CN118917719A - Transparentization education tracking and evaluating system - Google Patents

Abstract

The invention relates to the technical field of education, in particular to a transparent education tracking and evaluating system. According to the system, through a multi-source data acquisition and processing technology, comprehensive monitoring and evaluation of a student learning process are achieved by utilizing a deep learning model and high-performance computing equipment, and teaching effects and student learning experience are improved. The system comprises a data collection unit, a data processing unit, a self-adaptive teaching module, an interactive communication unit, a report generation unit, a security management unit and a tracking and monitoring unit. The data collection unit collects interaction and learning data of students through the panoramic camera, the microphone array, the pressure sensor and the motion sensor; the data processing unit processes and analyzes the acquired data by using the deep learning model to generate a comprehensive report of student behaviors and learning efficiency; the self-adaptive teaching module adjusts a teaching plan and resources according to the real-time performance and the historical learning data of the students, and provides personalized teaching videos, tests and interactive exercises; the interactive communication unit supports natural language communication and emotion analysis, and enhances interaction between teachers and students; the report generating unit generates a detailed learning report using the data analysis tool; the security management unit ensures the security of the data; the tracking and monitoring system is used for monitoring the position and learning activities of students in real time. Through the technical scheme, the intelligent, scientific and efficient education management is realized, and the teaching effect and the learning experience of students are remarkably improved.

Description

A transparent education tracking and evaluation system

Technical Field

The present invention relates to the field of educational technology, and specifically to a transparent education tracking and evaluation system. The system uses multi-source data collection and processing technology, deep learning models and high-performance computing equipment to achieve comprehensive monitoring and evaluation of students' learning process, aiming to improve teaching effectiveness and students' learning experience.

Background Art

Disadvantages of existing technology

At present, traditional education management systems mainly rely on teachers' subjective judgment and limited student performance data to evaluate students' learning performance. However, this approach has the following problems:

1. Data uniformity: Traditional systems rely only on test scores and homework completion, and cannot fully reflect students’ interactions and learning process in class.

2. Poor real-time performance: It is difficult for teachers to grasp students’ learning status and behavioral performance in a timely manner, and they are unable to adjust teaching plans based on students’ real-time feedback.

3. Lack of personalization: Traditional systems find it difficult to provide personalized learning suggestions and resource recommendations for each student, resulting in limited teaching effectiveness.

4. Limited communication: Communication between teachers and students mainly relies on face-to-face interaction, and it is impossible to fully utilize modern technology to improve communication efficiency and effectiveness.

Related patent technologies

In recent years, some technologies have attempted to improve education management systems through smart devices and data analysis. For example, Chinese patent CN108829702A discloses a smart campus system based on the Internet of Things, which uses sensors and Internet of Things technology to achieve intelligent management of personnel and equipment on campus. Although this system has improved the level of intelligent campus management to a certain extent, it mainly focuses on the management of hardware equipment and fails to deeply mine students' learning data for educational evaluation.

Another example is US Patent US20180324511A1, which introduces a method of using artificial intelligence for educational assessment, which can generate personalized learning suggestions based on students' behavioral data and learning records. However, the implementation of this system requires a large amount of manually labeled data, and has weak support for real-time performance, making it difficult to meet the needs of dynamically adjusting teaching plans.

Related academic research

In terms of academic research, many researchers are committed to using big data and artificial intelligence technologies to improve the accuracy and effectiveness of educational assessment. For example, Smith et al. pointed out in their paper "Educational Data Mining: A Review of the State of the Art" that educational data mining (EDM) technology can identify key factors that affect learning outcomes by analyzing students' behavioral data, thereby providing a basis for personalized teaching.

In addition, Jones et al.'s study "Real-time Student Monitoring and Adaptive Learning" proposed a real-time student monitoring and adaptive learning system that can dynamically adjust teaching content based on students' real-time performance. Although these studies have theoretically proved the feasibility of intelligent education evaluation systems, in practical applications, they still face challenges in data security, system integration, and actual effects.

Innovation of the present invention

In order to overcome the deficiencies in existing technologies and research, the present invention proposes a transparent education tracking and evaluation system. The system integrates a variety of sensors and data processing technologies, and comprehensively analyzes students' interaction and learning data through high-performance computing devices and deep learning models. The system can generate detailed student behavior and learning efficiency reports, support natural language communication and sentiment analysis, provide personalized teaching resources and learning suggestions, dynamically adjust teaching plans, and improve teaching effects and student learning experience. In addition, the security management unit and tracking monitoring system ensure the security of data and the transparency of the teaching process.

Summary of the invention

Purpose of the Invention

The purpose of this invention is to provide a transparent education tracking and evaluation system, which, through multi-source data acquisition and processing technology, utilizes high-performance computing equipment and deep learning models to achieve comprehensive monitoring and evaluation of students' learning process, thereby improving teaching effectiveness and students' learning experience.

Technical Solution

To achieve the above-mentioned purpose of the invention, the present invention proposes a transparent education tracking and evaluation system, which includes a data collection unit, a data processing unit, an adaptive teaching module, an interactive communication unit, a report generation unit, a security management unit and a tracking and monitoring unit.

Data collection unit

The data collection unit collects students’ interaction and learning data through a variety of sensors, including:

1. Panoramic camera: 4K panoramic cameras installed in the four corners of the classroom are used to capture student interactions from a panoramic perspective. The camera is installed at a height of 2.5 meters to ensure that the entire classroom is covered. The camera is connected to the switch in the classroom via an RJ45 network cable and is configured using H.265 encoding technology to reduce bandwidth usage while ensuring high-definition image quality.

2. Microphone array: A microphone array with directional sensing is used to clearly capture students' speeches and classroom discussions. Each microphone array contains 8 microphone units, arranged to pick up sound in all directions at 360 degrees, connected to the data processing server via a USB interface, and installed with a customized driver, enabling built-in noise reduction and echo cancellation algorithms to ensure recording quality.

3. Pressure and motion sensors: Pressure sensors and motion sensors installed on each student seat are used to monitor students' physical activities and posture changes. The pressure sensor is installed under the student's seat and uses capacitive measurement technology with a sensitivity of 0.1 kg; the motion sensor is installed on the seat back and uses a combination of accelerometer and gyroscope to detect students' posture changes in real time. The sensor data is transmitted to the central receiver in the classroom via Bluetooth 5.0 protocol, and then transmitted to the data processing server via Wi-Fi.

Data processing unit

The data processing unit uses high-performance computing equipment and deep learning models to process and analyze the collected data, including:

1. Data processing server: The data processing server is equipped with NVIDIA A100 GPU, uses dual-core Intel Xeon processors, is equipped with 512GB memory, and is installed with 8 NVIDIA A100 GPUs, each with 40GB video memory. The server is installed in a standard 42U cabinet and connected to the campus network via Gigabit Ethernet.

2. Deep learning model: A custom deep learning model written in Python is optimized with TensorFlow and PyTorch frameworks. The data preprocessing module uses NumPy and Pandas libraries for data cleaning and format conversion. Design the convolutional neural network (CNN) model structure, including the input layer, convolution layer, activation layer, pooling layer, fully connected layer, and output layer; design the long short-term memory network (LSTM) model structure, including the input layer, LSTM layer, fully connected layer, and output layer. Use TensorFlow's distributed training strategy to speed up model training, and use cross-validation and hyperparameter tuning techniques for model evaluation and optimization.

3. Data processing: The processed video, audio and sensor data are used to generate comprehensive reports on student behavior and learning efficiency. The OpenCV library is used to parse video data and extract key frames and motion features; the Librosa library is used to analyze audio data and extract voice features and emotional information; the SciPy library is used to process sensor data and analyze student physical activity and posture changes. All data are integrated into the comprehensive report generation module to generate charts and text descriptions, which are then presented to teachers and students through the web interface.

Adaptive teaching module

The adaptive teaching module adjusts teaching plans and resources based on students' real-time performance and historical learning data, including:

1. Integrate the API interfaces of multiple online learning platforms to automatically pull the latest educational resources and materials. The system integrates the API interfaces of Coursera, edX, Khan Academy and other platforms to regularly synchronize the latest courses and teaching materials. The data synchronization module runs automatically every 24 hours to ensure the timely update of teaching resources and store the synchronized data in the local database, including course videos, handouts, exercises and answer analysis.

2. Adjust teaching plans and resources based on students' real-time performance and historical learning data, and use collaborative filtering and content recommendation algorithms to adjust teaching content according to students' learning interests and grades. Using real-time performance data (class participation, test scores, and homework completion) and historical data (learning records and test scores) for analysis, the teaching plan adjustment module generates new teaching plans every week and recommends personalized learning paths and resources.

3. Built-in recommendation engine, using machine learning algorithms to predict students' learning needs, automatically recommending personalized teaching videos, quizzes and interactive exercises. The recommendation engine is trained with XGBoost and LightGBM models, combined with students' learning behavior data, and pushes the recommendation results to the students' learning interface in real time through the API interface. Students can provide feedback on the recommendation results, and the system continuously optimizes the recommendation model based on the feedback data.

Interactive Communication Unit

The interactive communication unit supports natural language communication and sentiment analysis to enhance teacher-student interaction, including:

1. Integrate speech recognition and speech synthesis technology to understand and respond to natural language queries. The speech recognition module uses Google

The Speech-to-Text API supports speech recognition in multiple languages; the speech synthesis module uses the Amazon Polly API to generate natural and fluent speech responses based on text. The system supports real-time voice conversations, and students can ask questions directly to the system through the microphone, and the system will respond immediately.

2. Adjust the tone and content of the response to match the student's emotional state through sentiment analysis algorithms. The sentiment analysis module uses the BERT model to analyze the student's emotional state in combination with their voice and expression data. Based on the sentiment analysis results, the system adjusts the tone and content of the voice response, such as providing encouragement and comfort when the student is feeling down. The sentiment analysis results are stored in the student's personal profile, which teachers can view and refer to at any time.

3. Support video conferencing function, allowing teachers and students to have face-to-face virtual interactions, enhancing the interactive experience of distance education. The video conferencing module uses WebRTC technology and supports multi-party video calls and screen sharing functions. Teachers can conduct remote teaching, answer questions and provide tutoring through video conferencing functions, and students can join the meeting and interact at any time. The system supports recording and playback of video conferences, and students can review and refer to them after class.

Report generation unit

The Report Generation Unit uses data analysis tools to generate detailed study reports, including:

1. Use SAS and R languages for data processing and analysis. The data analysis module uses SAS for large-scale data processing to generate detailed statistical reports and charts; uses R language for data visualization to generate intuitive learning progress charts and knowledge mastery depth analysis. Data analysis algorithms include regression analysis, cluster analysis, and association analysis.

2. The generated reports include detailed learning progress charts, knowledge mastery depth analysis and future learning suggestions. The learning progress chart shows the student's learning status in different courses and knowledge points, including learning time, completion rate and score; the knowledge mastery depth analysis evaluates the student's mastery of each knowledge point based on the student's test and exam scores, and generates radar charts and heat maps; the future learning suggestion module combines the student's learning performance and interests to recommend suitable learning resources and plans to help students improve their learning effects.

3. The report can be viewed in real time in the web interface, or exported as a PDF or Excel file, which is convenient for teachers and parents to download and print. The web interface uses HTML5 and JavaScript technology to provide interactive data display and analysis tools; the report generation module supports exporting analysis results as PDF or Excel files, with beautiful format and detailed content, which is easy to save and share; teachers and parents can log in through the system account to view and download students' learning reports at any time.

Security Management Unit

The security management unit ensures data security, including:

1. All data transmission uses SSL/TLS encryption technology. The data transmission module uses the OpenSSL library to ensure the encryption and decryption of all data during transmission; SSL/TLS certificates are issued by a trusted certificate authority (CA) to ensure the security and reliability of communications; the system regularly updates SSL/TLS certificates to prevent security risks caused by expired certificates.

2. The system database adopts an encrypted storage solution and uses MongoDB's encrypted storage engine to ensure the security of stored data. Database encryption uses the AES-256 algorithm to ensure the confidentiality and integrity of stored data; the MongoDB encrypted storage engine supports transparent encryption, and all data is automatically encrypted and decrypted when written and read; database access control is strictly restricted, and only authorized users and applications can access sensitive data.

3. Regularly and automatically updated anti-virus and anti-malware systems, as well as a comprehensive access control system, ensure the security of data access through multi-factor authentication and dynamic permission allocation. The anti-virus and anti-malware systems use enterprise-level solutions to regularly scan and update virus databases; the access control system uses the RBAC (role-based access control) model, with different roles having different permissions to ensure the security of data access; multi-factor authentication includes passwords, SMS verification codes, and biometrics (such as fingerprint or face recognition) to improve the security and protection capabilities of the system.

Tracking and monitoring systems

Tracking and monitoring systems are used to monitor students’ location and learning activities in real time, including:

1. Integrate GPS and Wi-Fi positioning technology to monitor students' physical location in real time. The GPS module uses a high-precision positioning chip to obtain students' location information in real time, with a positioning accuracy of less than 3 meters; the Wi-Fi positioning module assists in improving indoor positioning accuracy by analyzing the signal strength and MAC address of nearby Wi-Fi hotspots; the positioning data is transmitted to the system server through an encrypted protocol to ensure the security of students' location privacy.

2. The system can identify whether students are in the classroom or designated learning area, as well as their attendance. The system determines whether students arrive and leave on time by comparing their real-time location with the location of the classroom or designated learning area; the attendance management module records students' attendance, including arrival time, departure time and absence records, and generates attendance reports; teachers can view students' attendance in real time through the web interface and make necessary reminders and management.

3. Analyze students' mobile device usage and monitor the time allocation of learning activities to ensure students' learning efficiency. The mobile device monitoring module analyzes students' device usage logs to record usage time, application type and usage frequency; the learning activity analysis module evaluates students' time allocation on different learning tasks based on device usage data and generates a learning efficiency report; the system provides learning time management suggestions to help students arrange their learning time reasonably and improve their learning efficiency.

Beneficial Effects

The present invention realizes comprehensive monitoring and evaluation of students' learning process through a transparent education tracking and evaluation system, which has the following beneficial effects:

1. Comprehensiveness of data: This system uses a variety of sensors, such as panoramic cameras, microphone arrays, pressure and motion sensors, to comprehensively collect students’ interaction and learning data in the classroom. These multi-source data can truly and comprehensively reflect students’ learning behaviors and status, overcoming the defects of single data in traditional education management systems.

2. Real-time monitoring and feedback: This system can monitor students’ learning status and behavior in real time, and process and analyze data in real time through high-performance computing equipment and deep learning models. Teachers can understand students’ learning status in a timely manner based on the real-time reports generated by the system, and dynamically adjust teaching plans based on feedback to improve the timeliness and effectiveness of teaching.

3. Personalized teaching: The system's built-in adaptive teaching module and recommendation engine can provide personalized teaching resources and learning suggestions based on students' real-time performance and historical learning data. Through collaborative filtering and content recommendation algorithms, the system can accurately identify students' learning needs, recommend appropriate learning content and paths, and improve learning outcomes.

4. Enhanced interactivity: The interactive communication unit supports natural language communication and sentiment analysis, can understand and respond to students' natural language queries, and enhance the interaction between teachers and students. Through speech recognition and speech synthesis technology, the system can achieve real-time voice dialogue; the sentiment analysis module can adjust the response content according to the emotional state of the students, and improve the emotional and human level of interaction.

5. Comprehensive learning assessment: The report generation unit uses data analysis tools such as SAS and R language to generate detailed learning progress charts, knowledge mastery in-depth analysis and future learning suggestions. Through a variety of data analysis algorithms, such as regression analysis, cluster analysis and association analysis, the system can provide scientific and comprehensive learning assessment reports to provide strong decision-making support for teachers and students.

6. Data security and privacy protection: The system uses SSL/TLS encryption technology to ensure the security of data transmission and MongoDB's encrypted storage engine to ensure the security of data storage. Multi-layer firewalls and intrusion detection systems, regularly updated anti-virus and anti-malware systems, and comprehensive access control systems with multi-factor authentication and dynamic permission allocation jointly build a highly secure data management environment to protect the privacy and security of student data.

7. Efficient attendance management and learning activity monitoring: The system integrates GPS and Wi-Fi positioning technology, which can monitor students' physical location and attendance in real time and automatically generate attendance reports. By analyzing the use of students' mobile devices, the system can monitor the time allocation of learning activities, provide learning time management suggestions, help students arrange their study time reasonably, and improve learning efficiency.

In summary, the transparent education tracking and evaluation system of the present invention realizes the intelligent, scientific and efficient education management through comprehensive data collection, real-time monitoring and feedback, personalized teaching resource recommendations, enhanced teacher-student interaction, scientific learning evaluation and high data security protection, and significantly improves the teaching effect and students' learning experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1: Overall architecture of the transparent education tracking and evaluation system

The figure shows the overall architecture of the system, which includes a data collection unit, a data processing unit, a dynamic adaptive teaching module, an interactive communication unit, a report generation unit, a security management unit, and a tracking and monitoring unit.

Figure 2: Schematic diagram of the composition and installation location of the data collection unit

The diagram shows the composition of the data collection unit and its installation location in the classroom, including the arrangement of the 4K panoramic camera, microphone array, pressure sensor and motion sensor.

Figure 3: Hardware architecture of the data processing unit

The figure shows the hardware composition of the data processing unit, including components such as servers, processors, memory, and GPUs.

Figure 4: Deep learning model structure diagram

This figure shows the deep learning model structure used in the data processing unit, including the detailed structure of the convolutional neural network (CNN) and the long short-term memory network (LSTM).

Figure 5: Workflow diagram of the comprehensive report generation module

The figure shows the workflow of the comprehensive report generation module, including data collection, data processing, analysis, and report generation and presentation.

Figure 6: Interactive communication unit architecture

The figure shows the composition of the interactive communication unit, including the speech recognition module, speech synthesis module and sentiment analysis module.

Figure 7: Security management unit architecture diagram

The diagram shows the composition and workflow of the security management unit, including data transmission encryption, database encryption storage, and access control.

Figure 8: Tracking and monitoring system architecture

The figure shows the composition of the tracking and monitoring system, including the GPS positioning module, Wi-Fi positioning module and attendance management module.

Example

Example 1: Installation and configuration of data collection unit

Panoramic camera installation

1. Determine the four corners of the classroom as the installation locations for the panoramic cameras. Each camera should be installed at a height of 2.5 meters to ensure that the viewing angle covers the entire classroom.

2. Use an electric drill to drill holes on the walls at the four corners, install the fixed brackets, and ensure that the brackets are stable.

3. Fix the 4K panoramic camera on the bracket and use a level to adjust the angle to ensure that the viewing angle covers the entire classroom.

4. Use a high-quality RJ45 network cable to connect the camera to the switch in the classroom to ensure stable transmission of the video stream.

5. Set H.265 encoding technology in the camera configuration interface to reduce bandwidth usage while ensuring high-definition image quality.

6. Test camera connections and video quality to ensure all cameras are working properly.

Microphone array configuration

1. Select the central area of the classroom as the installation location for the microphone array, ensuring that it is arranged to pick up sound in all directions at 360 degrees.

2. Use an electric drill to drill holes at the selected locations and install the microphone array bracket.

3. Fix the microphone array on the bracket and connect it to the data processing server via USB cable, ensuring a stable connection.

4. Install the customized microphone driver on the server to ensure that the device is recognized normally.

5. Enable the built-in noise reduction and echo cancellation algorithms in the driver configuration interface to ensure clear recording quality.

6. Perform a recording test to ensure that the microphone array can clearly capture sounds from all directions.

Pressure and Motion Sensor Installation

1. Select an appropriate location under each student's seat to install a pressure sensor, using capacitive measurement technology and setting the sensitivity to 0.1 kg.

2. Use strong double-sided tape to stick the pressure sensor under the seat to ensure that the sensor is stable and not easy to fall off.

3. Arrange the sensor cables and connect them to the data transmission module.

4. Install a motion sensor at an appropriate location on the seat back, using a combination of an accelerometer and gyroscope to detect students' posture changes in real time.

5. Use strong double-sided tape to stick the motion sensor to the seat back and make sure the cable is neatly connected to the data transmission module.

6. Transmit sensor data to a central receiver in the classroom via Bluetooth 5.0 protocol, and then transmit to a data processing server via Wi-Fi.

7. Test the connection and data transmission of all sensors to ensure accurate data collection.

Example 2: Configuration and operation of data processing unit

Data processing server configuration

1. Select a location for a standard 42U cabinet and ensure that the cabinet is securely installed in the data center.

2. Install the server components with dual-socket Intel Xeon processors and 512GB of memory in the cabinet.

3. Insert and secure 8 NVIDIA A100 GPUs, each with 40GB of video memory, and ensure that the GPUs are properly connected to the motherboard.

4. Connect the server power cord and network cable, and connect the server to the campus network via Gigabit Ethernet.

5. Start the server and enter the BIOS setup to ensure that all components are recognized normally.

6. Install the Linux operating system and perform basic configuration.

7. Install necessary drivers and CUDA toolkit to ensure that the GPU can work properly.

8. Install TensorFlow and PyTorch frameworks and test the environment configuration to ensure the normal operation of the deep learning framework.

Deep learning model development

1. Use Python to write custom deep learning models and install dependent libraries such as NumPy, Pandas, TensorFlow, and PyTorch.

2. Use NumPy and Pandas libraries for data cleaning and format conversion in the data preprocessing module.

3. Design the convolutional neural network (CNN) model structure, including input layer, convolution layer, activation layer, pooling layer, fully connected layer and output layer.

4. Design the long short-term memory network (LSTM) model structure, including the input layer, LSTM layer, fully connected layer and output layer.

5. Use TensorFlow's distributed training strategy to speed up model training, and use cross-validation and hyperparameter tuning techniques to evaluate and optimize the model.

Data processing and analysis

1. Parse video data through the OpenCV library to extract key frames and motion features.

2. Analyze audio data through the Librosa library to extract speech features and emotional information.

3. Process sensor data through the SciPy library to analyze students’ physical activities and posture changes.

4. Integrate all data into the comprehensive report generation module to generate charts and text descriptions.

5. The analysis results are displayed through the web interface, and teachers and students can view them in real time.

Example 3: Implementation of the Adaptive Teaching Module

Online learning platform integration

1. Obtain the API interface development documents of Coursera, edX, Khan Academy and other platforms, write API call programs, and regularly pull the latest course and textbook data.

2. Set up a data synchronization module to run automatically every 24 hours to ensure timely updating of teaching resources.

3. Store the synchronized data in the local database, including course videos, handouts, exercises, and answers.

Adaptive algorithm application

1. Collect students’ real-time performance data and historical data, use collaborative filtering and content recommendation algorithms, and adjust teaching content according to students’ learning interests and grades.

2. Use real-time and historical data for analysis and generate personalized teaching plans.

3. The teaching plan adjustment module generates new teaching plans every week and recommends personalized learning paths and resources.

Intelligent recommendation engine

1. Use XGBoost and LightGBM models for training, combine with students’ learning behavior data, and push the recommendation results to students’ learning interface in real time through the API interface.

2. Students provide feedback on the recommendation results, and the system continuously optimizes the recommendation model based on the feedback data.

Example 4: Deployment of interactive communication unit

Speech Recognition and Synthesis

1. Integrate Google Speech-to-Text API for speech recognition and support speech recognition in multiple languages.

2. Use the Amazon Polly API for speech synthesis to generate natural and fluent voice responses based on text.

3. The system supports real-time voice dialogue. Students can ask questions directly to the system through the microphone, and the system will respond immediately.

Sentiment Analysis

1. The sentiment analysis module uses the BERT model to combine students’ voice and expression data to analyze their emotional state.

2. The system adjusts the tone and content of voice responses based on the results of emotion analysis, for example, providing encouragement and comfort when students are depressed.

3. The sentiment analysis results are stored in the students’ personal profiles and can be viewed and referenced by teachers at any time.

Video conferencing capabilities

1. The video conferencing module uses WebRTC technology and supports multi-party video calls and screen sharing functions.

2. Teachers can conduct remote teaching, answer questions and provide tutoring through video conferencing, and students can join the meeting and interact at any time.

3. The system supports recording and playback of video conferences, which students can review and refer to after class.

Example 5: Implementation of a report generation unit

Data analysis tool configuration

1. The data analysis module uses SAS for large-scale data processing and generates detailed statistical reports and charts.

2. Use R language for data visualization to generate intuitive learning progress charts and in-depth analysis of knowledge mastery.

3. Data analysis algorithms include regression analysis, cluster analysis and association analysis.

Study report generation

1. Generate detailed learning progress charts, in-depth analysis of knowledge mastery, and future learning suggestions.

2. The learning progress chart shows students’ learning status on different courses and knowledge points, including learning time, completion rate and scores.

3. In-depth analysis of knowledge mastery: Based on students’ test and exam scores, the system evaluates their mastery of each knowledge point and generates radar charts and heat maps.

4. The future learning suggestion module combines students’ learning performance and interests to recommend appropriate learning resources and plans to help students improve their learning outcomes.

Report display and export

1. The report can be viewed in real time in the web interface, and teachers and parents can log in and view it through the system account.

2. The report generation module supports exporting analysis results as PDF or Excel files with beautiful format and detailed content, which is easy to save and share.

3. Provide interactive data display and analysis tools through the web interface, so that teachers and parents can view and download students' learning reports at any time.

Example 6: Deployment of a security management unit

Data transmission security

1. All data transmission uses SSL/TLS encryption technology, and the data transmission module uses the OpenSSL library to ensure the encryption and decryption of all data during transmission.

2.SSL/TLS certificates are issued by trusted certificate authorities to ensure the security and reliability of communications.

3. The system regularly updates SSL/TLS certificates to prevent security risks caused by expired certificates.

Database security

1. The system database uses MongoDB's encrypted storage engine to ensure the security of stored data.

2. Database encryption uses AES-256 algorithm to ensure the confidentiality and integrity of stored data.

3. The MongoDB encrypted storage engine supports transparent encryption, and all data is automatically encrypted and decrypted when writing and reading.

4. Database access control is strictly limited, and only authorized users and applications can access sensitive data.

Antivirus and access control

1. The system uses an anti-virus and anti-malware system that is automatically updated regularly, and the virus database is scanned and updated regularly.

2. The access control system adopts a role-based access control model, where different roles have different permissions to ensure the security of data access.

3. Multi-factor authentication includes passwords, SMS verification codes and biometrics to improve the security and protection capabilities of the system.

Example 7: Implementation of Tracking and Monitoring System

Positioning Technology

1. The system integrates GPS and Wi-Fi positioning technology to monitor students’ physical location in real time.

2. The GPS module uses a high-precision positioning chip to obtain students' location information in real time, with a positioning accuracy of within 3 meters.

3. The Wi-Fi positioning module helps improve indoor positioning accuracy by analyzing the signal strength and MAC address of nearby Wi-Fi hotspots.

4. The positioning data is transmitted to the system server through an encrypted protocol to ensure the security of the student's location privacy.

Attendance Management

1. The system can identify whether students are in the classroom or designated learning area, and determine whether students arrive and leave on time by comparing their real-time location with the location of the classroom or designated learning area.

2. The attendance management module records students’ attendance, including arrival time, departure time and absence records, and generates attendance reports.

3. Teachers can check students' attendance in real time through the web interface and make necessary reminders and management.

Learning activity monitoring

1. The system monitors the time allocation for learning activities by analyzing students’ mobile device usage.

2. The mobile device monitoring module records usage time, application type and frequency of use by analyzing students’ device usage logs.

3. The learning activity analysis module evaluates students’ time allocation on different learning tasks based on device usage data and generates a learning efficiency report.

4. The system provides study time management suggestions to help students allocate study time reasonably and improve learning efficiency.

Claims (8)

1. A transparent education tracking and evaluation system, comprising: a data collection unit, which is used to collect students' interaction and learning data through panoramic cameras, microphone arrays, pressure sensors and motion sensors; a data processing unit, which uses high-performance computing equipment and deep learning models to process and analyze the collected data, and generate a comprehensive report on student behavior and learning efficiency; an adaptive teaching module, which adjusts teaching plans and resources according to students' real-time performance and historical learning data, and provides personalized teaching videos, quizzes and interactive exercises; an interactive communication unit, which supports natural language communication and sentiment analysis, and enhances teacher-student interaction; a report generation unit, which uses data analysis tools to generate detailed learning reports; a security management unit, which ensures the security of data; a tracking and monitoring unit, which is used to monitor students' locations and learning activities in real time. 2. The system according to claim 1, wherein the data collection unit comprises: panoramic cameras installed at the four corners of the classroom, configured using H.265 encoding technology, and connected to the switch in the classroom via an RJ45 network cable; a microphone array with direction sensing, connected to the data processing server via a USB interface, and a customized driver installed; pressure sensors and motion sensors installed on each student seat, which transmit data to a central receiver in the classroom via the Bluetooth 5.0 protocol, and then transmit it to the data processing server via Wi-Fi. 3. The system according to claim 1, wherein the data processing unit comprises: a data processing server equipped with NVIDIA A100 GPU, using dual-core Intel Xeon processors and 512GB of memory, connected to the campus network via Gigabit Ethernet; and a deep learning model developed using TensorFlow and PyTorch frameworks for data cleaning, format conversion and analysis. 4. The system according to claim 1, wherein the adaptive teaching module comprises: integrating API interfaces of online learning platforms such as Coursera, edX, Khan Academy, etc., and regularly synchronizing the latest courses and teaching materials; using collaborative filtering and content recommendation algorithms to adjust teaching content according to students' learning interests and grades. 5. The system according to claim 1, wherein the interactive communication unit comprises: a speech recognition module integrating Google Speech-to-Text API; a speech synthesis module integrating Amazon Polly API; and a sentiment analysis module using a BERT model to analyze students' emotional states. 6. The system according to claim 1, wherein the report generation unit comprises: a data analysis module, which uses SAS and R languages to process and analyze data, and generates learning progress charts, knowledge mastery depth analysis and future learning suggestions. 7. The system according to claim 1, wherein the security management unit comprises: a data transmission module, which adopts SSL/TLS encryption technology; a database, which adopts MongoDB's encrypted storage engine; an anti-virus and access control system, which adopts a role-based access control model and multi-factor authentication. 8. The system according to claim 1, wherein the tracking and monitoring unit comprises: a GPS module, which uses a high-precision positioning chip; a Wi-Fi positioning module, which improves indoor positioning accuracy by analyzing the signal strength and MAC address of nearby Wi-Fi hotspots; an attendance management module, which records students' attendance and generates attendance reports; and a learning activity monitoring module, which analyzes students' mobile device usage logs and generates learning efficiency reports.