Intelligent collaborative learning platform for education and teaching and real-time interactive guiding method

Abstract

The invention relates to the technical field of online education, and discloses an intelligent collaborative learning platform for education and teaching and a real-time interaction guiding method, and the platform comprises a learning data collection module which is used for collecting learning behavior data of students and transmitting the learning behavior data to a rear end for analysis; the learning intelligent engine module is used for generating a personalized learning path according to the learning data of the student and dynamically adjusting the learning content; the real-time interactive feedback module is used for analyzing the input of the students in real time and generating feedback; the collaborative learning platform module supports the students to carry out interdisciplinary and cross-region collaborative learning; the learning evaluation and monitoring module is used for monitoring the learning progress of the student and evaluating the learning effect of the student; and the user management and authority control module is used for managing identities and access authorities of platform users. According to the invention, through the learning intelligent engine module, the personalized learning path is automatically generated according to the real-time learning behavior data of the student, and the recommended content is dynamically adjusted, so that the learning efficiency and the learning result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of online education, and particularly to an intelligent collaborative learning platform for education and teaching and a real-time interactive guidance method. Background Art

[0002] With the rapid development of information technology, online education platforms have become an important part of modern education. Traditional online learning platforms usually rely on preset learning paths and fixed course content, and students mostly learn at the same pace during the learning process. These platforms use basic interactive tools (such as video playback, discussion forums, test questions, etc.) to provide teaching content for students.

[0003] However, most traditional online learning platforms use fixed courses and learning paths, and adopt a unified progress and content recommendation for all students, which may make some students feel that the learning tasks are too simple or too difficult, thus affecting learning motivation and effect. Moreover, existing platforms often fail to track students' learning situations in real time, nor can they dynamically adjust learning resources according to students' specific needs, resulting in students not receiving timely help or learning content adjustment when encountering difficulties. Therefore, the existing technology is difficult to meet the diverse learning needs of students and cannot provide personalized learning paths, thereby affecting students' learning efficiency and learning outcomes. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides an intelligent collaborative learning platform for education and teaching and a real-time interactive guidance method, aiming to improve the problem that traditional online learning platforms adopt a unified progress and content recommendation for all students, which may make some students feel that the learning tasks are too simple or too difficult.

[0005] In a first aspect, the present invention provides the following technical solution. An intelligent collaborative learning platform for education and teaching includes:

[0006] A learning data collection module for collecting students' learning behavior data and transmitting it to the backend for analysis;

[0007] A learning intelligent engine module for generating personalized learning paths according to students' learning data and dynamically adjusting learning content;

[0008] A real-time interactive feedback module for analyzing students' inputs in real time and generating feedback;

[0009] A collaborative learning platform module that supports students to conduct cross-disciplinary and cross-regional collaborative learning and provides real-time interactive tools;

[0010] A learning assessment and monitoring module for monitoring students' learning progress and evaluating their learning effects;

[0011] The user management and permission control module is used to manage the identities and access permissions of platform users and is responsible for data security.

[0012] Preferably, the learning data collection module includes the following units:

[0013] The front-end behavior data capture unit, through scripts embedded in the platform pages, captures in real time various behaviors of students during the learning process, including clicks, video viewing, and assignment submission, and sends the collected data to the back-end data storage service through requests;

[0014] The learning progress recording unit marks the current progress of students in the learning module through the front end, including the current module, task completion status, and learning duration, and uses requests to update it to the back-end database in real time, responsible for the real-time synchronization of students' learning progress;

[0015] The interaction event recording unit listens to the text and voice input by students and transmits these interaction events to the real-time data processing module through communication protocols for subsequent analysis.

[0016] Preferably, the learning intelligent engine module includes the following units:

[0017] The student behavior analysis unit calculates and analyzes the behavior data of students, evaluates the learning status and performance of students, including learning duration, task completion rate, and correct rate indicators, and establishes a behavior analysis model in combination with learning rules;

[0018] The learning path generation unit automatically generates a personalized learning path suitable for the current learning status of students based on the results of student behavior analysis, and pushes it to the front end for display through a data interface, enabling students to obtain learning content that meets their personal needs;

[0019] The intelligent prediction and adjustment unit collects the learning behavior data of students, including the number of course clicks, video viewing duration, assignment submission records, answer correct rate, and learning time distribution, and processes the data using a deep learning model, constructs a time series learning model using a long short-term memory network or a gated recurrent unit, and predicts the future learning progress of students;

[0020] The intelligent prediction and adjustment unit analyzes the knowledge mastery level, learning habits, and difficulties encountered by students, calculates the learning trajectory of students, and automatically adjusts the learning path based on the prediction results to optimize the recommendation of learning content;

[0021] The automatic adjustment of the learning path includes increasing or decreasing the difficulty level of learning tasks, pushing targeted supplementary resources, and combining personalized recommendation algorithms based on content recommendation and collaborative filtering to recommend learning materials that match the learning needs of students, making the learning content suitable for the current abilities and interests of students;

[0022] The intelligent prediction and adjustment unit adopts the reinforcement learning method. After the student completes a new learning task, it collects new learning behavior data in real time and inputs it into the prediction model for training and optimization, so that the learning path recommendation can continuously adapt to the student's learning progress;

[0023] The intelligent prediction and adjustment unit deploys a deep learning model through a cloud server and combines edge computing technology to improve the prediction operation efficiency, and can adjust the learning path in real time and provide dynamic learning feedback.

[0024] Preferably, the real-time interaction feedback module includes the following units:

[0025] A speech recognition unit, which converts the student's voice input into text through an integrated speech recognition service and pushes it to the natural language processing unit after processing;

[0026] A natural language processing unit, which performs sentiment analysis and understanding on the text input by the student based on semantic analysis technology, extracts the student's emotional state and understanding level from it, and generates personalized feedback in combination with the knowledge base;

[0027] A feedback generation unit, which generates feedback information based on the semantic and sentiment analysis results of the student's input and provides it to the student in the form of text or voice through a push system, and is responsible for the effectiveness of real-time interaction.

[0028] Preferably, the collaborative learning platform module includes the following units:

[0029] A real-time chat and messaging system unit, which uses an instant messaging protocol to transmit text and voice messages between teachers and students, and the messaging system uses a caching mechanism to optimize the message transmission speed and stability;

[0030] A video conferencing and discussion system unit, which uses streaming media communication technology to support real-time video discussions among students and provides screen sharing and document sharing to enhance learning interactivity;

[0031] A collaborative editing function unit, which performs multi-person synchronous editing of documents based on a real-time database and allows teachers to give real-time guidance and annotations.

[0032] Preferably, the learning evaluation and monitoring module includes the following units:

[0033] A progress tracking and analysis unit, which collects the student's learning data and generates a learning progress analysis report based on a calculation model. The report includes information on task completion and knowledge mastery;

[0034] An automatic evaluation and grading unit, in combination with an automatic grading system, calculates and grades students' homework and test results, and generates structured evaluation data to support teachers in reviewing and adjusting teaching plans;

[0035] A report generation and improvement suggestion unit generates learning reports based on evaluation results and provides personalized learning improvement suggestions through text analysis methods, enabling students to obtain targeted learning optimization plans.

[0036] Preferably, the user management and permission control module includes the following units:

[0037] An identity authentication unit uses an authentication protocol to authenticate students, teachers, and administrators and provides a multi-factor authentication mechanism;

[0038] A permission management unit sets access permissions for users of different roles based on the role-based access control model, enabling users to only access data and functions within their authorized scope;

[0039] A data encryption and privacy protection unit encrypts and stores users' sensitive data and uses an encrypted communication protocol to protect data transmission security, preventing information leakage and illegal access.

[0040] In a second aspect, the present invention provides the following technical solution, a real-time interaction guidance method based on an intelligent collaborative learning platform, including the following steps:

[0041] S1. Collect students' learning behavior data through a learning data collection module, including learning duration, task completion status, and interaction status, and transmit the data to the background for processing in real time;

[0042] S2. In the learning intelligent engine module, based on the analysis results of students' learning data, generate personalized learning paths, recommend content suitable for students' current learning status, and at the same time predict students' future learning performance and make dynamic adjustments;

[0043] S3. Through the real-time interaction feedback module, analyze students' voice or text input in real time, generate personalized learning feedback based on natural language processing and sentiment analysis technologies, and push it to students immediately in the form of text or voice;

[0044] S4. In the collaborative learning platform module, support real-time collaboration among students, and promote interaction and knowledge sharing among students through a real-time messaging system, a video conferencing system, and a collaborative editing function;

[0045] S5. Through the learning evaluation and monitoring module, track students' learning progress in real time, analyze their learning effects, and generate personalized learning reports and improvement suggestions;

[0046] S6. Through the user management and permission control module, it is responsible for the identity authentication and data security of students, teachers, and administrators, controls the access permissions of the platform, and ensures data privacy.

[0047] In a third aspect, the present invention provides the following technical solution. A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned real-time interactive guidance method based on the intelligent collaborative learning platform.

[0048] In a fourth aspect, the present invention provides the following technical solution. A readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned real-time interactive guidance method based on the intelligent collaborative learning platform.

[0049] The present invention has the following beneficial effects:

[0050] 1. In the present invention, through the learning intelligent engine module, personalized learning paths are automatically generated according to the real-time learning behavior data of students, and the recommended content is dynamically adjusted. This process can ensure that each student learns in the content that best suits their learning progress and ability level, avoiding the limitations of the "one-size-fits-all" approach in traditional teaching methods, enabling students to efficiently master knowledge points at their own pace. Through intelligent recommendation, students can obtain more accurate learning resources, thereby improving learning efficiency and learning outcomes.

[0051] 2. In the present invention, the real-time interactive feedback module can provide personalized feedback immediately according to the input (text or voice) of students through technologies such as speech recognition and natural language processing. This module can not only help students solve academic problems, but also, through sentiment analysis technology, identify the emotional changes of students and make corresponding feedback. For example, when a student shows learning confusion or frustration, the system will automatically provide encouraging or soothing feedback, reducing the student's sense of frustration and enhancing learning motivation. This immediate and emotional support helps students maintain continuous learning motivation.

[0052] 3. In the present invention, the collaborative learning module of the platform breaks through the limitations of time and space, and students can interact with classmates and teachers in real time anytime and anywhere. Through functions such as group discussions, shared documents, and video conferences, students can not only cooperate on the basis of individual learning, exchange opinions with each other, but also learn different learning experiences and perspectives from students in different regions. This collaborative learning promotes knowledge sharing, teamwork, and the improvement of problem-solving abilities among students, and enhances their social interaction and communication skills.

[0053] 4. In the present invention, through the learning evaluation and monitoring module, the platform can track the learning progress of students in real time and automatically evaluate their learning effects. This module can not only help teachers discover students' learning problems in a timely manner, but also provide students with detailed learning feedback and improvement suggestions. Through this real-time evaluation, teachers can adjust teaching strategies more precisely, while students can obtain personalized guidance and room for improvement to help them make up for weak links in learning more effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a system architecture diagram of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0055] Figure 2 It is an architecture diagram of the learning data acquisition module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0056] Figure 3 It is an architecture diagram of the learning intelligent engine module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0057] Figure 4 It is an architecture diagram of the real-time interaction feedback module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0058] Figure 5 It is an architecture diagram of the collaborative learning platform module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0059] Figure 6 It is an architecture diagram of the learning evaluation and monitoring module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0060] Figure 7 It is an architecture diagram of the user management and permission control module of the intelligent collaborative learning platform for education and teaching proposed by the present invention;

[0061] Figure 8 It is a flowchart of the real-time interaction guidance method based on the intelligent collaborative learning platform proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0063] Embodiment 1

[0064] Refer to Figures 1-7, in the first embodiment of the present invention, the present invention provides an intelligent collaborative learning platform for education and teaching, including:

[0065] A learning data collection module for collecting students' learning behavior data and transmitting it to the backend for analysis;

[0066] A learning intelligent engine module for generating personalized learning paths based on students' learning data and dynamically adjusting learning content;

[0067] A real-time interaction feedback module for analyzing students' inputs in real time and generating feedback;

[0068] A collaborative learning platform module that supports students to conduct cross-disciplinary and cross-regional collaborative learning and provides real-time interaction tools;

[0069] A learning assessment and monitoring module for monitoring students' learning progress and evaluating their learning effects;

[0070] A user management and permission control module for managing the identities and access permissions of platform users and responsible for data security.

[0071] Specifically, the intelligent collaborative learning platform for education and teaching greatly improves students' learning efficiency and sense of participation through functions such as personalized learning path recommendation, real-time interaction feedback, collaborative learning, and learning progress tracking. The platform uses an intelligent engine to automatically generate personalized learning paths based on students' learning behaviors and dynamically adjusts the recommended content to ensure that each student can learn within a suitable pace and ability range. At the same time, the real-time interaction feedback module provides personalized learning guidance for students in a timely manner through sentiment analysis technology, reducing confusion and frustration in learning and enhancing the motivation to learn. The collaborative learning function further promotes cross-regional interaction and knowledge sharing. Students can not only improve their teamwork ability in group cooperation but also solve problems with the help of collective wisdom. The learning assessment and monitoring module tracks students' learning progress and effects in real time, provides feedback and improvement suggestions in a timely manner, and helps students continuously adjust their learning strategies during the learning process, thus achieving more efficient learning results. These innovative functions complement each other and jointly promote an educational model of personalized learning and efficient interaction.

[0072] The learning data collection module includes the following units:

[0073] A front-end behavior data capture unit that, through scripts embedded in the platform page, captures various behaviors of students during the learning process in real time, including clicks, video viewing, and assignment submission, and sends the collected data to the backend data storage service through a request;

[0074] The learning progress record unit marks the current progress of students in the learning module through the front end, including the current module, task completion status, and learning duration, and uses the request method to update it to the back-end database in real time, responsible for the real-time synchronization of students' learning progress;

[0075] The interaction event record unit listens to the text and voice input by students and transmits these interaction events to the real-time data processing module through the communication protocol for subsequent analysis.

[0076] Specifically, the main task of the learning data collection module is to collect the learning behavior data of students on the platform in real time, ensuring that the platform can dynamically adjust the learning path according to the learning situation of students.

[0077] Data collection sources: The platform will embed data collection scripts in each learning activity participated by students. These scripts record by listening to students' click behaviors, learning durations, homework completion status, video viewing progress, etc.

[0078] Technical implementation: The front end integrates event listeners through JavaScript or other front-end scripting languages to capture students' interaction data in real time. These data are immediately transmitted to the background server using the AJAX or WebSocket protocol.

[0079] Data storage: The collected data will be transmitted to the back-end system through the API and stored in a distributed database (such as MongoDB, MySQL) to ensure data scalability and fast query.

[0080] Beneficial effects

[0081] Real-time performance: By collecting data in real time, the platform can accurately capture each learning behavior of students, ensuring the timeliness of data.

[0082] Accurate analysis: It can provide complete data support for subsequent analysis and provide a basis for the recommendation of personalized learning paths.

[0083] The learning intelligent engine module includes the following units:

[0084] The student behavior analysis unit calculates and analyzes the behavior data of students, evaluates the learning status and performance of students, including learning duration, task completion rate, and correct rate indicators, and establishes a behavior analysis model in combination with learning laws;

[0085] The learning path generation unit automatically generates a personalized learning path suitable for the current learning status of students based on the results of the student behavior analysis, and pushes it to the front end for display through the data interface, enabling students to obtain learning content that meets their personal needs;

[0086] The intelligent prediction and adjustment unit collects students' learning behavior data, including the number of course clicks, video viewing duration, homework submission records, question answering accuracy, and learning time distribution, and uses a deep learning model to process the data. It constructs a time series learning model using long short-term memory networks or gated recurrent units to predict students' future learning progress.

[0087] The intelligent prediction and adjustment unit analyzes students' knowledge mastery, learning habits, and difficulties encountered, calculates students' learning trajectories, and automatically adjusts the learning path based on the prediction results to optimize the learning content recommendation.

[0088] Automatically adjusting the learning path includes increasing or decreasing the difficulty level of learning tasks, pushing targeted supplementary resources, and combining personalized recommendation algorithms based on content recommendation and collaborative filtering to recommend learning materials that match students' learning needs, making the learning content suitable for students' current abilities and interests.

[0089] The intelligent prediction and adjustment unit uses the reinforcement learning method. After students complete new learning tasks, it collects new learning behavior data in real time and inputs it into the prediction model for training and optimization, enabling the learning path recommendation to continuously adapt to students' learning progress.

[0090] The intelligent prediction and adjustment unit deploys a deep learning model through a cloud server and combines edge computing technology to improve the prediction operation efficiency, enabling real-time adjustment of the learning path and providing dynamic learning feedback.

[0091] Specifically, the learning intelligent engine module analyzes the collected student data, generates a personalized learning path, and dynamically adjusts it according to the learning progress.

[0092] Personalized learning path generation: Use machine learning algorithms (such as decision trees, KNN) to classify and cluster students' behaviors to identify students' learning patterns. According to students' learning goals and current progress, the intelligent engine recommends the most suitable learning resources.

[0093] Intelligent prediction and adjustment: Based on students' historical behavior data, the intelligent engine uses time series analysis (such as deep learning models like LSTM, GRU) to predict students' future learning situations, discover potential learning difficulties in advance, and dynamically adjust the recommendation of learning content.

[0094] Beneficial effects

[0095] Adaptive learning: Through intelligent prediction and learning path adjustment, the platform can always provide students with the most suitable learning tasks, avoiding students from being exposed to overly difficult content too early or repeating learning knowledge that has already been mastered.

[0096] Improve learning efficiency: Ensure that learning resources are adapted to the student's current learning state, avoid learning bottlenecks, and enhance learning efficiency and effectiveness.

[0097] The real-time interactive feedback module includes the following units:

[0098] The speech recognition unit, by integrating a speech recognition service, converts the student's speech input into text, processes it, and pushes it to the natural language processing unit;

[0099] The natural language processing unit performs sentiment analysis and understanding on the text input by the student based on semantic analysis technology, extracts the student's emotional state and understanding level from it, and generates personalized feedback in combination with the knowledge base;

[0100] The feedback generation unit generates feedback information based on the semantic and sentiment analysis results of the student's input, and provides it to the student in the form of text or speech through a push system, responsible for the effectiveness of real-time interaction.

[0101] Specifically, the real-time interactive feedback module can analyze the student's text or speech input, generate feedback in real time, and push it.

[0102] Speech recognition: The platform integrates a speech recognition service (such as Google Speech API or Microsoft Azure) to convert the student's speech into text. This text will be sent to the natural language processing module for analysis.

[0103] Natural language processing: Use NLP technology (such as spaCy, BERT model) to perform semantic understanding and sentiment analysis on the student's input, and generate personalized feedback according to the student's emotional state and learning understanding level.

[0104] Feedback generation: According to the analysis results, the system feeds back to the student in text or speech. For example, if the student answers wrong, the system will provide step-by-step hints; if the student does not understand clearly, the system will provide more learning resources.

[0105] Beneficial effects

[0106] Immediate feedback: Students can receive feedback immediately, whether it is the analysis of the answer result or the advice on learning attitude, ensuring that students are not troubled during the learning process.

[0107] Emotional support: Through sentiment analysis, the platform can understand the student's emotional changes in real time, provide appropriate motivation or relieve emotions, and enhance the sense of participation and enthusiasm in learning.

[0108] The collaborative learning platform module includes the following units:

[0109] A real-time chat and messaging system unit, using instant messaging protocols, conducts text and voice message transmissions between teachers and students. The messaging system adopts a caching mechanism to optimize the message delivery speed and stability;

[0110] A video conferencing and discussion system unit, using streaming media communication technology, supports real-time video discussions among students and provides screen sharing and document sharing to enhance learning interactivity;

[0111] A collaborative editing function unit, based on a real-time database, enables multiple people to synchronously edit documents and allows teachers to provide real-time guidance and annotations.

[0112] Specifically, the collaborative learning platform module provides tools for students to engage in cross-time and space collaborative learning, promoting interaction and teamwork among students.

[0113] Real-time messaging system: The platform implements real-time chat functions through the WebSocket protocol, supporting text and voice communication among students to solve problems encountered during the learning process.

[0114] Video conferencing system: Students can use the audio and video functions provided by the platform for real-time discussions and virtual classrooms. It supports functions such as video conferencing, screen sharing, and file transfer, helping students and teachers communicate and cooperate efficiently.

[0115] Collaborative editing function: The platform provides online document editing tools, supporting students to jointly edit, discuss assignments, share materials, etc. Through a real-time synchronization mechanism, it ensures that multiple students can operate simultaneously, improving team collaboration efficiency.

[0116] Beneficial effects

[0117] Cross-regional collaboration: Students can participate in learning with others anywhere, breaking through the time and space limitations of traditional education.

[0118] Enhanced sense of participation: Through interaction with peers and teachers, students enhance the social nature of learning, boosting learning motivation and enthusiasm.

[0119] Cultivation of collaboration ability: Through cooperative learning, students can not only acquire knowledge but also improve communication and collaboration abilities in team cooperation.

[0120] The learning assessment and monitoring module includes the following units:

[0121] Progress tracking and analysis unit, by collecting students' learning data and generating a learning progress analysis report based on a computational model. The report contains information on task completion and knowledge mastery;

[0122] The automatic evaluation and scoring unit, in combination with the automatic scoring system, calculates and grades the students' homework and test results, and generates structured evaluation data to support teachers in reviewing and adjusting teaching plans;

[0123] The report generation and improvement suggestion unit generates learning reports based on the evaluation results and provides personalized learning improvement suggestions through text analysis methods, enabling students to obtain targeted learning optimization plans.

[0124] Specifically, the learning evaluation and monitoring module monitors the students' learning progress and provides evaluation and feedback.

[0125] Learning progress tracking: Real-time collection of students' activity records in the learning platform, generating learning progress reports for teachers and students to view. Through learning progress analysis, teachers can understand the students' learning status in real time and intervene promptly.

[0126] Automatic evaluation system: The platform grades students' homework and tests through an automated scoring system. Combining artificial intelligence technology, the system can identify patterns where students repeatedly make mistakes on certain questions and provide targeted feedback.

[0127] Report generation and suggestions: The system generates personalized learning reports based on students' learning data, provides suggestions and improvement directions to help students make up for their weaknesses in subsequent learning.

[0128] Beneficial effects

[0129] Personalized learning reports: Reports and learning suggestions tailored for each student to help students identify deficiencies in learning and take measures.

[0130] Timely intervention: Teachers can grasp the students' learning progress and understanding in real time, conduct timely and effective intervention, and improve teaching quality.

[0131] The user management and permission control module includes the following units:

[0132] The identity authentication unit uses an authentication protocol to authenticate students, teachers, and administrators and provides a multi-factor authentication mechanism;

[0133] The permission management unit sets access permissions for different role users based on the role-based access control model, enabling users to only access data and functions within their authorized scope;

[0134] The data encryption and privacy protection unit encrypts and stores users' sensitive data and uses an encrypted communication protocol to protect data transmission security, preventing information leakage and unauthorized access.

[0135] Specifically, the user management and permission control module ensures the security of the platform and reasonably manages the access permissions of students, teachers, and administrators.

[0136] Identity authentication: The platform integrates authentication protocols such as OAuth2.0 or JWT, and supports multiple authentication methods (such as account password, third-party authentication, etc.).

[0137] Permission management: Based on the RBAC (Role-Based Access Control) model, different access permissions are set according to different user roles (students, teachers, administrators) to ensure the security of data and functions.

[0138] Data encryption and privacy protection: The platform encrypts users' personal information and learning data to ensure the protection of students' privacy and compliance with data protection regulations.

[0139] Beneficial effects

[0140] Data security: Ensure the security of students' and teachers' personal data and learning data, and prevent data leakage.

[0141] Flexible permission management: Can precisely control different users' access permissions to platform data and functions according to their roles, ensuring the efficient operation and security of the platform.

[0142] Embodiment 2:

[0143] Refer to Figure 8 , in the second embodiment of the present invention, the present invention provides a real-time interactive guidance method based on an intelligent collaborative learning platform, including the following steps:

[0144] S1. Collect students' learning behavior data through the learning data collection module, including learning duration, task completion status, and interaction status, and transmit the data to the background for processing in real time;

[0145] S2. In the learning intelligent engine module, based on the analysis results of students' learning data, generate personalized learning paths, recommend content suitable for students' current learning status, and at the same time predict students' future learning performance and make dynamic adjustments;

[0146] S3. Through the real-time interaction feedback module, analyze students' voice or text input in real time, generate personalized learning feedback based on natural language processing and sentiment analysis technologies, and push it to students immediately in the form of text or voice;

[0147] S4. In the collaborative learning platform module, support real-time collaboration among students, and promote interaction and knowledge sharing among students through real-time messaging systems, video conferencing systems, and collaborative editing functions;

[0148] S5. Through the learning assessment and monitoring module, track students' learning progress in real time, analyze their learning effects, and generate personalized learning reports and improvement suggestions;

[0149] S6. The user management and permission control module is responsible for the identity authentication and data security of students, teachers, and administrators, controls the access permissions of the platform, and ensures data privacy.

[0150] Specifically, 1. Platform construction and user login

[0151] Environment deployment

[0152] The server is set up in the cloud, and AWS or Alibaba Cloud is used for computing resource management.

[0153] Data storage adopts a combination of MongoDB and MySQL. Among them, MongoDB stores real-time learning data, and MySQL stores user information and course information.

[0154] The deep learning model is deployed in the cloud using TensorFlow to provide learning path recommendations and prediction services.

[0155] User login and permission management

[0156] Students log in through OAuth2.0 authorization, supporting mobile phone number verification code login and social platform account binding.

[0157] The identity authentication module verifies the identity of logged-in users through JWT and assigns different permissions according to the role (student, teacher, administrator).

[0158] After entering the system, students can see their own learning progress, and teachers can manage courses and view students' learning situations.

[0159] 2. Learning data collection

[0160] Real-time data capture

[0161] A JavaScript listener is embedded in the front-end page of the learning platform to capture students' behavioral data such as clicks, mouse slides, text input, video viewing, etc., and send them to the back-end using AJAX.

[0162] The WebSocket protocol is used to monitor students' online duration and task completion status in real time and provide instant feedback.

[0163] Data storage and processing

[0164] The back-end data processing server receives students' behavioral data and performs data cleaning and formatting.

[0165] Key data such as learning duration and question answering accuracy rate are stored in the MongoDB database and synchronized to the MySQL database regularly.

[0166] 3. Learning Intelligent Engine and Personalized Recommendation

[0167] Learning Path Recommendation

[0168] Analyze the learning trajectories of students and other similar students through collaborative filtering algorithms, and recommend suitable learning resources.

[0169] Based on students' learning records and mastery, use deep learning models (such as LSTM) to predict possible learning obstacles for students and automatically adjust the difficulty of learning tasks.

[0170] After a student completes a task, the system automatically recommends the next suitable learning content and dynamically updates the learning path map on the interface.

[0171] Dynamic Adjustment of Learning Tasks

[0172] If a student's test accuracy rate for a certain knowledge point is lower than 60%, the system automatically recommends additional supplementary courses and practice questions.

[0173] If a student's accuracy rate in a series of tests is always higher than 90%, the system will skip some basic content and directly recommend advanced courses.

[0174] 4. Real-time Interactive Feedback

[0175] Intelligent Q&A

[0176] During the learning process, students can ask questions through text input or voice input.

[0177] Voice input will be converted to text by the Google Speech API and the semantics of the question will be analyzed through the natural language processing module (NLP).

[0178] If there is a matching answer in the system knowledge base, relevant answers will be provided automatically.

[0179] If there is no matching answer in the knowledge base, the question will be pushed to the teacher's end. The teacher can answer in real time, or the system can automatically arrange group discussions.

[0180] Immediate Feedback

[0181] After a student completes a quiz, the platform immediately gives the score through an automatic grading system and provides error analysis.

[0182] Combined with sentiment analysis technology, the system can identify the emotional state of the text or voice input by students. For example, when detecting "too difficult", the system can provide encouraging feedback or reduce the difficulty.

[0183] 5. Collaborative Learning Function

[0184] Real-time Discussion Area

[0185] Students can open the discussion area on the course interface to have real-time discussions with classmates or teachers.

[0186] The discussion area supports text, picture, and file sharing, and integrates an AI assistant that can automatically provide relevant course materials.

[0187] Group learning tasks

[0188] The platform supports creating study groups where students can collaborate on projects and discuss difficult problems.

[0189] In group tasks, the system can assign different roles, such as researchers, report writers, etc., to ensure clear task division.

[0190] Video conferencing support

[0191] Using WebRTC technology, students can initiate video conferences to communicate in real time with teachers or other students.

[0192] Teachers can explain difficult knowledge through the screen sharing function and provide a playback function after the meeting.

[0193] 6. Learning assessment and report generation

[0194] Real-time learning monitoring

[0195] During the learning process of students, the system will generate a learning progress bar to show the current progress and completion status.

[0196] Through learning trajectory analysis, predict the problems that students may encounter and give suggestions in advance.

[0197] Automatic assessment and personalized report

[0198] After students complete quizzes or assignments, the system automatically grades them and generates a personalized learning report through data analysis.

[0199] The learning report includes learning curves, knowledge mastery, recommended learning resources, etc., and provides targeted improvement suggestions.

[0200] 7. Data security and privacy protection

[0201] Permission management

[0202] Students can only access their own learning data. Teachers can view students' learning situations but cannot change students' personal information.

[0203] Administrators can only manage system functions and cannot view personal learning records.

[0204] Data encryption

[0205] The learning records of students are stored encrypted with AES256 to ensure data security.

[0206] The SSL / TLS protocol is used for data transmission to prevent data from being intercepted during network transmission.

[0207] Embodiment III

[0208] In the third embodiment of the present invention, based on the same inventive concept, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the real-time interaction guidance method based on the intelligent collaborative learning platform in the above embodiment are implemented.

[0209] Embodiment IV

[0210] In the fourth embodiment of the present invention, based on the same inventive concept, a computer device is proposed. The terminal includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to implement the real-time interaction guidance method based on the intelligent collaborative learning platform in the above embodiment.

[0211] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0212] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent collaborative learning platform for education and teaching, characterized in that: include: Learning data collection module, used to collect students' learning behavior data and transmit it to the backend for analysis; The learning intelligence engine module is used to generate personalized learning paths based on students’ learning data and dynamically adjust learning content; Real-time interactive feedback module, used to analyze students’ input and generate feedback in real time; The collaborative learning platform module supports students in cross-disciplinary and cross-regional collaborative learning and provides real-time interactive tools; Learning assessment and monitoring module, used to monitor students' learning progress and evaluate their learning effects; The user management and permission control module is used to manage the identity and access rights of platform users and is responsible for data security.

2. The intelligent collaborative learning platform for education and teaching according to claim 1 is characterized in that: The learning data acquisition module includes the following units: The front-end behavior data capture unit, through the script embedded in the platform page, captures the various behaviors of students in the learning process in real time, including clicks, video viewing, and homework submission, and sends the collected data to the back-end data storage service through a request method; The learning progress recording unit marks the student's current progress in the learning module through the front end, including the current module, task completion status and learning time, and updates it to the back-end database in real time using the request method, responsible for the real-time synchronization of the student's learning progress; The interactive event recording unit monitors the text and voice input by students and transmits these interactive events to the real-time data processing module through the communication protocol for subsequent analysis.

3. The intelligent collaborative learning platform for education and teaching according to claim 1 is characterized in that: The learning intelligence engine module includes the following units: The student behavior analysis unit evaluates students' learning status and performance by calculating and analyzing their behavior data, including learning time, task completion rate, and accuracy rate indicators, and establishes a behavior analysis model based on learning rules; The learning path generation unit automatically generates personalized learning paths suitable for students' current learning status based on the results of student behavior analysis, and pushes them to the front-end for display through the data interface, so that students can obtain learning content that meets their personal needs; The intelligent prediction and adjustment unit collects students' learning behavior data, including course clicks, video viewing time, homework submission records, answer accuracy, and learning time distribution, and uses deep learning models to process the data. It uses long short-term memory networks or gated recurrent units to build time series learning models to predict students' future learning progress. The intelligent prediction and adjustment unit calculates the student's learning trajectory by analyzing the student's knowledge mastery, learning habits and difficulties encountered, and automatically adjusts the learning path based on the prediction results to optimize the learning content recommendation; The automatic adjustment of the learning path includes increasing or decreasing the difficulty level of the learning task, pushing targeted supplementary resources, and combining a personalized recommendation algorithm based on content recommendation and collaborative filtering to recommend learning materials that match the students' learning needs, so that the learning content is adapted to the students' current abilities and interests; The intelligent prediction and adjustment unit adopts a reinforcement learning method to collect new learning behavior data in real time after students complete new learning tasks, and inputs it into the prediction model for training optimization, so that the learning path recommendation can continuously adapt to the students' learning progress; The intelligent prediction and adjustment unit deploys a deep learning model through a cloud server and combines it with edge computing technology to improve the efficiency of prediction operations, and can adjust the learning path in real time and provide dynamic learning feedback.

4. The intelligent collaborative learning platform for education and teaching according to claim 1, characterized in that: The real-time interactive feedback module includes the following units: The speech recognition unit converts the student's speech input into text by integrating the speech recognition service, and pushes it to the natural language processing unit after processing; The natural language processing unit performs sentiment analysis and understanding on the text input by students based on semantic analysis technology, extracts students’ emotional state and understanding level, and generates personalized feedback in combination with the knowledge base; The feedback generation unit generates feedback information based on the semantic and sentiment analysis results of the students' input, and provides it to students in the form of text or voice through the push system, responsible for the effectiveness of real-time interaction.

5. The intelligent collaborative learning platform for education and teaching according to claim 1 is characterized in that: The collaborative learning platform module includes the following units: The real-time chat and messaging system unit uses instant messaging protocols to transmit text and voice messages between teachers and students. The messaging system uses a cache mechanism to optimize the speed and stability of message delivery; The video conferencing and discussion system unit uses streaming communication technology to support real-time video discussions between students, and provides screen sharing and document sharing to enhance learning interactivity; The collaborative editing function unit, based on a real-time database, allows multiple people to edit documents simultaneously and allows teachers to provide real-time guidance and annotations.

6. The intelligent collaborative learning platform for education and teaching according to claim 1, characterized in that: The learning assessment and monitoring module includes the following units: The progress tracking and analysis unit collects students' learning data and generates a learning progress analysis report based on the calculation model. The report contains information on task completion and knowledge mastery. The automatic evaluation and grading unit, combined with the automatic grading system, calculates and grades students' homework and test results, and generates structured evaluation data to support teachers in reviewing and adjusting teaching plans; The report generation and improvement suggestion unit generates learning reports based on the evaluation results and provides personalized learning improvement suggestions through text analysis methods, so that students can obtain targeted learning optimization plans.

7. The intelligent collaborative learning platform for education and teaching according to claim 1, characterized in that: The user management and authority control module includes the following units: The identity authentication unit uses identity authentication protocols to authenticate students, teachers, and administrators, and provides a multi-factor authentication mechanism; The permission management unit sets access rights for users of different roles based on the role-based access control model, so that users can only access data and functions within their authorized scope; The data encryption and privacy protection unit encrypts and stores the user's sensitive data, and uses encrypted communication protocols to protect data transmission security and prevent information leakage and illegal access.

8. A real-time interactive guidance method based on an intelligent collaborative learning platform, characterized in that: The intelligent collaborative learning platform for education and teaching as claimed in any one of claims 1 to 7 comprises the following steps: S1. Collect students’ learning behavior data, including learning time, task completion and interaction, through the learning data collection module, and transmit the data to the background for processing in real time; S2. In the learning intelligence engine module, based on the analysis results of students’ learning data, a personalized learning path is generated, and content suitable for students’ current learning status is recommended. At the same time, students’ future learning performance is predicted and dynamically adjusted; S3. Through the real-time interactive feedback module, the student's voice or text input is analyzed in real time, and personalized learning feedback is generated based on natural language processing and sentiment analysis technology, and pushed to students instantly in the form of text or voice; S4. In the collaborative learning platform module, real-time collaboration between students is supported, and interaction and knowledge sharing between students are promoted through real-time messaging system, video conferencing system and collaborative editing function; S5. Through the learning assessment and monitoring module, students’ learning progress can be tracked in real time, their learning effects can be analyzed, and personalized learning reports and improvement suggestions can be generated; S6. Through the user management and permission control module, it is responsible for the identity authentication and data security of students, teachers and administrators, controls the access rights of the platform, and ensures data privacy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the real-time interactive guidance method based on the intelligent collaborative learning platform is implemented as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the real-time interactive guidance method based on the intelligent collaborative learning platform as described in any one of claims 1 to 7 is implemented.

Images