Personalized music teaching content pushing system

Abstract

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of personalized push technology, and in particular to a personalized music teaching content push system. Background Art

[0002] The field of personalized push technology focuses on customizing and providing content or services based on users' specific needs, preferences, behavior history, and interactive feedback. In education and learning environments, especially in music teaching, personalized push technology is committed to optimizing the allocation and use of teaching resources to improve learning efficiency and effectiveness. By analyzing the user's learning progress, preferences, and feedback, it can recommend the teaching content that best suits the user's current learning stage and interests.

[0003] Among them, the personalized music teaching content push system is a technical solution that uses user data, such as learning history, preferences, and behavior, to automatically provide customized music teaching resources. The purpose of this system is to improve the personalization level and efficiency of learning by accurately matching users' personalized learning needs and interests. It aims to achieve the effect of increasing users' learning motivation, optimizing the use of learning resources, and enhancing learning outcomes. It supports more efficient and targeted interactions between music educators and learners, thereby promoting the depth and breadth of music learning.

[0004] The traditional music teaching content push system lacks accurate assessment and personalized analysis of learners' abilities, which results in the teaching content not being able to effectively match the actual needs of each learner, and the mismatch between the teaching content and the learners' actual abilities and interests, which affects learning efficiency and motivation. In terms of path planning and push strategies, it mostly relies on static planning and simple algorithms, lacks personalized and dynamic adjustment capabilities, resulting in unreasonable allocation of learning resources, and learners cannot obtain the most suitable learning content. In terms of behavior analysis and teaching effect feedback, simple statistical methods are used, which cannot accurately predict learners' behaviors and needs, and it is difficult to provide effective teaching adjustments, which affects learning outcomes and efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a personalized music teaching content push system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a personalized music teaching content push system, the system includes a user ability assessment module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module, and a personalized recommendation module;

[0007] The user ability assessment module uses a clustering algorithm to classify learners' skill levels based on the practice recordings, homework and test results submitted by learners, and uses a support vector machine algorithm and a random forest algorithm to perform data feature analysis and assessment on learners' music theory knowledge, performance skills and creative ability to generate ability assessment results;

[0008] The teaching content matching module uses the K-means clustering algorithm based on the ability assessment results to quantitatively analyze the learner skill distribution, identify learner groups with differentiated skill levels, analyze teaching objectives, content difficulty and learner preferences through the hierarchical analysis method, formulate teaching content priorities corresponding to differentiated skill levels, and generate a teaching content matching plan;

[0009] The learning path planning module uses the Dijkstra algorithm to plan the learning path based on the teaching content matching solution, and combines the learner's personal preferences and learning progress to design a multi-stage learning path through a dynamic programming method to generate personalized learning path information;

[0010] The push strategy optimization module uses Q learning and policy gradient methods to adjust the push strategy based on personalized learning path information, learns and formulates the push strategy through reinforcement learning technology, and generates an optimized push strategy;

[0011] The behavior analysis and prediction module is based on the optimization push strategy, combined with the learner's music learning interaction data, using the long short-term memory network and seasonal decomposition time series, to analyze the learner's behavior pattern and periodic changes, predict the learner's future behavior and needs, and generate behavior prediction results;

[0012] The teaching effect feedback module evaluates the teaching effect based on the behavior prediction results and the learners' feedback on the pushed content through item response theory and Markov decision process, and formulates matching teaching adjustment strategies to generate teaching effect improvement strategy information;

[0013] The dynamic adjustment and optimization module adjusts and optimizes the learning path and teaching content based on the teaching effect improvement strategy information, combined with the teaching effect feedback, using the dynamic programming algorithm and the Bayesian network, by analyzing the learner's progress, effect feedback and change needs, and generates the adjusted learning path and teaching content;

[0014] The personalized recommendation module is based on the adjusted learning path and teaching content, uses a deep Q network model and Monte Carlo tree search technology to optimize the personalized recommendation strategy, analyzes the relationship between learner behavior, preferences and teaching content through a deep learning model, adjusts the push timing and content, and generates a personalized recommendation plan.

[0015] The present invention has improvements in that the ability assessment result includes music theory knowledge score, performance skill level, and creative ability evaluation; the teaching content matching plan includes recommended music theory teaching materials, skill practice repertoire, and creative guidance courses; the personalized learning path information includes stage learning goals, recommended learning materials, and expected completion time; the optimized push strategy includes content push frequency, push time period selection, and content preference type; the behavior prediction result includes active learning time period, content preference analysis results, and potential learning obstacle information; the teaching effect improvement strategy information includes teaching content adjustment plan, learning method optimization plan, and auxiliary material update demand information; the adjusted learning path and teaching content include learning material column information, learning progress schedule, and personalized learning plan; the personalized recommendation plan includes recommended teaching video information, recommended music scores and works, and practice tasks.

[0016] The present invention is improved in that the user capability assessment module includes a skill level classification submodule, a data feature analysis submodule, and a comprehensive assessment submodule;

[0017] The skill level classification submodule adopts a clustering algorithm based on the practice recordings, homework and test results submitted by the learners, uses the scikit-learn library to perform K-Means clustering, sets the number of clusters, matches the learner ability level classification, uses the Euclidean distance for distance calculation, sets the number of iterations to 100, groups the learners, and generates skill level classification results;

[0018] The data feature analysis submodule uses the support vector machine algorithm and the random forest algorithm based on the skill level classification results, and uses the Python Scikit-learn library to perform data feature analysis on the learner's music theory knowledge, playing skills and creative ability. By executing the SVC function and the RandomForestClassifier function, key features are extracted from the learner's practice recordings, homework and test results, and the factors that affect the learner's skill level are identified to generate data feature analysis results.

[0019] The comprehensive assessment submodule is based on the data feature analysis results, adopts the weighted decision matrix method, and combines Python's NumPy library to conduct a comprehensive ability assessment analysis. According to the importance of each skill to the learner's overall ability, weights are assigned to the assessment dimensions of music theory knowledge, playing skills, and creative ability, and weight calculations are performed. The weighted decision matrix is ​​applied to multiply the learner's score on the differentiated skill dimension by the corresponding weight, and the comprehensive ability score of each learner is accumulated to generate the ability assessment result.

[0020] The present invention is improved in that the teaching content matching module includes a skill quantification analysis submodule, a differentiation identification submodule, and a teaching content priority setting submodule;

[0021] The skill quantitative analysis submodule uses the K-means clustering algorithm based on the ability assessment results and uses Python's Scikit-learn library to perform quantitative analysis of learners' skill distribution. By inputting learners' ability assessment data, including scores of music theory knowledge, playing skills, and creative ability, clustering is performed through the KMeans function to divide learners into differentiated groups according to their skill levels, and generate skill quantitative analysis results;

[0022] The differentiation identification submodule uses statistical analysis methods based on the skill quantitative analysis results, uses Python's Pandas library and NumPy library to perform differentiated identification of skill levels among learner groups, uses the Pandas library to organize and group data, uses the NumPy library to perform mathematical operations, calculates the mean and standard deviation statistical indicators of differentiated group skill scores, identifies the skill differences among differentiated learner groups, and generates differentiated identification results;

[0023] The teaching content priority setting submodule is based on the differentiated identification results, adopts the hierarchical analysis method, uses Python's PyAHP library, inputs the importance of teaching objectives, the difficulty level of teaching content and the weight of learners' preferences, and formulates the teaching content priorities for learners with differentiated skill levels by constructing a judgment matrix and calculating the weight vector, and generates a teaching content matching plan.

[0024] The present invention is improved in that the learning path planning module includes a learning path design submodule, a learning preference integration submodule, and a multi-stage path planning submodule;

[0025] The learning path design submodule is based on the teaching content matching scheme, adopts the Dijkstra algorithm, and uses the NetworkX library to build a graph model. The nodes represent the teaching units, and the edges represent the logical relationship between the teaching units. The weight of the edge is set by the add_weighted_edges_from function in the NetworkX library to reflect the transition difficulty between the teaching units, and the dijkstra_path function is used to calculate the shortest path of all nodes to generate the path basic framework;

[0026] The learning preference integration submodule is based on the path basic framework, adopts the collaborative filtering algorithm, and uses the pairwise_distances function in the Scikit-learn library to calculate the similarity between the user preference vector and the content feature, adjusts the learning path according to the historical behavior data and the preferences of similar users, and captures the teaching content that matches the current learner's preferences through the NearestNeighbors model to generate a preference adjustment path;

[0027] The multi-stage path planning submodule adjusts the path based on preferences, adopts a dynamic programming method, uses the NumPy library, defines multi-stage learning goals, allocates learning resources, dynamically adjusts progress parameters according to the learner's current progress and future goals, and calculates the multi-stage optimal learning path through a dynamic programming algorithm, including minimizing learning costs and maximizing learning effects, and generates personalized learning path information.

[0028] The present invention is improved in that the push strategy optimization module includes a push strategy adjustment submodule, a push strategy learning submodule, and a push strategy implementation submodule;

[0029] The push strategy adjustment submodule adopts the Q learning algorithm based on the personalized learning path information, uses the NumPy library, sets the initial Q table, assigns initial values ​​to the state-behavior pairs, and defines the reward mechanism, including increasing the reward value for the learner's positive feedback on the pushed content and reducing the reward value for negative feedback. The learning rate parameter is set to 0.1 and the discount factor is set to 0.9. Through an iterative process, the Q value is updated according to the learner's feedback, and the behavior with the maximum Q value is selected as the push strategy to generate the basic push strategy;

[0030] The push strategy learning submodule is based on the basic push strategy, adopts the policy gradient method, and uses the TensorFlow and Keras libraries to build a network model, including an input layer, a hidden layer, and an output layer. The hidden layer uses the ReLU activation function, and the output layer uses the softmax function to generate a probability distribution. The learning rate is set to 0.01, and the learner feedback is used as a reward signal. Through the back propagation algorithm, the network parameters are adjusted, the policy gradient is optimized, and the learner characteristics and the current learning state are input. The probability distribution of the push behavior is output to generate the push strategy analysis results;

[0031] The push strategy implementation submodule uses the Monte Carlo method and the SciPy library based on the push strategy analysis results to calculate and sample the probability distribution. It analyzes the learner's current learning progress and preferences and uses the optimized strategy network to formulate the push content and frequency, including calculating the probability value corresponding to each push content, selecting the content with a high probability value as the push option, and analyzing the learner's learning time preference to select the push timing, thereby generating an optimized push strategy.

[0032] The present invention is improved in that the behavior analysis and prediction module includes a behavior pattern analysis submodule, a periodic change analysis submodule, and a behavior prediction submodule;

[0033] The behavior pattern analysis submodule is based on the optimization push strategy, adopts the long short-term memory network, uses the TensorFlow and Keras libraries to build a model, analyzes the learner's music learning interaction data, and uses the learner's interaction sequence data, including click-through rate, learning time and frequency, as input. The number of hidden layer nodes is set to 128, the relu activation function is used, and the output layer adopts the softmax function to identify and summarize the learner's behavior pattern, including learning preferences and active time periods. The batch size is set to 32 and the number of iterations is set to 100. Model training is performed to generate behavior pattern analysis results;

[0034] The cyclical change analysis submodule adopts the seasonal decomposition time series method based on the behavior pattern analysis results, uses the statsmodels library to perform seasonal decomposition, analyzes the cyclical changes of learner behavior, decomposes the time series data into trend, seasonal and residual components, inputs the learner's interactive time series data through the seasonal_decompose function, sets the model as an additive model, reveals the cyclical law of learner behavior, and generates cyclical change analysis results;

[0035] The behavior prediction submodule adopts the long short-term memory network based on the periodic change analysis results, adjusts the long short-term memory network to the prediction mode, inputs the behavior pattern and periodic change data, sets the number of hidden layer nodes to 128, uses the sigmoid activation function to match the prediction task, and uses the linear function in the output layer to predict the learner's future behavior and needs and generate behavior prediction results.

[0036] The present invention is improved in that the teaching effect feedback module includes a teaching effect evaluation submodule, a teaching adjustment strategy formulation submodule, and a teaching effect improvement submodule;

[0037] The teaching effect evaluation submodule is based on the behavior prediction results, adopts the item response theory, and uses the ltm package of the R language to analyze and evaluate the learners' feedback on the pushed content. By inputting the learners' response data to the differentiated pushed content, including the accuracy rate and response time, the grm function is used to perform a multi-parameter logical model analysis to evaluate the sensitivity of the learning content to the learners' ability level and the learners' mastery of the target teaching content, and generate the teaching effect evaluation results;

[0038] The teaching adjustment strategy formulation submodule is based on the teaching effect evaluation results, adopts the Markov decision process, and uses Python's pymdptoolbox library to optimize the strategy. By inputting the teaching effect evaluation information, including the learners' mastery of and preference for differentiated teaching content, the state space is set as the differentiated combination of teaching content, the action space is set as the estimated teaching adjustment measures, and the reward function is set according to the degree of improvement of the teaching effect. It is iterated through the mdp.ValueIteration function to capture the optimal teaching adjustment strategy and generate a teaching adjustment strategy plan;

[0039] The teaching effect improvement submodule is based on the teaching adjustment strategy scheme, adopts a feedback adjustment algorithm, and uses Python's SciPy library for mathematical optimization. By inputting the difference between the target teaching effect and the current teaching effect, using the minimize function, setting the goal to minimize the difference between the teaching effect and the target, adjusting the difficulty level of the teaching content, changing the teaching method, and increasing personalized push content to match the needs of learners, the teaching effect improvement strategy information is generated.

[0040] The present invention is improved in that the dynamic adjustment and optimization module includes a learning path adjustment submodule, a teaching content optimization submodule, and a teaching dynamic optimization submodule;

[0041] The learning path adjustment submodule adopts a dynamic programming algorithm based on the teaching effect improvement strategy information, uses Python's NumPy library, analyzes the learner's progress and effect feedback, defines the state as the learner's current knowledge mastery level and learning progress, and the action as the learning path adjustment option. The objective function is to maximize learning efficiency and meet the learner's personalized needs. Through iterative calculation, the state value is updated, the optimal learning path is captured, and an optimized learning path plan is generated;

[0042] The teaching content optimization submodule is based on the optimization learning path scheme, adopts Bayesian network, uses Python's pgmpy library to build a model, analyzes learners' feedback and change needs on teaching content, calculates the conditional probability of differentiated teaching content adjustment schemes by inputting learners' mastery of current teaching content and satisfaction feedback, selects content adjustment schemes that improve teaching effects, and generates teaching content optimization schemes;

[0043] The teaching dynamic optimization submodule is based on the teaching content optimization plan and the learning path optimization plan, combined with teaching effect feedback, adopts an iterative feedback adjustment algorithm, uses Python's SciPy library, and performs mathematical optimization. Through the optimize.minimize function, the goal is set to minimize the difference between teaching content and learner needs. Through the feedback loop, the teaching strategy is adjusted to match the development of learners' abilities and changes in needs, and the adjusted learning path and teaching content are generated.

[0044] The present invention is improved in that the personalized recommendation module includes a recommendation strategy optimization submodule, a recommendation content adjustment submodule, and a recommendation timing optimization submodule;

[0045] The recommendation strategy optimization submodule adopts a deep Q network model based on the adjusted learning path and teaching content, uses TensorFlow and Keras libraries to build a deep learning model, inputs learner behavior data and the characteristics of the current teaching content, outputs the value evaluation of the recommended action, and evaluates the impact of the recommended action on the learning effect by defining a reward function. The learning rate is set to 0.01 and the discount factor is set to 0.9. The experience replay mechanism is used to improve the learning efficiency. The recommendation strategy is optimized by iterative training of the model to generate the recommendation strategy optimization result;

[0046] The recommended content adjustment submodule uses the Monte Carlo tree search technology based on the recommendation strategy optimization results and the Monte Carlo tree search library of Python to adjust the recommended content. It simulates the differentiated recommended content selection path by building a search tree, evaluates the long-term benefits of each choice, and selects the optimal recommended content path by simulating multiple decision-making processes, and generates a recommended content adjustment plan;

[0047] The recommendation timing optimization submodule is based on the recommendation content adjustment plan, uses the deep Q network model, combines the learner's learning activity pattern and preference, adopts the TensorFlow and Keras libraries, and defines the reward function according to the learner's activity time data and preference information. It rewards the action of recommending when the learner is able to accept new information. The learning rate is set to 0.005 and the discount factor is set to 0.95. The model is trained using the fit function. By identifying the effective time period of the learner's learning activity and the preferred time to receive new content, a personalized recommendation plan is generated.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, by adopting clustering algorithm, support vector machine and random forest algorithm, skill level division and data feature analysis of learners are carried out, and accurate assessment of user ability is achieved. By using K-means clustering algorithm and hierarchical analysis method, accurate matching of teaching content and learner ability is achieved. Dijkstra algorithm and dynamic programming method are applied to design multi-stage and personalized learning paths for learners. The combination of Q learning and policy gradient method optimizes the push strategy and improves learning efficiency. The use of long short-term memory network and seasonal decomposition time series enhances the understanding of learner behavior patterns and the ability to predict future needs. The introduction of item response theory and Markov decision process, as well as the application of dynamic programming algorithm and Bayesian network, improves the feedback and adjustment accuracy of teaching effect. The use of deep Q network model and Monte Carlo tree search technology optimizes personalized recommendation, improves the pertinence and attractiveness of teaching content, enhances learning motivation, and improves teaching efficiency and results. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A module diagram of a personalized music teaching content push system is proposed for the present invention;

[0051] Figure 2 A system framework diagram of a personalized music teaching content push system is proposed for the present invention;

[0052] Figure 3 The present invention proposes a user ability evaluation module schematic diagram in a personalized music teaching content push system;

[0053] Figure 4 The present invention proposes a teaching content matching module schematic diagram in a personalized music teaching content push system;

[0054] Figure 5 The present invention proposes a schematic diagram of a learning path planning module in a personalized music teaching content push system;

[0055] Figure 6 The present invention proposes a push strategy optimization module schematic diagram in a personalized music teaching content push system;

[0056] Figure 7 The present invention proposes a schematic diagram of a behavior analysis and prediction module in a personalized music teaching content push system;

[0057] Figure 8 The present invention proposes a teaching effect feedback module schematic diagram in a personalized music teaching content push system;

[0058] Fig. 9 The present invention proposes a schematic diagram of a dynamic adjustment and optimization module in a personalized music teaching content push system;

[0059] Fig.10 The present invention proposes a schematic diagram of a personalized recommendation module in a personalized music teaching content push system. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0062] Example

[0063] See also Figure 1 ,The present invention provides a technical solution: a personalized music teaching content push system, the system includes a user ability assessment module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module, and a personalized recommendation module;

[0064] The user ability assessment module uses a clustering algorithm to classify learners' skill levels based on the practice recordings, homework and test results submitted by learners. It also uses the support vector machine algorithm and random forest algorithm to analyze and evaluate the data characteristics of learners' music theory knowledge, performance skills and creative ability, and generate ability assessment results.

[0065] Based on the ability assessment results, the teaching content matching module uses the K-means clustering algorithm to quantitatively analyze the learners' skill distribution, identify learner groups with differentiated skill levels, and analyze the teaching objectives, content difficulty, and learners' preferences through the hierarchical analysis method. It formulates the teaching content priorities corresponding to differentiated skill levels and generates a teaching content matching plan.

[0066] The learning path planning module uses the Dijkstra algorithm to plan the learning path based on the teaching content matching solution. It also uses the dynamic programming method to design multi-stage learning paths and generate personalized learning path information in combination with the learner's personal preferences and learning progress.

[0067] The push strategy optimization module uses Q learning and policy gradient methods to adjust the push strategy based on personalized learning path information. It learns and formulates push strategies through reinforcement learning technology and generates optimized push strategies.

[0068] The behavior analysis and prediction module is based on the optimization push strategy, combined with the learners' music learning interaction data, using long short-term memory networks and seasonal decomposition time series to analyze the learners' behavior patterns and periodic changes, predict the learners' future behavior and needs, and generate behavior prediction results;

[0069] The teaching effect feedback module evaluates the teaching effect based on the behavior prediction results and the learners' feedback on the pushed content through item response theory and Markov decision process, and formulates matching teaching adjustment strategies to generate teaching effect improvement strategy information;

[0070] The dynamic adjustment and optimization module is based on the teaching effect improvement strategy information, combined with the teaching effect feedback, using the dynamic programming algorithm and Bayesian network, and by analyzing the learner's progress, effect feedback and change needs, it adjusts and optimizes the learning path and teaching content, and generates the adjusted learning path and teaching content;

[0071] The personalized recommendation module is based on the adjusted learning path and teaching content, and uses deep Q network model and Monte Carlo tree search technology to optimize the personalized recommendation strategy. Through the deep learning model, it analyzes the relationship between learner behavior, preferences and teaching content, adjusts the push timing and content, and generates personalized recommendation plans.

[0072] The ability assessment results include music theory knowledge scores, playing skill levels, and creative ability evaluation. The teaching content matching plan includes recommended music theory teaching materials, skill practice repertoires, and creative guidance courses. Personalized learning path information includes stage learning goals, recommended learning materials, and expected completion time. The optimized push strategy includes content push frequency, push time period selection, and content preference type. The behavior prediction results include active learning time periods, content preference analysis results, and potential learning obstacles. The teaching effect improvement strategy information includes teaching content adjustment plans, learning method optimization plans, and auxiliary materials update needs information. The adjusted learning path and teaching content include learning material list information, learning progress schedule, and personalized learning plans. The personalized recommendation plan includes recommended teaching video information, recommended music scores and works, and practice tasks.

[0073] In the user ability assessment module, practice recordings, homework and test results submitted by learners are received as input data. The data is formatted into feature vectors, which include musical indicators such as pitch accuracy, rhythm stability and musical expression. A clustering algorithm is used to perform a preliminary skill level division for learners. The algorithm iteratively optimizes the cluster centers to ensure that learners can be accurately grouped into the corresponding skill levels. The support vector machine algorithm and the random forest algorithm conduct a more in-depth data feature analysis and evaluation of music theory knowledge, performance skills and creative ability. By constructing a multidimensional feature space and using a support vector machine to determine the classification boundary, the random forest trains and evaluates the data set by constructing multiple decision trees, thereby improving the accuracy and robustness of the evaluation. Generate ability assessment results, including music theory knowledge scores, performance skill levels and creative ability evaluations, to provide basic data for subsequent modules.

[0074] In the teaching content matching module, based on the user ability assessment results, the K-means clustering algorithm is used to quantitatively analyze the learner skill distribution and establish learner groups with differentiated skill levels. Learners are divided into corresponding groups by calculating the distance between each learner and the cluster center. The analytic hierarchy process is used to analyze teaching objectives, content difficulty, and learner preferences, and to formulate teaching content priorities corresponding to differentiated skill levels. The analytic hierarchy process ensures that the teaching content matching plan is scientific and reasonable by constructing a judgment matrix, calculating weights, and performing consistency tests. The generated teaching content matching plan includes recommended music theory teaching materials, skill practice repertoires, and creation guidance courses, so as to accurately match the learners' personalized needs.

[0075] In the learning path planning module, the Dijkstra algorithm is used to plan the learning path based on the teaching content matching scheme. By constructing a learning node network, each node represents a learning content or stage, and the weight of the edge reflects the transition cost, that is, the difficulty or time required to transition from one learning stage to another. The Dijkstra algorithm is used to find the path with the smallest weight to ensure the optimal efficiency of the learning path. Combined with the learner's personal preferences and learning progress, the dynamic programming method is used to design multi-stage learning paths. Dynamic programming dynamically adjusts the learning path throughout the learning process by defining the state transition equation and the optimal substructure to adapt to the changes in learners' abilities and the evolution of personal preferences. The generated personalized learning path information includes stage learning goals, recommended learning materials, and expected completion time, providing learners with clear learning guidance.

[0076] In the push strategy optimization module, Q learning and policy gradient methods are used to adjust the push strategy based on personalized learning path information. Q learning, as a model-free reinforcement learning algorithm, learns the optimal strategy by updating the value function of the state-action pair. The policy gradient method directly updates the policy parameters by gradient ascent to optimize the strategy by evaluating the performance of the strategy. It can find a balance between exploration and utilization, and dynamically adjust push strategies such as content push frequency, push time period selection, and content preference type to maximize learners' learning efficiency and satisfaction. Through the application of reinforcement learning technology, push strategies are learned and formulated, and optimized push strategies are generated to ensure that the push of teaching content is more personalized, timely, and attractive.

[0077] In the behavior analysis and prediction module, combined with the learners' music learning interaction data and the optimized push strategy, the long short-term memory network and seasonal decomposition time series are used to analyze the learners' behavior patterns and periodic changes. The long short-term memory network captures the long-term dependencies and short-term change trends of learners' behaviors through time series analysis of learners' behavior data. The seasonal decomposition time series performs seasonal decomposition on the data, identifies and strips out the seasonal factors of learning behavior, and improves the accuracy of prediction. It can accurately analyze learners' behavior patterns and predict learners' future behaviors and needs. The generated behavior prediction results include active learning time periods, content preference analysis results, and potential learning obstacle information. This makes the teaching content push more in line with the actual needs of learners and improves the effect of personalized teaching.

[0078] In the teaching effect feedback module, item response theory and Markov decision process are used based on the behavior prediction results and learners' feedback on the pushed content. Item response theory analyzes learners' responses to various teaching contents, evaluates the difficulty of teaching contents and learners' ability levels, and thus achieves a more accurate teaching effect evaluation. Markov decision process optimizes teaching decisions and provides personalized teaching adjustment strategies by considering learners' learning status and possible behavior choices. It can comprehensively evaluate the teaching effect and formulate matching teaching adjustment strategies based on the evaluation results. The generated teaching effect improvement strategy information includes teaching content adjustment plan, learning method optimization plan, and auxiliary materials update demand information, so as to achieve continuous optimization of teaching content and methods and improve teaching quality and learning effectiveness.

[0079] In the dynamic adjustment and optimization module, based on the teaching effect improvement strategy information and combined with the teaching effect feedback, the dynamic programming algorithm and Bayesian network are used to adjust and optimize the learning path and teaching content. By defining the recursive form of the optimization problem, the dynamic programming algorithm can consider the long-term development of learners in the multi-stage decision-making process and optimize the planning of the learning path. The Bayesian network provides a method for processing uncertain information and performing probabilistic reasoning, which can make more reasonable adjustments to the teaching content based on incomplete feedback information. The learning path and teaching content can be adjusted in real time according to the learner's progress, effect feedback and changing needs. The generated adjusted learning path and teaching content include updated learning material column information, learning progress schedule, and personalized learning plan to ensure that the teaching strategy always remains the best fit for the learner's current needs.

[0080] In the personalized recommendation module, based on the adjusted learning path and teaching content, the deep Q network model and Monte Carlo tree search technology are used to optimize the personalized recommendation strategy. By integrating deep learning and reinforcement learning, the deep Q network can make effective decision-making learning when processing high-dimensional feature spaces. At the same time, the Monte Carlo tree search optimizes recommendation decisions by exploring and utilizing strategies, enhancing the exploration ability and accuracy of the recommendation system. It can deeply analyze the relationship between learners' behaviors, preferences and teaching content, and effectively adjust the timing and content of push. The generated personalized recommendation plan includes recommended teaching video information, recommended music scores and works, and practice tasks, providing more accurate and attractive learning resources, thereby enhancing learners' learning experience and efficiency.

[0081] See also Figure 2 and Figure 3 ,The user capability assessment module includes a skill level classification submodule, a data feature analysis submodule, and a comprehensive assessment submodule;

[0082] The skill level classification submodule uses a clustering algorithm based on the practice recordings, homework and test results submitted by learners. It uses the scikit-learn library to perform K-Means clustering, sets the number of clusters, matches the learner ability level classification, uses Euclidean distance for distance calculation, sets the number of iterations to 100, groups learners, and generates skill level classification results;

[0083] The data feature analysis submodule uses the support vector machine algorithm and random forest algorithm based on the skill level classification results, and uses Python's Scikit-learn library to perform data feature analysis on learners' music theory knowledge, playing skills, and creative abilities. By executing the SVC function and the RandomForestClassifier function, key features are extracted from learners' practice recordings, homework, and test results, factors that affect learners' skill levels are identified, and data feature analysis results are generated;

[0084] The comprehensive assessment submodule uses the weighted decision matrix method based on the data feature analysis results, combined with Python's NumPy library, to conduct a comprehensive ability assessment analysis. According to the importance of each skill to the learner's overall ability, weights are assigned to the assessment dimensions of music theory knowledge, playing skills, and creative ability, and weight calculations are performed. The weighted decision matrix is ​​applied to multiply the learner's score on the differentiated skill dimension by the corresponding weight, and the cumulative comprehensive ability score of each learner is obtained to generate the ability assessment result.

[0085] In the skill level classification submodule, by receiving practice recordings, homework and test results submitted by learners, the input data is formatted into feature vectors containing indicators such as pitch accuracy, rhythm stability and musical expression. K-Means clustering is performed using the scikit-learn library, the number of clusters is set to match the learners' ability level classification, the Euclidean distance is used as the distance calculation method, and the number of iterations is set to 100 to ensure the stability and accuracy of the clustering process. Learners are grouped into corresponding skill levels by calculating the Euclidean distance between the feature vector and the cluster center. The skill level classification results are generated, which provides an accurate basis for subsequent teaching content matching and personalized recommendations.

[0086] In the data feature analysis submodule, based on the skill level classification results, Python's Scikit-learn library is used to conduct in-depth data feature analysis of learners' music theory knowledge, performance skills, and creative abilities using support vector machine algorithms and random forest algorithms. By executing the SVC function and the RandomForestClassifier function, key features are extracted from learners' practice recordings, homework, and test results, such as the degree of understanding of music theory knowledge, the technical mastery of performance, and the innovativeness of creation. The extraction of key features and the identification of factors that affect learners' skill levels enable the system to more accurately evaluate and understand learners' specific abilities. The generated data feature analysis results provide an important basis for matching and recommending personalized teaching content.

[0087] In the comprehensive evaluation submodule, the system uses the weighted decision matrix method combined with Python's NumPy library to evaluate and analyze the learner's comprehensive ability based on the data feature analysis results. According to the importance of music theory knowledge, performance skills and creative ability to the learner's overall ability, the corresponding weights are assigned. By applying the weighted decision matrix, the learner's score on the differentiated skill dimension is multiplied by the corresponding weight, and the results are accumulated to obtain the comprehensive ability score of each learner. The generated ability assessment results not only provide a quantitative basis for learning path planning and push strategy optimization, but also lay the foundation for the continuous improvement and optimization of teaching effects.

[0088] Suppose a learner submits a practice recording and music theory test results, where the practice recording feature vector is [0.8 (pitch accuracy), 0.7 (rhythm stability), 0.6 (musical expression)], and the music theory test score is 80 points. In the skill level division submodule, K-Means clustering is used to identify the learner as having an intermediate skill level. In the data feature analysis submodule, support vector machines and random forest algorithms analyze their playing skills and music theory knowledge, and determine that their music theory knowledge score is 80 points, and their playing skills are evaluated as intermediate. In the comprehensive evaluation submodule, considering that the weights of music theory knowledge and playing skills on overall ability are 0.6 and 0.4 respectively, the learner's comprehensive ability score is 0.680+0.4 intermediate level corresponding score, and the final score is 76 points. Detailed ability assessment results are generated for learners, providing a scientific basis for their subsequent personalized music teaching content push and learning path planning.

[0089] See also Figure 2 and Figure 4 ,The teaching content matching module includes the skill quantification analysis submodule, the ,differentiation identification submodule, and the teaching content priority setting submodule;

[0090] The skill quantitative analysis submodule uses the K-means clustering algorithm based on the ability assessment results and uses Python's Scikit-learn library to conduct quantitative analysis of learners' skill distribution. By inputting learners' ability assessment data, including scores of music theory knowledge, playing skills, and creative ability, clustering is performed through the KMeans function to divide learners into differentiated groups according to their skill levels, generating skill quantitative analysis results.

[0091] The differentiation identification submodule uses statistical analysis methods based on the results of skill quantitative analysis, and uses Python's Pandas and NumPy libraries to perform differentiated identification of skill levels among learner groups. It uses the Pandas library to organize and group data, and uses the NumPy library to perform mathematical operations to calculate the mean and standard deviation statistical indicators of the skill scores of differentiated groups, identify the skill differences among differentiated learner groups, and generate differentiated identification results.

[0092] The teaching content priority setting submodule is based on the differentiated identification results, adopts the hierarchical analysis method, and uses Python's PyAHP library to input the importance of teaching objectives, the difficulty level of teaching content, and the weight of learners' preferences. By constructing a judgment matrix and calculating the weight vector, the teaching content priority of learners with differentiated skill levels is set, and a teaching content matching plan is generated.

[0093] In the skill quantitative analysis submodule, the K-Means clustering algorithm is executed through Python's Scikit-learn library to process the learner's ability assessment data. The input data is the score of the learner's music theory knowledge, playing skills and creative ability, formatted as a numerical feature vector. When executing the KMeans function, the preset number of clusters is based on the optimal number of groups determined by the preliminary analysis. By setting the init parameter to k-means++, the n_clusters parameter is adjusted according to actual needs to ensure the optimal clustering results. The fit_predict method is used to cluster the learner data and divide the learners into different groups according to their skill levels. The learners in each group have similar skill characteristics. The generated skill quantitative analysis results provide a scientific basis for the subsequent customization of teaching content and personalized recommendations, ensuring that the teaching strategy matches the learner's actual ability.

[0094] In the differentiation identification submodule, the skill quantitative analysis results are processed through Python's Pandas library and NumPy library. The Pandas library is used to organize the data, such as using the DataFrame structure to store the skill score data of each group of learners, and then grouping the learners through the groupby method to classify the learners based on the group. The NumPy library is used to perform mathematical operations, such as using the mean and std functions to calculate the mean and standard deviation of the skill scores of each group to identify the differences in skill levels between different learner groups. The generated differentiation identification results reveal the specific differences between the groups and provide a basis for formulating more targeted teaching strategies.

[0095] In the teaching content priority setting submodule, the analytic hierarchy process is used to set the priority of teaching content through Python's PyAHP library. The input includes the importance of teaching objectives, the difficulty level of teaching content, and the weight of learner preferences. The input data is obtained and formatted through questionnaires or historical data. The judgment matrix is ​​constructed using the PyAHP library, and the weight vector is calculated using the library's functions to ensure the consistency and scientificity of the decision-making process. The contribution of different teaching contents to the realization of teaching objectives can be quantitatively evaluated, and teaching content matching plans for learner groups with different skill levels can be generated to ensure the effective allocation and utilization of teaching resources.

[0096] Assume that the scores of music theory knowledge, performance skills and creative ability of three groups of learners are: [70, 60, 80], [85, 75, 90], [55, 65, 70]. In the skill quantitative analysis submodule, the n_clusters of K-Means clustering is set to 3, and the three groups are classified into 2 elementary, 8th and 5th level groups respectively. In the differentiation identification submodule, it is found through calculation that the average music theory knowledge score of the elementary group is 55, the performance skill is 65, and the creative ability is 70; the average of the intermediate group is 70, 60, 80; and the advanced group is 85, 75, 90. In the teaching content priority setting submodule, the importance weights of teaching objectives are set as music theory: 0.5, performance: 0.3, and creation: 0.2. After the AHP calculation, it may be determined that the advanced group is given priority to the high-difficulty performance skill improvement course, the intermediate group is given priority to the balanced development course of music theory and performance, and the elementary group is given priority to the basic knowledge of music theory. The teaching content matching program ensures that each group receives teaching resources that match their abilities and needs.

[0097] See also Figure 2 and Figure 5 ,The learning path planning module includes the learning path design submodule, the learning ,preference integration submodule, and the multi-stage path planning submodule;

[0098] The learning path design submodule is based on the teaching content matching scheme, adopts the Dijkstra algorithm, and uses the NetworkX library to build a graph model. Nodes represent teaching units, and edges represent the logical relationship between teaching units. The weight of the edge is set through the add_weighted_edges_from function in the NetworkX library to reflect the transition difficulty between teaching units. The dijkstra_path function is used to calculate the shortest path of all nodes and generate the basic framework of the path.

[0099] The learning preference integration submodule is based on the path basic framework, adopts the collaborative filtering algorithm, and uses the pairwise_distances function in the Scikit-learn library to calculate the similarity between the user preference vector and the content features. It adjusts the learning path according to the historical behavior data and the preferences of similar users. Through the NearestNeighbors model, it captures the teaching content that matches the current learner's preferences and generates a preference adjustment path.

[0100] The multi-stage path planning submodule adjusts the path based on preferences, adopts dynamic programming methods, uses the NumPy library, defines multi-stage learning goals, allocates learning resources, and dynamically adjusts progress parameters according to the learner's current progress and future goals. Through the dynamic programming algorithm, it calculates the multi-stage optimal learning path, including minimizing learning costs and maximizing learning effects, and generates personalized learning path information.

[0101] In the learning path design submodule, a graph model is constructed using Python's NetworkX library to accurately plan the learner's learning path. The nodes in the graph model represent teaching units, such as music theory foundation, harmony, rhythm practice, etc., while the edges represent the logical relationship and transition difficulty between teaching units. In actual operation, the transition difficulty between teaching units is calculated based on the evaluation of educational experts and historical learning data, and the weight of the edge is set in detail through the add_weighted_edges_from function. For example, the transition difficulty from music theory foundation to harmony is 2, and the transition difficulty from harmony to rhythm practice is 1. Using the dijkstra_path function, the shortest path from the starting node to all other nodes is calculated to optimize the learning process and ensure that learners receive the teaching content in the most reasonable order. The generated path foundation framework provides learners with a clear and logical learning sequence, which helps to improve learning efficiency.

[0102] In the learning preference integration submodule, based on the generated path basic framework, a collaborative filtering algorithm is used to carefully integrate learners' preferences. This is achieved by calculating the similarity between the learner's preference vector and the content features of each teaching unit, using the pairwise_distances function in the Scikit-learn library. Assuming that a learner has a high interest in harmony and a low interest in rhythm practice, the system identifies the teaching content that matches the learner's preferences by analyzing historical behavior data and the preferences of similar users, and adjusts the learning path to include more harmony learning content. Through the NearestNeighbors model, it is ensured that the learning path not only follows logic, but also fits the learner's personal preferences, thereby improving the enthusiasm and effectiveness of learning.

[0103] In the multi-stage path planning submodule, based on the preference-adjusted learning path, a dynamic programming method is used to set multi-stage learning goals and allocate learning resources. The progress parameters of the learning path are dynamically adjusted according to the learner's current learning progress and future learning goals. The NumPy library is used to define multi-stage learning goals. For example, the short-term goal may be to master the basics of music theory, the medium-term goal is to apply harmony knowledge, and the long-term goal is to perform complex rhythm exercises. By calculating the optimal path to achieve the goal at each stage, it aims to minimize the learning cost while maximizing the learning effect. The generated personalized learning path information provides a detailed learning plan, including recommended learning materials, expected completion time, and specific learning goals.

[0104] Assume that a learner has basic knowledge of music theory and wants to improve his harmony and composition skills. In the learning path design submodule, the constructed graph model is based on the teaching units: music theory foundation (A), harmony (B), composition skills (C), rhythm practice (D), and transition difficulty: A to B is 2, B to C is 1, and A to D is 3. Through calculation, the recommended path is ABC, which aims to strengthen the learner's harmony and composition skills. In the learning preference integration submodule, the analysis of learners' preferences and historical data shows that learners are more interested in composition skills than harmony, so the learning path is adjusted to ACB to introduce the learning of composition skills earlier. In the multi-stage path planning submodule, according to the learner's current mastery of music theory, the short-term goal is set to complete the elementary tutorial of harmony, the medium-term goal is to master basic composition skills, and the long-term goal is to complete the comprehensive application of advanced harmony and composition skills, ensuring the personalization and practicality of the learning path.

[0105] See also Figure 2 and Figure 6 ,The push strategy optimization module includes a push strategy adjustment submodule, a push strategy learning submodule, and a push strategy implementation submodule;

[0106] The push strategy adjustment submodule is based on personalized learning path information, adopts Q learning algorithm, uses NumPy library, sets initial Q table, assigns initial value to state-behavior pair, and defines reward mechanism, including increasing reward value for learner’s positive feedback on push content, reducing reward value for negative feedback, setting learning rate parameter to 0.1, discount factor to 0.9, and updating Q value according to learner’s feedback through iterative process, selecting the behavior with the largest Q value as push strategy, and generating basic push strategy;

[0107] The push strategy learning submodule is based on the basic push strategy and adopts the policy gradient method. It uses the TensorFlow and Keras libraries to build a network model, including input layer, hidden layer, and output layer. The hidden layer uses the ReLU activation function, and the output layer uses the softmax function to generate probability distribution. The learning rate is set to 0.01, and learner feedback is used as a reward signal. Through the back propagation algorithm, the network parameters are adjusted, the policy gradient is optimized, and the learner characteristics and current learning status are input. The probability distribution of push behavior is output to generate the push strategy analysis results;

[0108] The push strategy implementation submodule is based on the push strategy analysis results, uses the Monte Carlo method, and uses the SciPy library to calculate and sample probability distributions. By analyzing the learners' current learning progress and preferences, and using the optimized strategy network, it formulates push content and frequency, including calculating the probability value corresponding to each push content, selecting content with high probability values ​​as push options, and analyzing learners' learning time preferences to select push timing, thereby generating an optimized push strategy.

[0109] In the push strategy adjustment submodule, the push strategy of personalized learning path information is adjusted by using the NumPy library and the Q learning algorithm. Set the initial Q table and assign initial values ​​to each state-behavior pair, usually zero. Define the reward mechanism, which includes increasing the reward value for the learner's positive feedback on the pushed content and reducing the reward value for negative feedback. Set the learning rate parameter to 0.1 and the discount factor to 0.9. These two parameters control the speed of learning and the degree to which future rewards are considered. Through an iterative process, the Q value is updated according to the learner's feedback, and the strategy is adjusted by taking actions and observing the results. The behavior with the maximum Q value is selected as the next push strategy. The generated basic push strategy provides a quantitative decision-making basis for subsequent content push, ensuring that content push is more in line with the actual needs and preferences of learners.

[0110] In the push strategy learning submodule, based on the basic push strategy, the policy gradient method is used to continue to optimize the push strategy. The neural network model built by using TensorFlow and Keras libraries includes an input layer, multiple hidden layers and an output layer. The hidden layer uses the ReLU activation function to introduce nonlinearity, and the output layer uses the softmax function to generate the probability distribution of behavior. The learning rate is set to 0.01, and the learner feedback is used as the reward signal. The network parameters are adjusted through the back propagation algorithm to optimize the policy gradient. The input data includes learner characteristics and the current learning state, and the output data is the probability distribution of push behavior. The generated push strategy analysis results provide more refined and personalized decision support for content push, ensuring that the push content and method meet the needs of learners to the greatest extent.

[0111] In the push strategy implementation submodule, based on the push strategy analysis results, the Monte Carlo method is used to implement the optimized strategy. By using the SciPy library to calculate and sample the probability distribution, the optimized strategy network is used to formulate the push content and frequency according to the learner's current learning progress and preferences. This includes calculating the probability value corresponding to each push content, and selecting high-probability content as the push option based on the probability value. Analyze the learner's learning time preference to select the best push time. The generated optimized push strategy ensures that the push of learning content not only meets the learner's preferences, but also adapts to their learning progress, thereby improving learning efficiency and satisfaction.

[0112] Assume that a learner is learning piano and the Q learning algorithm is used to analyze the learner's learning behavior and feedback. Initially, the Q value of all piano course content push behaviors is zero. After several rounds of learning, it is found that the learner has more positive feedback on chord practice, so the Q value of the chord practice content push behavior is increased. In the push strategy learning submodule, the constructed neural network model optimizes the push strategy based on the learner's feedback. It is found that the learner spends more time studying on weekends, so the push frequency on weekends is increased. In the push strategy implementation submodule, based on the learning and feedback data, it is decided to push more chord practice content to the learner on weekends, and at the same time, the push of rhythm practice content is reduced according to the probability distribution. The adjustment and implementation of the strategy ultimately improves the learner's learning efficiency and satisfaction.

[0113] See also Figure 2 and Figure 7 ,The behavior analysis and prediction module includes a behavior pattern analysis submodule, a periodic change analysis submodule, and a behavior prediction submodule;

[0114] The behavior pattern analysis submodule is based on the optimization push strategy, adopts the long short-term memory network, uses the TensorFlow and Keras libraries to build a model, analyzes the learners' music learning interaction data, and uses the learners' interaction sequence data, including click-through rate, learning time and frequency, as input. The number of hidden layer nodes is set to 128, the relu activation function is used, and the output layer adopts the softmax function to identify and summarize the learners' behavior patterns, including learning preferences and active time periods. The batch size is set to 32 and the number of iterations is set to 100. The model is trained to generate behavior pattern analysis results.

[0115] The cyclical change analysis submodule uses the seasonal decomposition time series method based on the behavior pattern analysis results, and uses the statsmodels library to perform seasonal decomposition to analyze the cyclical changes in learner behavior. The time series data is decomposed into trend, seasonal and residual components. The learner's interaction time series data is input through the seasonal_decompose function, and the model is set as an additive model to reveal the cyclical laws of learner behavior and generate cyclical change analysis results.

[0116] The behavior prediction submodule adopts the long short-term memory network based on the periodic change analysis results, adjusts the long short-term memory network to the prediction mode, inputs the behavior pattern and periodic change data, sets the number of hidden layer nodes to 128, uses the sigmoid activation function to match the prediction task, and uses the linear function in the output layer to predict the learner's future behavior and needs and generate behavior prediction results.

[0117] In the behavior pattern analysis submodule, the music learning interaction data of learners is analyzed by building a long short-term memory network using TensorFlow and Keras libraries. The input data is formatted as sequence data of learner interaction, including click-through rate, learning time and frequency, and the data is converted into a time series format to adapt to the long short-term memory network. In the model structure, the number of hidden layer nodes is set to 128, the relu activation function is used to enhance the nonlinear fitting ability of the network, and the output layer uses the softmax function to summarize the learner's behavior pattern as a probability distribution. During the model training process, the batch size is set to 32 and the number of iterations is set to 100 to ensure that the model can fully learn the data features. The generated behavior pattern analysis results help identify and summarize learners' learning preferences and active time periods, providing a basis for subsequent personalized push and teaching content adjustment.

[0118] In the cyclical change analysis submodule, based on the results of behavioral pattern analysis, the seasonal decomposition time series method is used to analyze the cyclical changes in learner behavior. The seasonal_decompose function is executed using the statsmodels library to decompose the learner interaction time series data into trend, seasonal, and residual components. The input data is formatted as a time series, and the model is set as an additive model to conform to the characteristics of most teaching interaction data. The cyclical laws of learner behavior, such as weekly or daily learning patterns, are revealed. The generated cyclical change analysis results provide in-depth insights into learner behavior and help optimize the timing of learning content delivery.

[0119] In the behavior prediction submodule, based on the results of periodic change analysis, the long short-term memory network is adjusted to the prediction mode, and the input is the behavior pattern and periodic change data. The number of hidden layer nodes is set to 128 again, the activation function is changed to sigmoid to match the characteristics of the prediction task, and the output layer uses a linear function to predict the learner's future behavior and needs. This enables the long short-term memory network to predict future behavior based on past behavior patterns and periodic changes. The generated behavior prediction results provide the possibility for personalization of learning content and push strategies.

[0120] Suppose a learner's music learning interaction data shows that in the past two months, the learner is most active on Wednesday nights, with click-through rates and learning time exceeding other time periods. In the behavior pattern analysis submodule, the long short-term memory network model analyzes the learner's time series data, confirms the behavior pattern, and identifies the learner's preference for harmony learning content. In the periodic change analysis submodule, seasonal decomposition finds that the learner's learning activity does show a weekly repetitive pattern, especially on Wednesdays. In the behavior prediction submodule, the adjusted long short-term memory network predicts that the learner is likely to maintain similar learning behaviors and preferences in the coming weeks, especially a continued interest in harmony learning. Based on the analysis and prediction results, it is decided to push more harmony learning materials to the learner every Wednesday night to maximize the learner's learning efficiency and satisfaction.

[0121] See also Figure 2 and Figure 8 ,The teaching effect feedback module includes the teaching effect evaluation submodule, the teaching ,adjustment strategy formulation submodule, and the teaching effect improvement submodule;

[0122] The teaching effect evaluation submodule is based on the behavior prediction results, adopts the item response theory, and uses the ltm package of the R language to analyze and evaluate learners' feedback on the pushed content. By inputting learners' response data to differentiated pushed content, including accuracy and response time, the grm function is used to perform multi-parameter logical model analysis to evaluate the sensitivity of learning content to learners' ability levels and learners' mastery of target teaching content, and generate teaching effect evaluation results;

[0123] The teaching adjustment strategy formulation submodule uses the Markov decision process based on the teaching effect evaluation results and uses Python's pymdptoolbox library to optimize the strategy. By inputting the teaching effect evaluation information, including the learners' mastery of and preference for differentiated teaching content, the state space is set as the differentiated combination of teaching content, the action space is set as the estimated teaching adjustment measures, and the reward function is set according to the degree of improvement in teaching effect. It is iterated through the mdp.ValueIteration function to capture the optimal teaching adjustment strategy and generate a teaching adjustment strategy plan.

[0124] The teaching effect improvement submodule is based on the teaching adjustment strategy plan, adopts the feedback adjustment algorithm, and uses Python's SciPy library for mathematical optimization. By inputting the difference between the target teaching effect and the current teaching effect, using the minimize function, setting the goal to minimize the difference between the teaching effect and the target, adjusting the difficulty level of the teaching content, changing the teaching methods, and increasing personalized push content to match the needs of learners, it generates teaching effect improvement strategy information.

[0125] In the teaching effectiveness evaluation submodule, the feedback of learners on differentiated push content is analyzed in detail by adopting item response theory and using the ltm package of R language. The input data is the learners' response data to various push content, including the accuracy and response time of each content, etc. These data are formatted into a format suitable for IRT analysis. By executing the grm function in the ltm package, a multi-parameter logistic model analysis is performed to evaluate the sensitivity of different learning contents in revealing the learners' ability levels and the learners' mastery of specific teaching content. The generated teaching effectiveness evaluation results provide a scientific basis for subsequent teaching adjustments, revealing which teaching contents are effective and which need to be improved, so that teaching can better meet the actual needs and ability levels of learners.

[0126] In the teaching adjustment strategy formulation submodule, based on the teaching effect evaluation results, the Markov decision process is used and the Python pymdptoolbox library is used for strategy optimization. The input data is the teaching effect evaluation information, including the learners' mastery and preference for differentiated teaching content. The state space is set as the differentiated combination of teaching content, and the action space is the teaching adjustment measures. The reward function is defined according to the degree of improvement in teaching effect. By using the mdp.ValueIteration function, the strategy is iteratively optimized to capture the optimal teaching adjustment strategy. The generated teaching adjustment strategy makes the allocation and adjustment of teaching content more personalized and targeted, thereby improving the overall teaching efficiency and effect.

[0127] In the teaching effect improvement submodule, based on the teaching adjustment strategy scheme, a feedback adjustment algorithm is used and mathematical optimization is performed using Python's SciPy library. The input data includes the difference between the target teaching effect and the current teaching effect. The minimize function in the SciPy library is used to set the optimization goal to minimize the difference between the teaching effect and the target. The difficulty level of the teaching content is adjusted, the teaching method is changed, and personalized push content that matches the learner's needs is added. The generated teaching effect improvement strategy information provides a clear direction and strategy for the refinement and personalization of the teaching content, ensuring that teaching activities can more effectively meet the needs of learners while improving learners' learning outcomes.

[0128] Suppose that after a learner has studied a series of music theory courses, the item response theory analysis shows that the learner's understanding of harmony is significantly lower than the expected level. In the teaching effectiveness evaluation submodule, by analyzing the learner's response data to the harmony content, such as accuracy and response time, the specific aspects of the harmony teaching content that need to be improved are revealed. In the teaching adjustment strategy formulation submodule, adjustment strategies are proposed based on the evaluation results, such as strengthening the practical aspects of harmony and reducing the proportion of theoretical explanations. In the teaching effectiveness improvement submodule, improvement measures are implemented based on the teaching adjustment strategies, such as adjusting the harmony course structure, introducing more case analysis, and providing personalized exercises based on the learner's learning situation. The improved teaching strategy helps to improve learners' understanding and application ability in harmony.

[0129] See also Figure 2 and Fig. 9 ,The dynamic adjustment and optimization module includes the learning path adjustment submodule, the teaching content optimization submodule, and the teaching dynamic optimization submodule;

[0130] The learning path adjustment submodule is based on the teaching effect improvement strategy information, adopts a dynamic programming algorithm, and uses Python's NumPy library to analyze the learner's progress and effect feedback. By defining the state as the learner's current knowledge mastery level and learning progress, the action as the learning path adjustment option, and the objective function as maximizing learning efficiency and meeting the learner's personalized needs, the state value is updated through iterative calculation, the optimal learning path is captured, and the optimized learning path plan is generated;

[0131] The teaching content optimization submodule is based on the optimization learning path plan, adopts Bayesian network, and uses Python's pgmpy library to build a model to analyze learners' feedback and change needs on teaching content. By inputting learners' mastery of the current teaching content and satisfaction feedback, it calculates the conditional probability of differentiated teaching content adjustment plans, selects content adjustment plans that improve teaching effects, and generates teaching content optimization plans;

[0132] The teaching dynamic optimization submodule is based on the teaching content optimization plan and the learning path optimization plan, combined with teaching effect feedback, and adopts an iterative feedback adjustment algorithm. It uses Python's SciPy library for mathematical optimization. Through the optimize.minimize function, it sets the goal to minimize the difference between teaching content and learner needs. Through the feedback loop, it adjusts the teaching strategy to match the development of learners' abilities and changes in needs, and generates adjusted learning paths and teaching content.

[0133] In the learning path adjustment submodule, the learning path is optimized by adopting a dynamic programming algorithm and using Python's NumPy library to analyze the learner's progress and effect feedback. The input data is formatted as the learner's current knowledge mastery level and learning progress, converted into a state space, and the action space is defined as optional learning path adjustment options. The objective function aims to maximize learning efficiency and meet the learner's personalized needs. Through iterative calculations, the system updates the state value and captures the optimal learning path. The generated optimized learning path plan provides a learning roadmap that is dynamically adjusted according to learner feedback and progress, ensuring that the learning content and sequence are adapted to the learner's current needs and abilities to the greatest extent, thereby improving learning results.

[0134] In the teaching content optimization submodule, based on the optimization learning path plan, the Bayesian network method is used and the model is constructed using Python's pgmpy library to analyze learners' feedback and change needs on teaching content. The input data is the learners' mastery of the current teaching content and satisfaction feedback, and the data is used to calculate the conditional probability of different teaching content adjustment plans. By selecting content adjustment plans that can significantly improve teaching results. The generated teaching content optimization plan provides data support for the adjustment and optimization of teaching content, ensuring that the teaching content is more in line with learners' needs, thereby more effectively promoting learners' knowledge mastery and skill improvement.

[0135] In the teaching dynamic optimization submodule, based on the teaching content optimization plan and the optimized learning path plan, combined with the teaching effect feedback, the iterative feedback adjustment algorithm is used and the Python SciPy library is used for mathematical optimization. The input data includes the difference between the target teaching effect and the current teaching effect. Through the optimize.minimize function of the SciPy library, the goal is set to minimize the difference between the teaching content and the learner's needs. Through a continuous feedback loop, the teaching strategy is adjusted to match the development of learners' abilities and changes in needs. The generated adjusted learning path and teaching content provide learners with a more personalized and optimized learning experience, ensuring the continuity and adaptability of the teaching strategy, thereby achieving more efficient and effective learning outcomes.

[0136] Suppose a learner shows a lack of mastery of certain topics in a music theory course, such as harmony and rhythmic understanding. In the learning path adjustment submodule, the learner's current learning state and progress are converted into state space, and the dynamic programming algorithm proposes a plan to adjust the learning path based on the data, such as adding a learning unit of harmony and reducing the music theory knowledge that has been mastered. In the teaching content optimization submodule, based on the learner's mastery of the harmony content and satisfaction feedback, the Bayesian network model identifies the optimal content adjustment plan to improve teaching effectiveness, such as improving learners' understanding of harmony by adding more interactive exercises. In the teaching dynamic optimization submodule, based on the learner's progress and feedback, the teaching strategy is optimized through the iterative feedback adjustment algorithm to ensure that the teaching content is more in line with the learner's learning needs and abilities, thereby improving the learner's learning efficiency and satisfaction.

[0137] See also Figure 2 and Fig.10 ,The personalized recommendation module includes a recommendation strategy optimization submodule, a ,recommendation content adjustment submodule, and a recommendation timing optimization submodule;

[0138] The recommendation strategy optimization submodule is based on the adjusted learning path and teaching content. It adopts the deep Q network model, uses the TensorFlow and Keras libraries, builds a deep learning model, inputs the learner's behavior data and the characteristics of the current teaching content, outputs the value evaluation of the recommended action, and evaluates the impact of the recommended action on the learning effect by defining the reward function. The learning rate is set to 0.01 and the discount factor is set to 0.9. The experience replay mechanism is used to improve the learning efficiency. The recommendation strategy is optimized through iterative training of the model to generate the recommendation strategy optimization results.

[0139] The recommended content adjustment submodule uses the Monte Carlo tree search technology and Python's Monte Carlo tree search library to adjust the recommended content based on the recommendation strategy optimization results. It simulates the differentiated recommended content selection path by building a search tree, evaluates the long-term benefits of each choice, and selects the optimal recommended content path by simulating multiple decision-making processes, generating a recommended content adjustment plan.

[0140] The recommendation timing optimization submodule is based on the recommendation content adjustment plan, using the deep Q network model, combined with the learner's learning activity pattern and preference, using the TensorFlow and Keras libraries, and defining the reward function according to the learner's activity time data and preference information. It rewards the action of recommending at the moment when the learner is able to accept new information, sets the learning rate to 0.005 and the discount factor to 0.95, and uses the fit function for model training. It generates a personalized recommendation plan by identifying the effective time period of the learner's learning activities and the preferred time to receive new content.

[0141] In the recommendation strategy optimization submodule, the learner's behavior data and the characteristics of the current teaching content are analyzed by adopting a deep Q network model and building a deep learning model using TensorFlow and Keras libraries. The input data formatting includes learners' interactive behavior data and teaching content characteristics, such as click-through rate, speed of completing tasks, and accuracy. The impact of recommended actions on learning effects is evaluated by defining a reward function, setting the learning rate to 0.01 and the discount factor to 0.9, and using the experience replay mechanism to improve learning efficiency. In the iterative training model process, the Q value is continuously updated according to the learner's actual feedback to optimize the recommendation strategy. The generated recommendation strategy optimization results ensure that the recommended content can better meet the actual needs of learners and improve learning effects.

[0142] In the recommended content adjustment submodule, based on the recommendation strategy optimization results, the Monte Carlo tree search technology is used and the Python Monte Carlo tree search library is used to adjust the recommended content. By building a search tree, different recommended content selection paths are simulated and the long-term benefits of each option are evaluated. By simulating multiple decision-making processes, the optimal recommended content path can be identified. The generated recommended content adjustment plan ensures that the recommended teaching content not only meets the learner's current learning status, but also maximizes the long-term learning benefits.

[0143] In the recommendation timing optimization submodule, based on the recommended content adjustment plan, the deep Q network model is used again to optimize the learner's learning activity pattern and preference. The model is built using the TensorFlow and Keras libraries, and the input data is the learner's activity time data and preference information. By defining a reward function, the action of recommending at the moment when the learner is most likely to accept new information is rewarded. The learning rate is set to 0.005, the discount factor is set to 0.95, and the model is trained using the fit function. The generated personalized recommendation plan ensures that the recommended content not only meets the learner's needs in terms of content, but also that the recommendation timing is most suitable for the learner's personal learning habits and life rhythm.

[0144] Suppose a learner shows a high interest in improvisation courses on a music theory learning platform, but makes slow progress in harmony analysis. In the recommendation strategy optimization submodule, the deep Q network analyzes the learner's behavioral data and course interaction characteristics, identifies the key features of the improvisation course and the harmony analysis course, and adjusts the recommendation strategy to push harmony analysis-related content more frequently. In the recommendation content adjustment submodule, the Monte Carlo tree search technology evaluates different recommendation paths and selects a recommendation path that focuses on increasing harmony analysis exercises. In the recommendation timing optimization submodule, the deep Q network determines that the best time to recommend harmony analysis content is during the daily learning peak period based on the learner's learning habits and preferences. The comprehensive application of strategies ensures that learners can receive the right learning content at the right time to promote their learning efficiency and satisfaction.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A personalized music teaching content push system, characterized in that: The system includes a user ability assessment module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module, and a personalized recommendation module; The user ability assessment module uses a clustering algorithm to classify learners' skill levels based on the practice recordings, homework and test results submitted by learners, and uses a support vector machine algorithm and a random forest algorithm to perform data feature analysis and assessment on learners' music theory knowledge, performance skills and creative ability to generate ability assessment results; The teaching content matching module uses the K-means clustering algorithm based on the ability assessment results to quantitatively analyze the learner skill distribution, identify learner groups with differentiated skill levels, analyze teaching objectives, content difficulty and learner preferences through the hierarchical analysis method, formulate teaching content priorities corresponding to differentiated skill levels, and generate a teaching content matching plan; The learning path planning module uses the Dijkstra algorithm to plan the learning path based on the teaching content matching solution, and combines the learner's personal preferences and learning progress to design a multi-stage learning path through a dynamic programming method to generate personalized learning path information; The push strategy optimization module uses Q learning and policy gradient methods to adjust the push strategy based on personalized learning path information, learns and formulates the push strategy through reinforcement learning technology, and generates an optimized push strategy; The behavior analysis and prediction module is based on the optimization push strategy, combined with the learner's music learning interaction data, using the long short-term memory network and seasonal decomposition time series, to analyze the learner's behavior pattern and periodic changes, predict the learner's future behavior and needs, and generate behavior prediction results; The teaching effect feedback module evaluates the teaching effect based on the behavior prediction results and the learners' feedback on the pushed content through item response theory and Markov decision process, and formulates matching teaching adjustment strategies to generate teaching effect improvement strategy information; The dynamic adjustment and optimization module adjusts and optimizes the learning path and teaching content based on the teaching effect improvement strategy information, combined with the teaching effect feedback, using the dynamic programming algorithm and the Bayesian network, by analyzing the learner's progress, effect feedback and change needs, and generates the adjusted learning path and teaching content; The personalized recommendation module is based on the adjusted learning path and teaching content, uses a deep Q network model and Monte Carlo tree search technology to optimize the personalized recommendation strategy, analyzes the relationship between learner behavior, preferences and teaching content through a deep learning model, adjusts the push timing and content, and generates a personalized recommendation plan.

2. The personalized music teaching content push system according to claim 1, characterized in that: The ability assessment results include music theory knowledge scores, playing skill levels, and creative ability evaluations; the teaching content matching plan includes recommended music theory teaching materials, skill practice repertoires, and creative guidance courses; the personalized learning path information includes stage learning goals, recommended learning materials, and expected completion time; the optimization push strategy includes content push frequency, push time period selection, and content preference type; the behavior prediction results include active learning time periods, content preference analysis results, and potential learning obstacle information; the teaching effect improvement strategy information includes teaching content adjustment plans, learning method optimization plans, and auxiliary material update demand information; the adjusted learning path and teaching content include learning material list information, learning progress schedule, and personalized learning plans; the personalized recommendation plan includes recommended teaching video information, recommended music scores and works, and practice tasks.

3. The personalized music teaching content push system according to claim 1, characterized in that: The user capability assessment module includes a skill level classification submodule, a data feature analysis submodule, and a comprehensive assessment submodule; The skill level classification submodule adopts a clustering algorithm based on the practice recordings, homework and test results submitted by the learners, uses the scikit-learn library to perform K-Means clustering, sets the number of clusters, matches the learner ability level classification, uses the Euclidean distance for distance calculation, sets the number of iterations to 100, groups the learners, and generates skill level classification results; The data feature analysis submodule uses the support vector machine algorithm and the random forest algorithm based on the skill level classification results, and uses the Python Scikit-learn library to perform data feature analysis on the learner's music theory knowledge, playing skills and creative ability. By executing the SVC function and the RandomForestClassifier function, key features are extracted from the learner's practice recordings, homework and test results, and the factors that affect the learner's skill level are identified to generate data feature analysis results. The comprehensive assessment submodule is based on the data feature analysis results, adopts the weighted decision matrix method, and combines Python's NumPy library to conduct a comprehensive ability assessment analysis. According to the importance of each skill to the learner's overall ability, weights are assigned to the assessment dimensions of music theory knowledge, playing skills, and creative ability, and weight calculations are performed. The weighted decision matrix is ​​applied to multiply the learner's score on the differentiated skill dimension by the corresponding weight, and the comprehensive ability score of each learner is accumulated to generate the ability assessment result.

4. The personalized music teaching content push system according to claim 1, characterized in that: The teaching content matching module includes a skill quantification analysis submodule, a differentiation identification submodule, and a teaching content priority setting submodule; The skill quantitative analysis submodule uses the K-means clustering algorithm based on the ability assessment results and uses Python's Scikit-learn library to perform quantitative analysis of learners' skill distribution. By inputting learners' ability assessment data, including scores of music theory knowledge, playing skills, and creative ability, clustering is performed through the KMeans function to divide learners into differentiated groups according to their skill levels, and generate skill quantitative analysis results; The differentiation identification submodule uses statistical analysis methods based on the skill quantitative analysis results, uses Python's Pandas library and NumPy library to perform differentiated identification of skill levels among learner groups, uses the Pandas library to organize and group data, uses the NumPy library to perform mathematical operations, calculates the mean and standard deviation statistical indicators of differentiated group skill scores, identifies the skill differences among differentiated learner groups, and generates differentiated identification results; The teaching content priority setting submodule is based on the differentiated identification results, adopts the hierarchical analysis method, uses Python's PyAHP library, inputs the importance of teaching objectives, the difficulty level of teaching content and the weight of learners' preferences, and formulates the teaching content priorities for learners with differentiated skill levels by constructing a judgment matrix and calculating the weight vector, and generates a teaching content matching plan.

5. The personalized music teaching content push system according to claim 1 is characterized by: The learning path planning module includes a learning path design submodule, a learning preference integration submodule, and a multi-stage path planning submodule; The learning path design submodule is based on the teaching content matching scheme, adopts the Dijkstra algorithm, and uses the NetworkX library to build a graph model. The nodes represent the teaching units, and the edges represent the logical relationship between the teaching units. The weight of the edge is set by the add_weighted_edges_from function in the NetworkX library to reflect the transition difficulty between the teaching units, and the dijkstra_path function is used to calculate the shortest path of all nodes to generate the path basic framework; The learning preference integration submodule is based on the path basic framework, adopts the collaborative filtering algorithm, and uses the pairwise_distances function in the Scikit-learn library to calculate the similarity between the user preference vector and the content feature, adjusts the learning path according to the historical behavior data and the preferences of similar users, and captures the teaching content that matches the current learner's preferences through the NearestNeighbors model to generate a preference adjustment path; The multi-stage path planning submodule adjusts the path based on preferences, adopts a dynamic programming method, uses the NumPy library, defines multi-stage learning goals, allocates learning resources, dynamically adjusts progress parameters according to the learner's current progress and future goals, and calculates the multi-stage optimal learning path through a dynamic programming algorithm, including minimizing learning costs and maximizing learning effects, and generates personalized learning path information.

6. The personalized music teaching content push system according to claim 1, characterized in that: The push strategy optimization module includes a push strategy adjustment submodule, a push strategy learning submodule, and a push strategy implementation submodule; The push strategy adjustment submodule adopts the Q learning algorithm based on the personalized learning path information, uses the NumPy library, sets the initial Q table, assigns initial values ​​to the state-behavior pairs, and defines the reward mechanism, including increasing the reward value for the learner's positive feedback on the pushed content and reducing the reward value for negative feedback. The learning rate parameter is set to 0.1 and the discount factor is set to 0.

9. Through an iterative process, the Q value is updated according to the learner's feedback, and the behavior with the maximum Q value is selected as the push strategy to generate the basic push strategy; The push strategy learning submodule is based on the basic push strategy, adopts the policy gradient method, and uses the TensorFlow and Keras libraries to build a network model, including an input layer, a hidden layer, and an output layer. The hidden layer uses the ReLU activation function, and the output layer uses the softmax function to generate a probability distribution. The learning rate is set to 0.01, and the learner feedback is used as a reward signal. Through the back propagation algorithm, the network parameters are adjusted, the policy gradient is optimized, and the learner characteristics and the current learning state are input. The probability distribution of the push behavior is output to generate the push strategy analysis results; The push strategy implementation submodule uses the Monte Carlo method and the SciPy library based on the push strategy analysis results to calculate and sample the probability distribution. It analyzes the learner's current learning progress and preferences and uses the optimized strategy network to formulate the push content and frequency, including calculating the probability value corresponding to each push content, selecting the content with a high probability value as the push option, and analyzing the learner's learning time preference to select the push timing, thereby generating an optimized push strategy.

7. The personalized music teaching content push system according to claim 1, characterized in that: The behavior analysis and prediction module includes a behavior pattern analysis submodule, a periodic change analysis submodule, and a behavior prediction submodule; The behavior pattern analysis submodule is based on the optimization push strategy, adopts the long short-term memory network, uses the TensorFlow and Keras libraries to build a model, analyzes the learner's music learning interaction data, and uses the learner's interaction sequence data, including click-through rate, learning time and frequency, as input. The number of hidden layer nodes is set to 128, the relu activation function is used, and the output layer adopts the softmax function to identify and summarize the learner's behavior pattern, including learning preferences and active time periods. The batch size is set to 32 and the number of iterations is set to 100. Model training is performed to generate behavior pattern analysis results; The cyclical change analysis submodule adopts the seasonal decomposition time series method based on the behavior pattern analysis results, uses the statsmodels library to perform seasonal decomposition, analyzes the cyclical changes of learner behavior, decomposes the time series data into trend, seasonal and residual components, inputs the learner's interactive time series data through the seasonal_decompose function, sets the model as an additive model, reveals the cyclical law of learner behavior, and generates cyclical change analysis results; The behavior prediction submodule adopts the long short-term memory network based on the periodic change analysis results, adjusts the long short-term memory network to the prediction mode, inputs the behavior pattern and periodic change data, sets the number of hidden layer nodes to 128, uses the sigmoid activation function to match the prediction task, and uses the linear function in the output layer to predict the learner's future behavior and needs and generate behavior prediction results.

8. The personalized music teaching content push system according to claim 1, characterized in that: The teaching effect feedback module includes a teaching effect evaluation submodule, a teaching adjustment strategy formulation submodule, and a teaching effect improvement submodule; The teaching effect evaluation submodule is based on the behavior prediction results, adopts the item response theory, and uses the ltm package of the R language to analyze and evaluate the learners' feedback on the pushed content. By inputting the learners' response data to the differentiated pushed content, including the accuracy rate and response time, the grm function is used to perform a multi-parameter logical model analysis to evaluate the sensitivity of the learning content to the learners' ability level and the learners' mastery of the target teaching content, and generate the teaching effect evaluation results; The teaching adjustment strategy formulation submodule is based on the teaching effect evaluation results, adopts the Markov decision process, and uses Python's pymdptoolbox library to optimize the strategy. By inputting the teaching effect evaluation information, including the learners' mastery of and preference for differentiated teaching content, the state space is set as the differentiated combination of teaching content, the action space is set as the estimated teaching adjustment measures, and the reward function is set according to the degree of improvement of the teaching effect. It is iterated through the mdp.ValueIteration function to capture the optimal teaching adjustment strategy and generate a teaching adjustment strategy plan; The teaching effect improvement submodule is based on the teaching adjustment strategy scheme, adopts a feedback adjustment algorithm, and uses Python's SciPy library for mathematical optimization. By inputting the difference between the target teaching effect and the current teaching effect, using the minimize function, setting the goal to minimize the difference between the teaching effect and the target, adjusting the difficulty level of the teaching content, changing the teaching method, and increasing personalized push content to match the needs of learners, the teaching effect improvement strategy information is generated.

9. The personalized music teaching content push system according to claim 1, characterized in that: The dynamic adjustment and optimization module includes a learning path adjustment submodule, a teaching content optimization submodule, and a teaching dynamic optimization submodule; The learning path adjustment submodule adopts a dynamic programming algorithm based on the teaching effect improvement strategy information, uses Python's NumPy library, analyzes the learner's progress and effect feedback, defines the state as the learner's current knowledge mastery level and learning progress, and the action as the learning path adjustment option. The objective function is to maximize learning efficiency and meet the learner's personalized needs. Through iterative calculation, the state value is updated, the optimal learning path is captured, and an optimized learning path plan is generated; The teaching content optimization submodule is based on the optimization learning path scheme, adopts Bayesian network, uses Python's pgmpy library to build a model, analyzes learners' feedback and change needs on teaching content, calculates the conditional probability of differentiated teaching content adjustment schemes by inputting learners' mastery of current teaching content and satisfaction feedback, selects content adjustment schemes that improve teaching effects, and generates teaching content optimization schemes; The teaching dynamic optimization submodule is based on the teaching content optimization plan and the learning path optimization plan, combined with teaching effect feedback, adopts an iterative feedback adjustment algorithm, uses Python's SciPy library, and performs mathematical optimization. Through the optimize.minimize function, the goal is set to minimize the difference between teaching content and learner needs. Through the feedback loop, the teaching strategy is adjusted to match the development of learners' abilities and changes in needs, and the adjusted learning path and teaching content are generated.

10. The personalized music teaching content push system according to claim 1, characterized in that: The personalized recommendation module includes a recommendation strategy optimization submodule, a recommendation content adjustment submodule, and a recommendation timing optimization submodule; The recommendation strategy optimization submodule adopts a deep Q network model based on the adjusted learning path and teaching content, uses TensorFlow and Keras libraries to build a deep learning model, inputs learner behavior data and the characteristics of the current teaching content, outputs the value evaluation of the recommended action, and evaluates the impact of the recommended action on the learning effect by defining a reward function. The learning rate is set to 0.01 and the discount factor is set to 0.

9. The experience replay mechanism is used to improve the learning efficiency. The recommendation strategy is optimized by iterative training of the model to generate the recommendation strategy optimization result; The recommended content adjustment submodule uses the Monte Carlo tree search technology based on the recommendation strategy optimization results and the Monte Carlo tree search library of Python to adjust the recommended content. It simulates the differentiated recommended content selection path by building a search tree, evaluates the long-term benefits of each choice, and selects the optimal recommended content path by simulating multiple decision-making processes, and generates a recommended content adjustment plan; The recommendation timing optimization submodule is based on the recommendation content adjustment plan, uses the deep Q network model, combines the learner's learning activity pattern and preference, adopts the TensorFlow and Keras libraries, and defines the reward function according to the learner's activity time data and preference information. It rewards the action of recommending when the learner is able to accept new information. The learning rate is set to 0.005 and the discount factor is set to 0.

95. The model is trained using the fit function. By identifying the effective time period of the learner's learning activity and the preferred time to receive new content, a personalized recommendation plan is generated.

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