used to calculate the matching degree of each teaching resource based on the weighted learning history, interest preference and behavior data; the formula is: M r =W h ·H r +W p ·P r +W b ·B r , where M r is the matching score of the rth teaching resource; W h is the weight coefficient of learning history; H r The score of the rth resource in the student's learning history; W p is the weight coefficient of interest preference; P r Score the interest preference of the rth resource; W b is the weight coefficient of behavioral data; B r Score the behavioral data of the rth resource; Recommendation list generation subunit: used to sort the calculated matching scores of teaching resources, specifically sorting all teaching resources according to the matching score M r Sort from high to low, select the first n1 resources with the highest matching degree from the sorted teaching resources to generate a recommendation list, and send the recommendation list to the resource push unit, where n1 is the number of recommended resources.

7. An Internet art teaching demonstration system according to claim 1, characterized in that: The method comprises a data collection unit, a demand analysis unit, a report generation unit and a result feedback unit; wherein: Data collection unit: connected to the online interactive module, used to collect students' learning data and interaction data in the online interactive module; learning data includes records of teaching resources visited by students, usage time and access frequency; interaction data includes the frequency of communication between students and teachers and other students, interaction forms and operation records on the collaborative drawing board; the data collection unit also includes aggregating learning data and interaction data to form an interaction behavior data set; Demand analysis unit: connected to the data collection unit, used to perform statistical processing on the interactive behavior data set to identify students' learning preferences, knowledge mastery and learning needs; and to determine students' learning strengths and weaknesses, and generate targeted learning needs assessment results; Report generating unit: connected to the demand analysis unit, used to generate a learning report according to the targeted learning demand assessment results, the learning report includes the student's learning progress, resource usage, interaction frequency and knowledge point mastery; Result feedback unit: connected with the report generation unit and the personalized recommendation module, it is used to extract students' latest learning needs, resource preferences and behavioral trends in the learning report, and feed them back to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation and realize dynamic optimization of personalized resource recommendations.

8. An Internet art teaching demonstration system according to claim 7, characterized in that: The demand analysis unit comprises: Learning preference identification subunit: used to identify students’ access preferences for different types of art teaching resources based on the interactive behavior data set; count the frequency of students’ access to various resources, and calculate the students’ preference score P for the predetermined resource type based on the proportion of the number of accesses to the total number of accesses i , the calculation formula is: Among them, P i The score for the student's preference for the i-th resource type ranges from 0 to 1; F i is the number of times a student accesses the i-th resource type in a given time period; F total The total number of times students accessed all resource types during the time period; Knowledge mastery assessment subunit: used to assess the students' mastery of predetermined knowledge points. The knowledge mastery assessment subunit compares the students' correct answer rate for each knowledge point in the test or exercise with the average correct rate. The calculation formula is: Among them, G k C is the score of the student's mastery of the kth knowledge point; k is the correct rate of students’ answers on the kth knowledge point; C avg is the average correct rate of all students on the kth knowledge point; when the mastery score G k When it is greater than 1, it means that the student's mastery level is above the average; when it is less than 1, it means that it is below the average; Learning needs assessment subunit: used to combine learning preference scores and knowledge mastery to judge students' learning strengths and weaknesses. When a student's preference score for a certain type of resource or knowledge point is higher than 0.7, and the mastery score is greater than 1, the corresponding resource type or knowledge point is judged as the student's learning advantage. When a student's preference score for a certain type of resource or knowledge point is higher than 0.7, but the mastery score is less than 1, the corresponding resource type or knowledge point is judged as the student's learning weakness.

9. An Internet art teaching demonstration system according to claim 8, characterized in that: The result feedback unit comprises: Demand extraction subunit: used to extract students' latest learning needs, resource preferences and behavior trends from the generated learning reports; The learning needs extraction is to identify the weak knowledge points shown by students in the learning process from the learning reports and take them as the latest learning needs; the resource preference extraction is to calculate the access frequency of each resource type to obtain the resource preference score, and then identify the resource type currently preferred by the students; the resource preference score formula is: Among them, R i Score the student's preference for the i-th resource type; V i is the number of times students visit the i-th resource type; V total is the total number of times students visit all resource types. The resource type with a higher preference score is the resource type that students currently prefer. The behavior trend analysis is to identify the changing trend of students' learning habits by calculating their interaction frequency and learning progress in the recent learning stage, including the increase or decrease of learning frequency and the tendency of resource use; the behavior trend score calculation formula is: Where T is the behavior trend score; I current The interaction frequency or learning progress of the current learning stage; I previous It is the interaction frequency or learning progress in the previous learning stage; when the T value is greater than 0, it indicates an upward trend in behavior, and when the value is less than 0, it indicates a downward trend in behavior; Dynamic feedback subunit: used to feed back the extracted learning needs, resource preferences and behavioral trends to the personalized recommendation module, so that it can adjust the recommendation list based on the latest learning situation.

10. The Internet art teaching demonstration system according to claim 1, characterized in that: The security management module includes an authority control unit and an encryption processing unit; wherein: Permission control unit: used to manage the permissions of data interactions between modules, ensuring that only authorized modules and users can access the predetermined data; specifically, by defining access control lists, assigning corresponding access permissions to different modules and users to prevent unauthorized access; Encryption processing unit: used to encrypt data transmitted between modules to ensure the confidentiality and integrity of data during transmission; specifically, a symmetric encryption algorithm is used to encrypt data and decrypt it at the receiving end to ensure that data is not stolen or tampered with during transmission.

Images