Personalized learning path planning method and system based on user behaviors, medium and equipment

Abstract

The invention relates to the field of personalized learning path planning, and discloses a personalized learning path planning method and a personalized learning path planning system based on user behaviors. Learning behaviors such as video watching, simulation practice and forum communication are generated; the method comprises the following steps: acquiring generated behavior states based on a system background database, analyzing weight scores of different behaviors, and constructing a user behavior state model; estimating and judging the learning result of the user according to the behavior state generated by the user and the internal weight score of the model; generating a personalized learning path by estimating a result generated by judgment; according to the method, the learning behavior state of the user is analyzed, and the personalized learning path is established, so that the problem of low matching degree between the conventional fixed learning path and the learning ability of the user is solved, the learning efficiency of the user is improved, and more accurate and suitable learning experience is provided for different users.

Description

Technical Field

[0001] The present invention relates to the technical field of personalized education technology, and in particular to a method, system, medium and device for planning a personalized learning path based on user behavior. Background Art

[0002] Online education and training platforms provide people with a wealth of learning resources, which cover areas ranging from basic knowledge to advanced skills. Users can find the learning content they need from these resources. In the process of learning these resources, users' needs are becoming more and more diverse and personalized. For example, different users have different requirements for the difficulty, form, rhythm, and duration of learning content. However, traditional course recommendation systems often rely on fixed learning paths or preset course combinations. This method is difficult to adapt to these diverse needs. Combined with the uncertainty of users' own personalities and their ability to understand knowledge points, when different users adopt traditional fixed course paths to learn a certain course, their learning motivation and efficiency are often far apart, which results in the original courses being unable to meet the personalized needs of users.

[0003] As a popular research direction in education and training, personalized learning paths are rapidly changing the traditional teaching model and providing users with a unique and effective learning experience. At present, research in the field of personalized learning paths mainly focuses on user personality characteristics (such as learning style, etc.). This method focuses on the user's intrinsic learning characteristics and realizes personalized education by inferring the user's learning preferences. However, it ignores the user's learning behavior and the degree of mastery of course knowledge points. The existing personalized learning system mainly uses the ant colony algorithm to improve the matching degree between courses and users. However, the ant colony algorithm has a stagnation phenomenon near the neighborhood of one or some local optimal solutions, resulting in premature convergence. That is, after the search reaches a certain extent, the solutions found by all ants are exactly the same, and the solution space cannot be further searched. In personalized learning, the user's knowledge level, interests and learning goals often change continuously, which leads to a low adaptation rate of the learning resources recommended by the algorithm or the planned learning paths for learners, and a poor personalized experience for users. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a personalized learning path planning method, system, medium and device based on user behavior, collects the learning behavior generated by the user in the learning process, and constructs a user behavior state model, evaluates the user's learning achievements, establishes a personalized learning path, improves the course matching degree, improves the user's learning efficiency, and increases the user's sense of achievement in learning. Therefore, in order to achieve the above technical effects, the present invention provides the following technical solutions: A personalized learning path planning method based on user behavior, comprising the following steps:

[0005] Step S1: collecting user learning behavior data based on the system background database, wherein the user behavior data includes video viewing data, simulation practice data, and forum communication data;

[0006] According to the above technical solution, the video viewing data further includes the number of video playbacks and the total playback time, the simulation exercise data further includes the simulation exercise score, and the forum communication data further includes the number of forum posts and comments.

[0007] Step S2: Classify the collected user learning behavior data, and construct a user behavior status model according to the classification results, wherein the user behavior status model is composed of course completion, course mastery and community participation;

[0008] According to the above technical solution, the collected user learning behavior data is classified into: video viewing behavior is divided into course completion status, simulation practice behavior is divided into course mastery status, forum communication behavior is divided into course participation status, and the user behavior state model is constructed according to the classification results after the division;

[0009] According to the above technical solution, the user behavior status model is composed of course completion, course mastery and community participation. Course completion refers to the number of video playbacks and the total playback time, course mastery refers to the simulation exercise score, and community participation refers to the number of forum posts and comments.

[0010] For example, based on the total duration and number of times a user plays a video, the following course completion calculation formula is defined:

[0011]

[0012] Among them, num and NUM represent the number of times the user plays the video and the total number of course videos, respectively; time and TIME represent the cumulative duration of the user playing the video and the total duration of the course videos, respectively;

[0013] For example, based on the score of each simulation exercise completed by the user, the following course mastery calculation formula is defined:

[0014]

[0015] Among them, grade(i) represents the score of the user's ith simulation exercise, N represents the total number of simulation exercises performed by the user,

[0016] Specifically, the following community participation calculation formula is defined based on the number of posts and comments in the user forum:

[0017]

[0018] Among them, post(i) represents the influence factor of user forum posts, dis(j) represents the effective factor of user comments, and n and m respectively represent the number of user forum posts and the number of user comments;

[0019] According to the above technical solution, based on the n user behavior data collected from the system background database of users who have completed all courses, the analytic hierarchy process and its calculation formula are used to judge and measure the parameter weights of the model. The calculation formula of the analytic hierarchy process is as follows:

[0020]

[0021] Among them, ɑ ij represents the importance degree of the i-th element to the j-th element, and ɑ kj represents the importance degree of the k-th element to the j-th element;

[0022] Exemplarily, to obtain the parameter weights of the model, the following user behavior state model weight score formula is defined:

[0023] g(x) = ɑ * read + β * sco + γ * com

[0024] Among them, read is the defined course completion degree, sco is the defined course mastery degree, com is the defined community participation degree, and ɑ, β, and γ respectively represent the weights of the course completion degree, the course mastery degree, and the community participation degree.

[0025] Step S3: Use the user behavior state model to evaluate and judge the learning achievements of users. The learning achievements of the users include the weight score of the user behavior state model and the course mastery degree;

[0026] According to the above technical solution, for the evaluation and judgment of the learning achievements of users, based on the n user simulation exercise data collected from the system background database of users who have completed all courses, the mastery degree and experience value algorithm formula is used to evaluate g(x) and sco. The mastery degree and experience value algorithm formula is as follows:

[0027]

[0028] Among them, degree is the minimum requirement for users to master the course, norm is the normalization coefficient, g(x) i is the behavior state of the i-th user, sco i is the course mastery degree of the i-th user. When g(x) ≥ degree, the mastery degree of the user's knowledge points of the course is marked as fully mastered. When g(x) < degree, the mastery degree of the user's knowledge points of the course is marked as not fully mastered, and the mastery degree of the user's learned course knowledge points is obtained

[0029] Step S4: Iteratively optimize the evaluation and judgment results of the user's learning outcomes through a dynamic adaptive function and a dynamic adaptive genetic algorithm to generate a personalized learning path.

[0030] According to the above technical solution, the dynamic adaptive function further includes: improving the fitness function in the original genetic algorithm, and using the improved dynamic adaptive function to calculate the real-time fitness score of the course;

[0031] The improved dynamic adaptive function formula is as follows:

[0032]

[0033] Among them, θ(x i ) is the real-time fitness score of the i-th course, is the real-time fitness score of the i-th course before dynamic update, g(x i ) is the user's mastery of the i-th course, degree i is the minimum requirement for users to master the i-th course, δ(x i ) is the efficiency of the user learning the i-th course;

[0034] Furthermore, the dynamic fitness function formula is as follows:

[0035]

[0036] Among them, p i is the probability of recommending the i-th course, θ(x i ) is the real-time fitness score of the i-th course, f(x i ) is the recommended value of the i-th course, and N is the total number of courses;

[0037] Finally, a dynamic adaptive genetic algorithm is used, combined with iterative optimization of user learning progress, to output the optimized path as the personalized learning path recommendation result based on user behavior.

[0038] According to the above technical solution, the method for personalized learning path planning based on user behavior is performed by a personalized learning path planning system based on user behavior, and the system includes the following modules:

[0039] A user learning behavior collection module is used to collect user learning behavior data from the system;

[0040] The user behavior state model building module is used to analyze and set the weight scores corresponding to different learning behaviors of users and build a user behavior state model;

[0041] The user learning results analysis module is used to evaluate the user's learning outcomes through the user's learning behavior and state model, and calculate the user's mastery of the course knowledge points;

[0042] The personalized learning path planning module is used to iteratively optimize the user's learning path using a dynamic adaptive genetic algorithm, and dynamically update and recommend personalized learning paths based on user behavior in real time.

[0043] According to the above technical solution, the present invention also discloses a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

[0044] According to the above technical solution, the present invention also discloses a computer device, including a memory, a processor and computer instructions stored in the memory and running on the processor. When the processor runs the computer instructions, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0046] (1) The present invention collects the learning behaviors generated by users during the learning process, sets weight scores for different behaviors, and constructs a user behavior state model, which helps users understand their learning behaviors and provides theoretical guidance for subsequent personalized learning path planning and design;

[0047] (2) Integrate user learning behaviors into personalized learning paths, and timely adjust learning paths and content based on user learning progress and the degree of mastery of course knowledge points, improve the match between users and courses, enhance user learning efficiency and experience, and ensure user learning quality;

[0048] (3) To solve the problem of learning path optimization, the present invention adopts a dynamic adaptive genetic algorithm, which has the ability of fast random search and is independent of the problem domain. It can find the global optimal solution among all courses, and has fast computing speed and strong robustness. It meets the needs of real-time dynamic update and can improve the accuracy of course recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0050] In the attached picture:

[0051] Figure 1 A flow chart of a personalized learning path planning method based on user behavior is proposed for the present invention;

[0052] Figure 2 This is a schematic diagram of the user behavior state model of the present invention;

[0053] Figure 3 This is a flowchart of personalized learning path planning of the present invention;

[0054] Figure 4 The present invention proposes a structure diagram of a personalized learning path planning system based on user behavior. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, the present invention provides a personalized learning path planning method based on user behavior, including the following:

[0057] Step S1: Collect user learning behavior.

[0058] When users are studying a certain chapter of a course, they will generate a series of learning behaviors, such as video watching, simulation exercises, and forum exchanges. The system background automatically records and updates the learning behaviors generated by users during the learning process.

[0059] Step S2: Construct a user behavior state model.

[0060] The learning behaviors collected in step S1 are classified, and the video viewing behavior is divided into the course completion status, the simulation practice behavior is divided into the course mastery status, and the forum communication behavior is divided into the course participation status.

[0061] According to the classification results after division, a user behavior state model is constructed, such as Figure 2 As shown in the figure, the user behavior status model is composed of course completion, course mastery and community participation. Course completion refers to the number of video plays and the total play time, course mastery refers to the simulation exercise score, and community participation refers to the number of forum posts and comments.

[0062] Based on the total duration and number of times users play videos, we define a formula for calculating course completion. The formula is as follows:

[0063]

[0064] Among them, num and NUM represent the number of times the user plays the video and the total number of course videos, respectively; time and TIME represent the cumulative duration of the user playing the video and the total duration of the course videos, respectively;

[0065] According to the scores of each simulation exercise completed by the user, a course mastery calculation formula is defined as follows:

[0066]

[0067] Among them, grade(i) represents the score of the user's i-th simulation exercise, and N represents the total number of simulation exercises performed by the user.

[0068] Combining the number of posts and comments on user forums, we define a formula for calculating community participation. The formula is as follows:

[0069]

[0070] Among them, post(i) represents the influence factor of user forum posts, dis(j) represents the effectiveness factor of user comments, n and m represent the number of user forum posts and user comments, respectively;

[0071] Obtain the behavior data of n users who have completed all courses in the system platform, and use the hierarchical analysis method and its calculation formula to judge and measure the parameter weights of the model. The calculation formula of the hierarchical analysis method is as follows:

[0072]

[0073] Among them, ij is the importance of the i-th element to the j-th element, ɑ kj is the importance of the k-th element to the j-th element.

[0074] Get the parameter weights of the model and define the weight score formula of the user behavior status model. The formula is as follows:

[0075] g(x)=ɑ*read+β*sco+γ*com

[0076] Among them, read is the defined course completion, sco is the defined course mastery, com is the defined community participation, and ɑ, β, and γ represent the weights of course completion, course mastery, and community participation, respectively.

[0077] Step S3: Estimate and determine the user's learning outcomes.

[0078] Get the simulated practice data of n users who have completed all courses from the system platform, and select the mastery and experience value algorithm to evaluate g(x) and sco in step S2. The mastery and experience value algorithm formula is as follows:

[0079]

[0080] Among them, degree is the minimum requirement for users to master the course, norm is the normalization coefficient, and g(x) i is the behavior state of the i-th user, sco i is the course mastery of the ith user.

[0081] When g(x)≥degree, it is recorded that the user has fully mastered the knowledge points of this chapter. Otherwise, it is recorded that the user has not fully mastered the knowledge points of this chapter. Finally, the user's mastery of the knowledge points of the learned course is obtained.

[0082] Step S4: Generate a personalized learning path.

[0083] Before studying this series of courses for the first time, the system randomly assembles a set of test papers from the test question set and conducts a baseline test on the user. It determines the user's understanding of the relevant knowledge points based on the accuracy of the exercises and roughly forms the original learning path.

[0084] From step S3, the user's mastery of the knowledge points of the learned course is obtained, such as Figure 3 As shown, the next stage of course learning path is determined according to the user's mastery of the course knowledge points; if the user has not fully mastered the course knowledge points of this chapter, the course learned is reviewed and related courses are pushed for consolidation; if the user has fully mastered the course knowledge points of this chapter, the next stage of learning is carried out.

[0085] The fitness function in the original genetic algorithm is improved, and the system obtains the real-time fitness score of the course through the improved dynamic adaptive function.

[0086] The improved dynamic adaptive function formula is as follows:

[0087]

[0088] Among them, θ(x i ) is the real-time fitness score of the i-th course, is the real-time fitness score of the i-th course before dynamic update, g(x i ) is the user's mastery of the i-th course, degree i is the minimum requirement for users to master the i-th course, δ(x i ) is the efficiency of the user learning the i-th course.

[0089] By using a dynamic adaptive genetic algorithm and combining iterative optimization with the user's learning progress, the optimized path is finally selected as the user's learning path recommendation result.

[0090] Among them, the formula of the dynamic fitness function is as follows:

[0091]

[0092] Among them, p i is the recommendation probability of the i-th course, θ(x i ) is the real-time fitness score of the i-th course, f(x i ) is the recommendation value of the i-th course, and N is the total number of courses.

[0093] Example 2

[0094] Exemplarily, the background of the online training system collects the learning data of a certain user, obtains the learning behavior of the user for a certain course. The course completion rate read = 0.9, the course mastery sco = 0.8, and the community participation com = 0.75 of this user; based on the defined weight score formula of the user behavior state model g(x) = ɑ * read + β * sco + γ * com, where the course completion weight ɑ = 40, the course mastery weight β = 20, and the community participation weight γ = 20, calculate the user's mastery degree of this course g(x) = 83; and obtain the minimum requirement degree = 85 for the user to fully master this course from 100 user data that have completed learning this course in the system platform. Since g(x) < degree, the system determines that the user has not fully mastered this course; the system adjusts the real-time fitness score θ(x) of this course according to the improved dynamic adaptive function, and allows the user to review the learned course again and push relevant courses for consolidation.

[0095] Example 3

[0096] As Figure 4 shown, this embodiment proposes a personalized learning path planning system based on user behavior, including the following modules:

[0097] User learning behavior collection module: Collect user learning data from the training system background log, and obtain learning behaviors such as user video viewing, simulation practice, and forum communication;

[0098] User behavior state model construction module: Analyze and set the weight scores corresponding to different learning behaviors according to the user's learning behaviors, and construct a user behavior state model;

[0099] User learning result analysis module: Evaluate the user's learning achievements through the user's learning behaviors and state model, and infer the user's mastery degree of the course knowledge points;

[0100] Personalized learning path planning module: Generates an initial personalized learning path for the course knowledge points that the user is weak in. As the user's learning progresses, it uses a dynamic adaptive genetic algorithm to iteratively optimize, dynamically update the learning path in real time, delete knowledge points that have been fully mastered, add knowledge points that need to be fully mastered, and recommend them to the user.

[0101] Embodiment 4

[0102] This embodiment provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

[0103] Embodiment 5

[0104] This embodiment provides a computer device, including a memory, a processor, and computer instructions stored in the memory and running on the processor. When the processor runs the computer instructions, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

[0105] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 Steps for specifying functions in a box or boxes

[0109] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0110] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A personalized learning path planning method based on user behavior, characterized by: The following steps are involved: Step S1: collecting user learning behavior data based on the system background database, wherein the user behavior data includes video viewing data, simulation practice data, and forum communication data; Step S2: Classify the collected user learning behavior data, and construct a user behavior status model according to the classification results, wherein the user behavior status model is composed of course completion, course mastery and community participation; Step S3: using the user behavior state model to evaluate and judge the user's learning outcomes, wherein the user's learning outcomes include the weight score of the user behavior state model and the course mastery; Step S4: Iteratively optimize the evaluation and judgment results of the user's learning outcomes through a dynamic adaptive function and a dynamic adaptive genetic algorithm to generate a personalized learning path.

2. The method for personalized learning path planning based on user behavior according to claim 1, characterized in that: The video viewing data further includes the number of video playbacks and the total playback duration, the simulation exercise data further includes the simulation exercise scores, and the forum communication data further includes the number of forum posts and the number of comments.

3. The method for personalized learning path planning based on user behavior according to claim 1, characterized in that: The classification of the collected user learning behavior data includes: dividing video viewing behavior into course completion status, dividing simulation practice behavior into course mastery status, and dividing forum communication behavior into course participation status, and constructing a user behavior status model based on the classification results after division.

4. The method for personalized learning path planning based on user behavior according to claim 3, characterized in that: The user behavior status model is composed of course completion, course mastery and community participation. The course completion is the number of video playbacks and the total playback time. The course mastery is the simulation exercise score. The community participation is the number of forum posts and comments. Based on the total duration and number of times users play videos, the following course completion calculation formula is defined: Among them, num and NUM represent the number of times the user plays the video and the total number of course videos, respectively; time and TIME represent the cumulative duration of the user playing the video and the total duration of the course videos, respectively; According to the scores of each simulation exercise completed by the user, the following course mastery calculation formula is defined: Among them, grade(i) represents the score of the user's ith simulation exercise, N represents the total number of simulation exercises performed by the user, Further, based on the number of posts and comments in the user forum, the following community participation calculation formula is defined: Among them, post(i) represents the influence factor of user forum posts, dis(j) represents the effectiveness factor of user comments, n and m represent the number of user forum posts and user comments, respectively; Based on the behavioral data of n users who have completed all courses collected by the system background database, the analytic hierarchy process and its calculation formula are used to judge and measure the parameter weights of the model. The calculation formula of the analytic hierarchy process is as follows: Among them, ij is the importance of the i-th element to the j-th element, ɑ kj is the importance of the kth element to the jth element; Get the parameter weights of the model and define the following user behavior state model weight score formula: g(x)=ɑ*read+β*sco+γ*com Among them, read is the defined course completion, sco is the defined course mastery, com is the defined community participation, and ɑ, β, and γ represent the weights of course completion, course mastery, and community participation, respectively.

5. A method for personalized learning path planning based on user behavior according to claim 4, characterized in that: The evaluation and judgment of the user's learning outcomes is based on the simulated practice data of n users who have completed all courses collected by the system background database, and the mastery and experience value algorithm formula is used to evaluate g(x) and sco. The mastery and experience value algorithm formula is as follows: where degree is the minimum requirement for the user to master the course, norm is the normalization coefficient, and g(x) i is the behavior status of the i-th user, and sco i is the course mastery degree of the i-th user. When g(x) ≥ degree, the mastery degree of the user for the course knowledge points is marked as fully mastered. When g(x) < degree, the mastery degree of the user for the course knowledge points is marked as not fully mastered, and the mastery degree of the user for the learned course knowledge points is obtained.

6. A method for personalized learning path planning based on user behavior according to claim 5, characterized in that: The dynamic adaptive function further comprises: improving the fitness function in the original genetic algorithm, and using the improved dynamic adaptive function to calculate the real-time fitness score of the course; The improved dynamic adaptive function formula is as follows: Among them, θ(x i ) is the real-time fitness score of the i-th course, is the real-time fitness score of the i-th course before dynamic update, g(x i ) is the user's mastery of the i-th course, degree i is the minimum requirement for users to master the i-th course, δ(x i ) is the efficiency of the user learning the i-th course; Furthermore, the dynamic fitness function formula is as follows: Among them, p i is the probability of recommending the i-th course, θ(x i ) is the real-time fitness score of the i-th course, f(x i ) is the recommended value of the i-th course, and N is the total number of courses; Finally, a dynamic adaptive genetic algorithm is used, combined with iterative optimization of user learning progress, to output the optimized path as the personalized learning path recommendation result based on user behavior.

7. A personalized learning path planning system based on user behavior, characterized by: The system includes the following modules: A user learning behavior collection module is used to collect user learning behavior data from the system; The user behavior state model building module is used to analyze and set the weight scores corresponding to different learning behaviors of users and build a user behavior state model; The user learning results analysis module is used to evaluate the user's learning outcomes through the user's learning behavior and state model, and calculate the user's mastery of the course knowledge points; The personalized learning path planning module is used to iteratively optimize the user's learning path using a dynamic adaptive genetic algorithm, and dynamically update and recommend personalized learning paths based on user behavior in real time.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and computer instructions stored in the memory and running on the processor. When the processor runs the computer instructions, the steps in the above-mentioned personalized learning path planning method based on user behavior are implemented.

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