Integrated management system for intelligent digital campus

Abstract

The invention discloses a comprehensive management system for an intelligent digital campus, and relates to the technical field of digitization, and the system comprises the steps: collecting the learning behavior data of students in real time, and forming a preliminary data set of the students; the learning behaviors of the students in the preliminary data set are analyzed, and a personalized learning portrait of each student is generated; based on the personalized learning portrait, determining a learning demand and a resource demand feature of the student; pushing a personalized learning path and dynamically adjusting a teaching resource allocation scheme according to the learning demand characteristics and the resource demand characteristics of the students; and collecting energy consumption data of each region, and automatically optimizing an energy consumption use strategy in combination with a resource allocation result and the energy consumption data of each region to obtain an optimized learning environment. According to the resource scheduling strategy based on personalized demands, the learning efficiency of students can be improved, and full utilization of resources can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to a comprehensive management system for a smart digital campus. Background Art

[0002] With the development of smart campuses and educational informatization, more and more universities and educational institutions have begun to introduce digital management systems to improve the utilization efficiency of teaching resources and optimize the learning environment. However, most of the existing smart campus systems focus on the static allocation and management of teaching resources, and lack the ability to dynamically respond to students' personalized learning needs. In the prior art, although some systems can collect students' learning behavior data, most of them are limited to simple data statistical analysis, and fail to deeply explore students' learning behavior patterns, nor can they conduct timely resource scheduling and learning path adjustments based on students' real-time performance and changes in the learning environment. In addition, the allocation of teaching resources is usually separated from the campus energy consumption management system, and the coordinated optimization of resources and energy consumption is not achieved, resulting in energy waste.

[0003] The shortcomings of existing technologies are mainly reflected in the following two aspects: first, the existing resource scheduling system lacks the ability to dynamically respond to students' personalized learning needs and cannot flexibly allocate resources according to students' real-time performance and changes in learning paths; second, the existing energy consumption management system is disconnected from the teaching resource scheduling system, and fails to optimize energy consumption according to real-time teaching resource usage, resulting in unnecessary energy waste. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a comprehensive management system for a smart digital campus to solve the problem of how to dynamically adjust the allocation of teaching resources and optimize energy consumption according to the personalized learning needs of students and changes in the learning environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a comprehensive management system for a smart digital campus, which includes: The data collection module is used to collect students’ learning behavior data in real time to form a preliminary data set for students; The behavior analysis module is used to analyze the learning behavior of students in the preliminary data set and generate a personalized learning profile for each student; The demand confirmation module determines students’ learning needs and resource demand characteristics based on personalized learning portraits; The resource push module is used to push personalized learning paths and dynamically adjust the teaching resource allocation plan according to the students' learning needs and resource demand characteristics; The optimization module is used to collect energy consumption data of each area, combine the resource allocation results and the energy consumption data of each area, automatically optimize the energy consumption strategy, and obtain the optimized learning environment; The adjustment module tracks students’ learning progress and efficiency in real time through an optimized learning environment, and dynamically adjusts the learning path based on students’ personalized learning profiles and current learning environment.

[0007] As a preferred solution of the comprehensive management system for smart digital campus described in the present invention, wherein: the learning behavior data includes student classroom performance data, online learning data, achievement data and interaction data; Clean and format the collected student behavior data; The processed student behavior data is divided into multiple dimensions and indexed according to student ID, course ID and timestamp to form a preliminary data set of students.

[0008] As a preferred solution of the integrated management system for smart digital campus described in the present invention, the following steps are included in analyzing the learning behavior of students in the preliminary data set to form a student behavior feature vector: Extracting concentration from the facial expressions and body posture data in the students' classroom performance data as the main feature of the students' classroom performance; The online learning duration and frequency of students in the online learning data are converted into continuous variables to obtain the learning duration and frequency characteristics; Convert the test scores and homework completion rates in the performance data into standard scores to form performance characteristics; Generate quantitative features through the number of students participating in class and the quality of interaction in the interactive data; All the extracted features are integrated to form the student behavior feature vector.

[0009] As a preferred solution of the integrated management system for smart digital campus described in the present invention, generating a personalized learning portrait of each student based on the student behavior feature vector includes the following steps: Through the unsupervised K-means clustering algorithm, students are divided into different learning type groups according to their learning behavior feature vectors; Use supervised learning with random forest classifiers, combined with students’ test scores and learning frequency labels, to predict each student’s learning ability and study habits; Through K-means clustering and random forest classification results, a personalized learning portrait is generated for each student.

[0010] As a preferred solution of the integrated management system for smart digital campus described in the present invention, determining the learning needs of students based on personalized learning portraits includes the following steps: Extract students’ learning habits and learning abilities from each student’s personalized portrait; Based on students’ learning habits and learning abilities, determine the intensity of students’ learning needs at the current stage; Based on the intensity of students' learning needs, the urgency of students' learning and resource requirements are evaluated to obtain the characteristics of students' learning needs.

[0011] As a preferred solution of the integrated management system for smart digital campus described in the present invention, wherein: based on personalized learning portraits, determining the learning resource demand characteristics of students includes the following steps: Extract learning preferences from each student’s personalized profile; Recommend learning resource types to students based on their learning preferences; Recommend the best resource type based on students' preferences for different resource types and the availability of the current resource library; Determine the amount of resources students need based on their needs; The optimal resource type and the required resource quantity are integrated to obtain the resource demand characteristics.

[0012] As a preferred solution of the integrated management system for smart digital campus described in the present invention, the following steps are included: according to the learning demand characteristics and resource demand characteristics of students, pushing personalized learning paths and dynamically adjusting the teaching resource allocation plan: Based on the intensity of students’ learning needs, determine the subject areas that need to be matched first; According to the resource type needs of students, select resources that meet the needs in the identified priority subject areas; By calculating the similarity between student needs and the selected resource demand characteristics, the matching degree between student needs and the selected resource demand characteristics is determined; Check the real-time availability of each resource from the matching results, exclude currently unavailable resources, and obtain the final available resources; Generate personalized learning paths based on the final available resources and combined with the students' learning needs and resource demand characteristics; Detect the available status of resources in real time, reschedule the final available resources, and adjust the personalized learning path to obtain a dynamically adjusted teaching resource scheduling and allocation plan.

[0013] As a preferred solution of the integrated management system for smart digital campuses described in the present invention, wherein: the energy consumption data includes power consumption, equipment status and environmental parameters; The power consumption refers to the real-time power consumption of classrooms and laboratories; the equipment status refers to whether the lighting system, air conditioning and experimental equipment are turned on; the environmental parameters refer to the temperature, humidity and light intensity data that affect students' learning comfort.

[0014] As a preferred solution of the integrated management system for smart digital campus described in the present invention, the following steps are included: combining the resource allocation results and the energy consumption data of each area to automatically optimize the energy consumption strategy and obtain the optimized learning environment: Detect the environmental status and equipment usage of classrooms and laboratory areas based on energy consumption data; According to the teaching resource scheduling and allocation results, determine whether the current area needs to maintain the current energy consumption status; Automatically generate energy consumption optimization strategies based on detection and judgment results; The energy consumption changes of each area are detected in real time, and based on the generated energy consumption optimization usage strategy, the power and switching status of physical devices are dynamically adjusted to obtain an optimized learning environment.

[0015] As a preferred solution of the integrated management system for smart digital campus described in the present invention, wherein: through the optimized learning environment, the learning progress and learning efficiency of students are tracked in real time, and the learning path is dynamically adjusted in combination with the personalized learning portrait of the students and the current learning environment, including the following steps: Track students' learning progress in real time according to each student's learning path, and obtain students' progress in each learning step; Assess students' learning efficiency through their progress in each learning step; Evaluate students’ learning outcomes based on their performance data in the learning path; Dynamically adjust personalized learning paths based on students' learning performance and efficiency.

[0016] The beneficial effects of the present invention are: by collecting students' learning behavior data in real time, generating personalized learning portraits, and combining with the current learning environment, dynamically adjusting students' learning paths and teaching resource allocation plans; resource scheduling strategies based on personalized needs can not only improve students' learning efficiency, but also ensure full utilization of resources; combining energy consumption management with resource scheduling, using energy consumption data from each area, automatically optimizing energy consumption usage strategies, and dynamically adjusting the power and switching status of physical equipment, thereby minimizing energy waste while ensuring the comfort of the learning environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the comprehensive management system for a smart digital campus in Example 1.

[0019] Figure 2 This is a schematic diagram of cluster distribution in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a comprehensive management system for a smart digital campus, including a data collection module, a behavior analysis module, a demand confirmation module, a resource push module, an optimization module and an adjustment module.

[0024] Specifically, the data collection module is used to collect students' learning behavior data in real time to form a preliminary data set of students.

[0025] Learning behavior data includes student classroom performance data, online learning data, grade data and interaction data; the collected student behavior data is cleaned and formatted; the processed student behavior data is divided into multiple dimensions and indexed according to student ID, course ID and timestamp to form a preliminary data set for students.

[0026] Specifically, classroom performance data includes students' attention and participation in class. Facial expressions, eye tracking, body posture and other data collected by cameras are analyzed in combination with image recognition algorithms to determine students' concentration and participation. Online learning data includes students' study time, study frequency, and course selection after logging into the learning management system. The system records the timestamp of each student login, browsed learning resources, completed tasks, etc. Grade data includes students' test results and homework submission status. For subjective questions, the system uses an automatic scoring algorithm combined with teacher feedback to generate the final grade. Interaction data refers to the frequency and content of students' speeches collected through a microphone, combined with a natural language processing (NLP) algorithm to analyze the quality of students' language interaction.

[0027] The behavior analysis module is used to analyze the learning behavior of students in the preliminary data set and generate a personalized learning profile for each student.

[0028] Analyzing the learning behavior of students in the preliminary data set and forming the student behavior feature vector includes the following steps: Concentration is extracted from the facial expressions and body posture data in the classroom performance data of students as the main feature of students' classroom performance; the online learning time and frequency of students in the online learning data are converted into continuous variables to obtain learning time and frequency features; the test scores and homework completion rates in the grade data are converted into standard scores to form grade features; quantitative features are generated through the number of times students participate in class and the quality of interaction in the interactive data; all extracted features are integrated to form a student behavior feature vector.

[0029] Based on the student behavior feature vector, generating a personalized learning profile for each student includes the following steps: Through the unsupervised K-means clustering algorithm, students are divided into different learning type groups according to their learning behavior feature vectors; Use supervised learning with random forest classifiers, combined with students’ test scores and learning frequency labels, to predict each student’s learning ability and study habits; Through K-means clustering and random forest classification results, a personalized learning portrait is generated for each student.

[0030] Specifically, the specific operations of the K-means clustering algorithm are: Divide students into Each class represents a group of students with similar learning behaviors and habits. The feature vectors of each student are used as the initial cluster centers (centroids). These centroids represent the center points of each cluster. By minimizing the sum of the squares of the distances from each student to its cluster center, the students are divided into multiple different groups to form a learning portrait of each student. The expression is: ; in, represents the sum of squared distances from all students to their cluster centers, represents the clustering index, represents the number of clusters, Indicates The learning behavior feature vector of data points, represents the data point index, Indicates The set of all data points in a cluster contains all data points belonging to the cluster (student behavior feature vectors). Indicates The center point of a cluster.

[0031] Cluster 1 (learning progress): Students in this group have a higher learning time and frequency, their test scores gradually improve, and they behave actively in class.

[0032] Cluster 2 (Learning Delay): Students in this group study less, have lower test scores, and have lower class participation.

[0033] Cluster 3 (Self-driven learners): Students in this group study for an average amount of time, but study frequently, have a high level of participation, and have good grades.

[0034] Cluster 4 (passive learning): Students in this group study for a long time, but the learning effect is not good and the class participation is low.

[0035] Specifically, the operation of random forest classification is as follows: Through random sampling and feature selection, multiple decision trees are constructed. Each tree is generated based on different feature subsets and data subsets. The student's behavioral feature vector and clustering results are used as training data. Bootstrap sampling is used for the training data to generate multiple data sets. When each tree splits, it selects the best feature from a random subset of multiple feature data sets. For each decision tree, a classification model is generated using the training data. Each tree learns to classify learning ability or learning habits through the features in the training data. The random forest integrates the results of multiple decision trees and finally obtains the prediction result through a voting mechanism. The expression is: ; in, Represents the final classification result, that is, the random forest model for the input feature vector (Students’ learning behavior characteristics) generate prediction results, Represents the majority voting mechanism, which selects the category with the most occurrences in the prediction results of all trees as the final classification result. Indicates that the first decision tree responds to the input The classification results, Indicates that the second decision tree responds to the input The classification results, Indicates A decision tree for input The classification results, Represents the number of decision trees.

[0036] The demand confirmation module determines students' learning needs and resource demand characteristics based on personalized learning portraits.

[0037] Based on the personalized learning profile, determining students’ learning needs includes the following steps: Extract students' learning habits and learning abilities from each student's personalized portrait; judge the intensity of students' learning needs at the current stage based on their learning habits and learning abilities; evaluate the urgency and resource needs of students' learning based on the intensity of their learning needs, and obtain the characteristics of students' learning needs.

[0038] Based on personalized learning portraits, determining the characteristics of students' learning resource needs includes the following steps: Extract learning preferences from each student's personalized portrait; recommend learning resource types to students based on their learning preferences; recommend the optimal resource type based on students' preferences for different resource types and the availability of the current resource library; determine the number of resources required by students based on their demand characteristics; integrate the optimal resource type with the required resource quantity to obtain resource demand characteristics.

[0039] The resource push module is used to push personalized learning paths and dynamically adjust the teaching resource allocation plan according to the students' learning needs and resource needs. It includes the following steps: Based on the intensity of students' learning needs, determine the subject areas that need to be matched first; based on students' resource type needs, screen out resources that meet this type of needs within the determined priority subject areas; by calculating the similarity between students' needs and the screened resource demand characteristics, determine the degree of match between students' needs and the screened resource demand characteristics; check the real-time availability of each resource from the matching results, and exclude currently unavailable resources to obtain the final available resources; based on the final available resources, generate personalized learning paths in combination with students' learning demand intensity and resource demand characteristics; detect the available status of resources in real time, reschedule the final available resources, and adjust the personalized learning path to obtain a dynamically adjusted teaching resource scheduling and allocation plan.

[0040] Specifically, the expression of similarity is: ; in, Indicates The similarity between resources and student needs, similarity The larger the value, the higher the degree of matching between the resource and the student's needs. represents the distance between student demand characteristics and resource demand characteristics, Indicates the characteristics of student needs. Represents resource demand characteristics.

[0041] The expression of the distance between student demand characteristics and resource demand characteristics is: ; in, Indicates that students’ demand characteristics are The value of the dimension, Indicates the resource demand characteristics in The value of the dimension, represents the dimension of the feature vector, Represents the dimension index of the feature vector.

[0042] The expression generated by the personalized learning path is: ; in, represents a personalized learning path, represents the number of learning steps (i.e., the number of resources) contained in the learning path, represents the step index in the learning path, Indicates resources in each learning step, Indicates The learning time or task time of a resource.

[0043] The optimization module is used to collect energy consumption data of each area, combine the resource allocation results and the energy consumption data of each area, automatically optimize the energy consumption usage strategy, and obtain an optimized learning environment.

[0044] Energy consumption data includes power consumption, equipment status and environmental parameters.

[0045] Power consumption refers to the real-time power consumption of classrooms and laboratories; equipment status refers to whether the lighting system, air conditioning and experimental equipment are turned on; environmental parameters refer to temperature, humidity and light intensity data that affect students' learning comfort.

[0046] Combining the resource allocation results and the energy consumption data of each area, the energy consumption strategy is automatically optimized. The optimized learning environment includes the following steps: Based on energy consumption data, the environmental status and equipment usage of classrooms and laboratory areas are detected; based on the results of teaching resource scheduling and allocation, it is determined whether the current area needs to maintain the existing energy consumption status; based on the detection and judgment results, an energy consumption optimization strategy is automatically generated; the energy consumption changes of each area are detected in real time, and based on the generated energy consumption optimization strategy, the power and switch status of physical equipment are dynamically adjusted to obtain an optimized learning environment.

[0047] The adjustment module tracks students’ learning progress and efficiency in real time through the optimized learning environment, and dynamically adjusts the learning path based on students’ personalized learning profiles and current learning environment. The steps include: Track students' learning progress in real time according to each student's learning path, and obtain students' progress in each learning step; Assess students' learning efficiency through their progress in each learning step; Evaluate students’ learning outcomes based on their performance data in the learning path; Dynamically adjust personalized learning paths based on students' learning performance and efficiency.

[0048] Specifically, the student's progress in each learning step includes the following data: whether the student completes the current learning step on time; the degree of excellence of the student in the current learning step (such as test scores, homework accuracy, experiment success rate, etc.); and the time the student spends in the learning process.

[0049] Learning efficiency assessment means, for example, if a student completes 20 math problems within 1 hour with a high accuracy rate, then his learning efficiency is judged to be high.

[0050] Learning outcomes assessment refers to determining whether a student is performing well or having difficulty in a course, for example, based on data such as the student's scores on a test or the accuracy of an assignment.

[0051] Dynamic adjustment of personalized paths means that if a student performs well in basic mathematics courses (scores consistently above 90%), he or she will be recommended to study more difficult advanced mathematics courses; if a student makes slow progress in a course (for example, low test scores, high homework error rates), he or she will be recommended additional learning resources or tutoring courses.

[0052] In summary, the present invention generates personalized learning portraits by collecting students' learning behavior data in real time, and dynamically adjusts students' learning paths and teaching resource allocation plans in combination with the current learning environment; the resource scheduling strategy based on personalized needs can not only improve students' learning efficiency, but also ensure the full utilization of resources; it combines energy consumption management with resource scheduling, utilizes the energy consumption data of each area, automatically optimizes the energy consumption usage strategy, and dynamically adjusts the power and switching status of physical equipment, thereby minimizing energy waste while ensuring the comfort of the learning environment.

[0053] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A comprehensive management system for a smart digital campus, characterized by: include, The data collection module is used to collect students’ learning behavior data in real time to form a preliminary data set for students; The behavior analysis module is used to analyze the learning behavior of students in the preliminary data set and generate a personalized learning profile for each student; The demand confirmation module determines students’ learning needs and resource demand characteristics based on personalized learning portraits; Resource push module, which is used to push personalized learning paths and dynamically adjust teaching resource allocation plans based on students' learning needs and resource requirements; The optimization module is used to collect energy consumption data of each area, combine the resource allocation results and the energy consumption data of each area, automatically optimize the energy consumption strategy, and obtain the optimized learning environment; The adjustment module tracks students’ learning progress and efficiency in real time through an optimized learning environment, and dynamically adjusts the learning path based on students’ personalized learning profiles and current learning environment.

2. The integrated management system for a smart digital campus as claimed in claim 1, characterized in that: The learning behavior data includes student classroom performance data, online learning data, achievement data and interaction data; Clean and format the collected student behavior data; The processed student behavior data is divided into multiple dimensions and indexed according to student ID, course ID and timestamp to form a preliminary data set of students.

3. The integrated management system for a smart digital campus as claimed in claim 2, characterized in that: Analyze the learning behavior of students in the preliminary data set to form a student behavior feature vector The following steps are included: Extracting concentration from the facial expressions and body posture data in the students' classroom performance data as the main feature of the students' classroom performance; The online learning duration and frequency of students in the online learning data are converted into continuous variables to obtain the learning duration and frequency characteristics; Convert the test scores and homework completion rates in the performance data into standard scores to form performance characteristics; Generate quantitative features through the number of students participating in class and the quality of interaction in the interactive data; All the extracted features are integrated to form the student behavior feature vector.

4. The integrated management system for a smart digital campus as claimed in claim 3, characterized in that: Based on the student behavior feature vector, generating a personalized learning profile for each student includes the following steps: Through the unsupervised K-means clustering algorithm, students are divided into different learning type groups according to their learning behavior feature vectors; Use supervised learning with random forest classifiers, combined with students’ test scores and learning frequency labels, to predict each student’s learning ability and study habits; Through K-means clustering and random forest classification results, a personalized learning portrait is generated for each student.

5. The integrated management system for a smart digital campus as claimed in claim 4, characterized in that: Based on the personalized learning profile, determining students’ learning needs includes the following steps: Extract students’ learning habits and learning abilities from each student’s personalized portrait; Based on students’ learning habits and learning abilities, determine the intensity of students’ learning needs at the current stage; Based on the intensity of students' learning needs, the urgency of students' learning and resource requirements are evaluated to obtain the characteristics of students' learning needs.

6. The integrated management system for a smart digital campus as claimed in claim 5, characterized in that: Determine students’ learning resource demand characteristics based on personalized learning portraits The following steps are included: Extract learning preferences from each student’s personalized profile; Recommend learning resource types to students based on their learning preferences; Recommend the best resource type based on students' preferences for different resource types and the availability of the current resource library; Determine the amount of resources students need based on their needs characteristics; The optimal resource type and the required resource quantity are integrated to obtain the resource demand characteristics.

7. The integrated management system for a smart digital campus as claimed in claim 6, characterized in that: According to the learning needs and resource requirements of students, pushing personalized learning paths and dynamically adjusting the teaching resource allocation plan includes the following steps: Based on the intensity of students’ learning needs, determine the subject areas that need to be matched first; According to the resource type needs of students, select resources that meet the needs in the identified priority subject areas; By calculating the similarity between student needs and the selected resource demand characteristics, the matching degree between student needs and the selected resource demand characteristics is determined; Check the real-time availability of each resource from the matching results, exclude currently unavailable resources, and obtain the final available resources; Generate personalized learning paths based on the final available resources and combined with the students' learning needs and resource demand characteristics; Detect the available status of resources in real time, reschedule the final available resources, and adjust the personalized learning path to obtain a dynamically adjusted teaching resource scheduling and allocation plan.

8. The integrated management system for a smart digital campus as claimed in claim 1, characterized in that: The energy consumption data includes power consumption, equipment status and environmental parameters; The power consumption refers to the real-time power consumption of classrooms and laboratories; the equipment status refers to whether the lighting system, air conditioning and experimental equipment are turned on; the environmental parameters refer to the temperature, humidity and light intensity data that affect students' learning comfort.

9. The integrated management system for a smart digital campus as claimed in claim 8, characterized in that: Combining the resource allocation results and the energy consumption data of each area, the energy consumption strategy is automatically optimized. The optimized learning environment includes the following steps: Detect the environmental status and equipment usage of classrooms and laboratory areas based on energy consumption data; According to the teaching resource scheduling and allocation results, determine whether the current area needs to maintain the current energy consumption status; Automatically generate energy consumption optimization strategies based on detection and judgment results; The energy consumption changes of each area are detected in real time, and based on the generated energy consumption optimization usage strategy, the power and switching status of physical devices are dynamically adjusted to obtain an optimized learning environment.

10. The integrated management system for a smart digital campus according to claim 9, characterized in that: Through the optimized learning environment, students' learning progress and learning efficiency are tracked in real time, and the learning path is dynamically adjusted in combination with students' personalized learning portraits and current learning environment. The following steps are included: Track students' learning progress in real time according to each student's learning path, and obtain students' progress in each learning step; Assess students' learning efficiency through their progress in each learning step; Evaluate students’ learning outcomes based on their performance data in the learning path; Dynamically adjust personalized learning paths based on students' learning performance and efficiency.

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