A method and system for personalized adjustment of course learning in an empowerment system

Abstract

This invention relates to the field of personalized course adjustment, and more particularly to a method and system for personalized course learning adjustment within an empowerment system. Upon receiving a user's learning instruction, the system acquires the user's user information and historical learning data. Based on this information, it identifies the user's target course data. It also acquires the user's historical behavioral data, identifies the user's attention decay trend based on this data, and segments the target course data into multiple course segments. While the user is learning a course segment, the system acquires the user's behavioral data in real time. Based on this data, it determines the user's attention feedback state. When the user's attention feedback state is positive, it generates learning efficiency enhancement strategies and compound incentive strategies. When the user's attention feedback state is negative, it generates cognitive load adjustment strategies and stress balancing strategies. By judging the user's learning state based on behavioral data, the system can flexibly adjust learning strategies.

Description

Technical Field

[0001] This application relates to the field of personalized course adjustment technology, and in particular to a method and system for personalized course learning adjustment that enables systems. Background Technology

[0002] An empowerment system refers to a system that helps individuals or teams improve their capabilities by providing appropriate tools, resources, knowledge, or support. In course learning, it can help users improve learning outcomes through personalized learning resources and pathways. For example, online learning platforms and intelligent learning systems are types of empowerment systems that analyze students' learning behaviors to provide targeted learning content and strategies.

[0003] Existing technologies typically adjust course content based on user behavioral data (such as study time, task completion, and click count) and historical learning data (such as exam scores and knowledge mastery). These methods, based on user learning records and combined with pre-set algorithms, push personalized learning resources and make certain adjustments to learning strategies. However, this approach easily overlooks the dynamic changes in user attention. A user's attention span can vary significantly across different learning stages, especially when attention is low. Recommendations and adjustments may still be made based on static tags, resulting in poor personalized learning effectiveness. Secondly, existing personalized learning systems often rely on static tags to categorize user learning, such as by learning progress, ability level, gender, and age, to push course content. While this method can provide personalized learning paths to some extent, its adaptability is limited. Because these static tags cannot reflect changes in user attention across different learning stages, their adaptability to behavioral data from the same user at different learning stages is poor.

[0004] Therefore, existing technologies have shortcomings and need to be improved. Summary of the Invention

[0005] In order to solve one or more problems in the prior art, the main purpose of this application is to provide a method and system for personalized adjustment of course learning in an empowering system.

[0006] To achieve the aforementioned objectives, this application proposes a method for personalized adjustment of course learning in an empowerment system, the method comprising:

[0007] When a user's learning instruction is received, the user's user information and historical learning data are obtained, and the user's target course data is identified based on the user information and historical learning data.

[0008] The user's historical behavior data is obtained, and the user's attention decay trend is identified based on the historical behavior data. Based on the attention decay trend, the target course data is divided into multiple course segments.

[0009] When the user is learning a course segment, the user's behavior data is acquired in real time;

[0010] The user's attention feedback state is determined based on the behavioral data;

[0011] When the user's attention feedback state is positive, a learning efficiency enhancement strategy and a compound incentive strategy are generated.

[0012] When the user's attention feedback state is negative, a cognitive load adjustment strategy and a stress balance strategy are generated.

[0013] This application also provides a personalized course learning adjustment system for empowering systems, including:

[0014] The receiving module is used to obtain the user's user information and historical learning data when it receives the user's learning instruction, and to identify the user's target course data based on the user information and historical learning data.

[0015] The first acquisition module is used to acquire the user's historical behavior data, identify the user's attention decay trend based on the historical behavior data, and divide the target course data into multiple course segments based on the attention decay trend.

[0016] The second acquisition module is used to acquire the user's behavior data in real time when the user is learning a course segment;

[0017] The judgment module is used to judge the user's attention feedback state based on the behavioral data;

[0018] The first generation module is used to generate a learning efficiency enhancement strategy and a compound incentive strategy when the user's attention feedback state is positive.

[0019] The second generation module is used to generate a cognitive load adjustment strategy and a stress balance strategy when the user's attention feedback state is negative feedback.

[0020] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0021] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0022] The personalized learning adjustment method and system of the empowerment system in this application, through comprehensive analysis of user information, historical learning data, and real-time behavioral data during the learning process, can accurately identify the user's learning needs and attention fluctuation trends. By dividing the course content into multiple segments to adapt to fluctuations in the user's attention and judging the user's learning status based on real-time acquired behavioral data, the system can flexibly adjust learning strategies. Specifically, when the user's attention is good, the system enhances learning efficiency and incentive mechanisms; when attention declines, it adopts cognitive load adjustment and pressure balancing strategies to avoid fatigue and reduced efficiency caused by over-learning. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for personalized adjustment of course learning in an empowerment system according to an embodiment of this application.

[0024] Figure 2 This is a flowchart illustrating a method for personalized adjustment of course learning in an empowerment system according to an embodiment of this application.

[0025] Figure 3 This is a schematic block diagram of the structure of a personalized course learning adjustment system of an empowerment system according to an embodiment of this application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of this application.

[0027] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] Reference Figure 1 This application provides a method for personalized adjustment of course learning in an empowerment system, the method comprising:

[0030] S1. When a user's learning instruction is received, the user's user information and historical learning data are obtained, and the user's target course data is identified based on the user information and historical learning data.

[0031] S2. Obtain the user's historical behavior data, identify the user's attention decay trend based on the historical behavior data, and divide the target course data into multiple course segments based on the attention decay trend.

[0032] S3. When the user is learning a course segment, the user's behavior data is acquired in real time;

[0033] S4. Determine the user's attention feedback state based on the behavioral data;

[0034] S5. When the user's attention feedback state is positive, generate a learning efficiency enhancement strategy and a compound incentive strategy.

[0035] S6. When the user's attention feedback state is negative, a cognitive load adjustment strategy and a stress balance strategy are generated.

[0036] As described in steps S1-S3 above, by acquiring the user's basic information (such as age, subject preferences, learning style, etc.) and historical learning data (such as academic performance, learning time, learning progress, etc.), the system determines the user's current learning needs and goals. By analyzing this information, the system can identify and determine the most suitable target course data for the user's current situation. In this way, the system can provide users with personalized course recommendations, thereby improving the relevance and effectiveness of learning. Different users have different learning needs; precise target course selection can help users better achieve their learning goals. By analyzing the user's historical learning behavior data (such as learning duration, learning frequency, concentration, etc.), the system identifies the trend of changes in the user's concentration over time. For example, users may experience a decline in concentration after prolonged study. By monitoring this behavioral data, the system can effectively determine the trend of the user's concentration decline. Based on this, the course content is broken down into multiple short course segments to adapt to the user's learning concentration at different times, thereby optimizing the learning process and avoiding reduced efficiency due to excessively long study periods. By dividing the course into multiple segments, the system can maintain the user's learning concentration and avoid fatigue and reduced efficiency caused by prolonged continuous study. Short segments help boost user engagement and ensure optimal focus throughout the learning process. This feature works by tracking user learning behavior in real time (such as study duration, mouse clicks, video viewing time, and progress on practice questions), providing immediate feedback. This data reflects the user's learning status and level of concentration. Real-time acquisition of user behavior data allows the system to adjust its learning strategies based on the data generated during the learning process. This real-time monitoring enhances the flexibility and accuracy of personalized adjustments, enabling the system to optimize the user's learning style at the most appropriate time.

[0037] As described in steps S4-S7 above, the system determines the user's current focus state based on real-time collected behavioral data (such as user interaction frequency, input accuracy, page switching frequency, etc.). Focus feedback can be positive (indicating good focus) or negative (indicating potential distraction or fatigue). Through precise behavioral data analysis, the system can understand in real time whether the user is in a good learning state. If focus is low, the system can adjust learning strategies or recommend appropriate interventions to improve learning efficiency. When the user's focus is in a positive feedback state, it means the user is in a good learning state and can handle more challenges or tasks. At this time, the system further enhances the user's learning effect by generating learning efficiency reinforcement strategies (such as increasing learning difficulty, extending learning time, etc.) and compound incentive strategies (such as adding reward mechanisms, providing progress prompts, etc.). Reinforcement strategies in a positive feedback state can effectively improve the user's learning motivation and efficiency. For example, increasing task difficulty or providing advanced content can encourage users to further improve their learning level. Compound incentive strategies maintain the user's motivation in multiple ways, ensuring that the user's learning process is not easily interrupted or leads to fatigue. If a user's focus is in a negative feedback state, it indicates potential fatigue, distraction, or excessive cognitive load. In this case, the system analyzes the trend of focus decline and generates cognitive load adjustment strategies (such as reducing task difficulty and providing rest time) and stress balancing strategies (such as psychological support and reducing learning intensity) to help the user regain focus and reduce stress during the learning process. Through this adjustment under negative feedback conditions, the system can prevent users from experiencing a decline in learning effectiveness due to over-learning. Cognitive load adjustment and stress balancing strategies effectively reduce stress during the learning process, ensuring that users can maintain a highly efficient learning state after regaining focus.

[0038] As mentioned above, by comprehensively analyzing user information, historical learning data, and real-time behavioral data during the learning process, the system can accurately identify users' learning needs and trends in attention span. By dividing course content into multiple segments to adapt to fluctuations in user attention and judging the user's learning status based on real-time acquired behavioral data, the system can flexibly adjust learning strategies. Specifically, when users have good attention spans, the system enhances learning efficiency and incentive mechanisms; when attention spans decline, it adopts cognitive load adjustment and pressure balancing strategies to avoid fatigue and reduced efficiency caused by over-learning.

[0039] Reference Figure 2 In one embodiment, the steps of generating the learning efficiency enhancement strategy and the composite incentive strategy include:

[0040] S51. Based on the behavioral data, analyze the user's current attention decline trend and eye-tracking focus rate;

[0041] S52. Construct a prediction model, input the attention decay trend and eye movement focusing rate into the prediction model, and predict the duration of the current attention peak through the prediction model;

[0042] S53. Based on the predicted duration of the peak focus, infer the time compression ratio parameter;

[0043] S54. Based on the time compression ratio parameter, adjust the learning duration of the current course data;

[0044] S55. Based on the adjusted learning duration, determine the composite incentive strategy;

[0045] S56. Based on the target course data and historical learning data, construct a knowledge association model, input the current knowledge point into the knowledge association model, identify the corresponding higher-order knowledge nodes through the knowledge association model, and generate interdisciplinary related learning content.

[0046] S57. Determine learning efficiency enhancement strategies based on the interdisciplinary related learning content.

[0047] As described in the steps above, behavioral data includes user learning behaviors (such as clicks, dwell time, mouse clicks, etc.) and physiological data (such as eye movement data, EEG, etc.). This data can reveal fluctuations in user attention, especially eye focus rate (the percentage of time the eyes are focused on the course content), which reflects the user's level of focus on the current learning content. By analyzing this data, the system can determine the user's attention decline trend in real time and understand changes in their attention during the learning process. Attention decline trends are usually related to factors such as fatigue, learning difficulty, or information overload. For example, if a rapid decline in attention is detected, the system can implement adjustment strategies (such as adjusting the learning pace, content, or incentive strategies) to prevent user fatigue from affecting learning efficiency. The predictive model uses machine learning techniques, combining attention decline trends, eye focus rate, and other factors to build a dynamic model to predict the trajectory of changes in user attention. Training on extensive historical data, the model can identify patterns of attention fluctuation and predict the user's current peak attention span (i.e., the longest period the user can concentrate). It predicts the duration of this peak attention span, helping the system dynamically adjust the presentation time of learning content and avoid content overload or inappropriate learning arrangements before attention wanes. The core idea of ​​this predictive step is to estimate the user's optimal learning time based on the predicted peak attention span. The time compression ratio is an efficiency optimization parameter for completing learning content within the optimal attention span. The system can use this parameter to adjust the learning duration of course content. For example, if a user's attention is most concentrated within 30 minutes, the time compression ratio parameter can adapt the density and presentation of course content according to this duration, compressing unnecessary lengthy content and highlighting key knowledge points. This method can intelligently adjust the learning progress based on the user's attention characteristics, preventing user fatigue due to excessive learning time and ensuring maximum learning efficiency. By applying the time compression ratio, learning content can be completed within the user's optimal attention span, enhancing learning engagement and efficiency. Based on the previously calculated time compression ratio, the duration of the current learning module is adjusted. This means that if a user's attention span is short, the system can shorten the course time to focus on key content; if their attention span is long, the learning time can be extended or the course depth increased. By adjusting the time, the learning time can be matched with the user's attention span, preventing learning fatigue and information indigestion caused by learning too quickly, thereby improving learning effectiveness and experience. A composite incentive strategy refers to adjusting appropriate incentive methods based on multiple dimensions such as learning progress, attention span, and learning goals. This includes various means such as rewards, feedback, and interactivity, aiming to increase learners' motivation and engagement. If the system detects that a user has completed the expected learning task within a certain period, the system will provide timely rewards or positive feedback (such as points, progress bars, challenge prompts, etc.) based on the learning time and progress, enhancing the learner's sense of accomplishment.A composite incentive strategy enhances users' learning motivation while maintaining good focus. By adjusting incentive methods, it promotes proactive learning and increases motivation and engagement for continuous learning. The knowledge association model analyzes the inherent connections between knowledge points based on course data and historical learning records. This model identifies the relationship between current knowledge points and higher-level knowledge nodes and recommends interdisciplinary related learning content. For example, when learning mathematics, there may be some overlap with knowledge in fields such as physics and computer science. The system can analyze these relationships and suggest interdisciplinary learning paths. This model helps achieve the integration and learning of interdisciplinary knowledge, improving the coherence and depth of knowledge. Users can obtain a more comprehensive learning experience, enhancing their comprehensive abilities by connecting knowledge points from multiple disciplines, thereby improving learning outcomes. The learning efficiency enhancement strategy, based on the user's knowledge structure and interdisciplinary learning content, specifically adjusts learning methods, pace, and content presentation to improve learning efficiency. The system may adjust learning strategies based on the difficulty of interdisciplinary content, the user's current knowledge mastery, and historical learning data, selecting the most suitable enhancement methods, such as memory enhancement, problem-solving exercises, and interdisciplinary thinking training. This strategy can customize learning methods based on the user's specific situation, thereby effectively improving learning efficiency. By dynamically optimizing the learning strategy, it helps users improve their comprehension and application abilities in interdisciplinary learning, achieving more efficient learning goals.

[0048] In one embodiment, the step of generating cognitive load regulation strategies and stress balancing strategies includes:

[0049] Based on the behavioral data, analyze the user's current attention decline trend, and predict the user's cognitive load threshold based on the attention decline trend;

[0050] Based on the target course data and historical learning data, the knowledge points of the current learning course segment are broken down into the smallest logical units;

[0051] Based on the disassembly results, a pressure balancing strategy is determined;

[0052] When the cognitive load threshold is reached by the user, a multimodal guidance mechanism is activated to reduce the learning difficulty.

[0053] Based on the aforementioned multimodal guidance mechanism, a load adjustment strategy is determined.

[0054] As mentioned above, a user's attention span gradually diminishes over time during learning. This decline can be identified by analyzing behavioral data (such as eye movement data, mouse click frequency, learning duration, and pause times). Behavioral data reflects a user's learning activities, and combined with pattern recognition or machine learning algorithms, it's possible to predict the degree of attention span decline. The cognitive load threshold refers to the maximum cognitive load a user can tolerate. When attention span declines to a certain level, the user's cognitive load reaches the threshold, potentially leading to reduced learning effectiveness or fatigue. Therefore, predicting the cognitive load threshold based on the attention span decline trend can help the system adjust learning content or strategies in a timely manner, preventing users from overworking. Improving the user learning experience is crucial; by predicting the user's cognitive load threshold, the system can make adjustments in advance to prevent cognitive overload and ensure the learning process remains within its optimal efficiency range. Breaking down course content into the smallest logical units involves decomposing knowledge points from a macro-level overview into easily understood and memorable chunks, avoiding information overload. Historical learning data can provide feedback on a user's learning performance on certain knowledge points. This data helps identify which content is more difficult and which is easier for the user, allowing for personalized breakdown. The smallest logical unit refers to a relatively simple knowledge unit that can be understood independently, such as a concept, a formula, or a step in an operation. This breakdown makes the learning process more operational and modular, facilitating user comprehension. Based on the broken-down smallest logical units, the system can adjust its learning strategy by assessing the difficulty of each unit, the user's learning progress, and cognitive load, forming a "pressure balancing strategy." If a knowledge point is too complex or exceeds the user's current cognitive load threshold, the system can suggest segmented learning, increase demonstrations, or provide tutoring to reduce pressure. The pressure balancing strategy may involve adjusting the learning content, such as simplifying information, providing supplementary materials, or adjusting the learning pace. The goal is to reduce user stress during the learning process and prevent them from developing negative emotions or giving up due to excessive stress. It ensures that the learning content matches the user's cognitive load, avoiding overly complex content that could cause stress and reduce learning efficiency or experience. The pressure balancing strategy helps users learn in their optimal learning state. When the system detects through behavioral data that the user's cognitive load is approaching or has reached its threshold, it activates a multimodal guidance mechanism. Multimodal guidance mechanisms incorporate multi-sensory input, including visual, auditory, and tactile senses. This approach provides users with more intuitive guidance, such as through illustrations, animations, or voice prompts, reducing their learning burden. Multimodal feedback allows the system to offer more vivid and engaging assistance, enhancing the learning experience while reducing information complexity in a single-mode learning environment. It lowers the user's cognitive load, making the learning process easier and more intuitive. Multimodal guidance effectively reduces cognitive stress, improves learning acceptability, and enhances learning outcomes.After enabling the multimodal guidance mechanism, the system adjusts its workload regulation strategy in real time based on the current learning progress, user response, and cognitive load level. This may include adjusting the complexity of the learning content, the learning pace, or providing rest prompts or changes to the guided course design when necessary. The workload regulation strategy needs to monitor the user's response in real time and dynamically adjust the learning methods based on changes in learning effectiveness and workload level. For example, if a certain knowledge point causes the user too much anxiety or fatigue, the system may postpone learning that knowledge point and instead review previous content or provide more supplementary information.

[0055] In one embodiment, the step of determining the user's attention feedback state based on the behavioral data includes:

[0056] Based on the behavioral data, analyze the user's current eye-tracking focus rate and page scrolling frequency;

[0057] Obtain the attention decay trend from historical behavioral data, and determine the attention feedback threshold based on the attention decay trend;

[0058] Calculate the user's current focus index based on the eye-tracking focus rate and page scrolling frequency;

[0059] When the focus index is continuously greater than the focus feedback threshold, it is determined to be a positive feedback state;

[0060] When the focus index is consistently less than or equal to the focus feedback threshold, it is determined to be a negative feedback state.

[0061] As mentioned above, eye-tracking technology can detect a user's eye focus and analyze their level of attention to different areas on the page. Eye focus rate (EMR) represents the ratio of the time a user's eyes are focused on learning content to the total time spent on that content. If a user's eyes linger on a particular section for an extended period, it indicates high concentration on that section. Page scrolling frequency refers to the speed and frequency at which a user scrolls the page during learning. Frequent scrolling may mean the user is quickly browsing or skipping uninteresting content, while less scrolling may indicate in-depth reading or reflection. By analyzing EMR and page scrolling frequency, a user's level of concentration can be identified. If a user's eyes frequently move or scroll across the page, it may indicate a lack of focus; if the eyes are more focused and scrolling is less frequent, it usually means the user is focused on the current learning content. Analysis of EMR and page scrolling frequency allows for real-time monitoring of the user's concentration state, helping the system dynamically determine whether the user is concentrating and adjust the learning content accordingly. This analysis provides a reliable basis for subsequent feedback decisions. Attention decay trends in historical behavioral data: By analyzing a user's historical learning behavior, the system can identify trends in attention decay. As study time increases or the complexity of the content rises, users' concentration typically weakens. Historical data can provide a trend analysis model to predict the decline in user concentration during the current learning process. Based on the trends analyzed from historical behavioral data, the system can determine a concentration feedback threshold, which represents the critical point of user concentration. When a user's concentration falls below this threshold, the system considers their attention to be unfocused and requires feedback or adjustment of learning strategies. By determining the concentration feedback threshold, the system can judge in real time whether the user's concentration is within an acceptable range. The threshold setting is based on the individual user's historical behavioral data, ensuring that the judgment of concentration status is personalized and accurate, and helping to provide timely feedback when concentration is insufficient. The concentration index is an indicator calculated based on a combination of eye-tracking focus rate and page scrolling frequency. Generally, a higher eye-tracking focus rate and a lower page scrolling frequency indicate that the user is concentrating, resulting in a higher concentration index. Conversely, a lower eye-tracking focus rate and frequent page scrolling indicate that the user is distracted, resulting in a lower concentration index. The calculation of the concentration index can combine various algorithms, such as weighted averages and machine learning models, to comprehensively assess the user's current concentration state. This index dynamically changes based on user behavior data, reflecting their level of focus. The calculation of the focus index provides a quantitative basis for subsequent feedback judgments, enabling the system to more accurately identify the user's focus state. Through the dynamic focus index, the system can adjust learning strategies in real time to maintain the user's optimal learning state. When the focus index consistently exceeds the feedback threshold, it indicates that the user is in a focused and efficient learning state.In this situation, the system doesn't need to intervene much and can maintain the current learning strategy or continue advancing the learning content. A positive feedback state means the user is learning with an ideal level of focus. When the system determines a positive feedback state, it may continue to provide appropriate learning content and maintain the current learning pace, avoiding unnecessary interruptions or adjustments. By determining a positive feedback state, the system can confirm that the user's learning progress and focus are ideal, thus eliminating the need to adjust the learning content or strategy and ensuring that the user's learning efficiency is not disturbed. When the focus index is below or equal to the focus feedback threshold, it indicates that the user's focus is insufficient and they may not be able to effectively absorb the current learning content. At this time, the system will determine a negative feedback state, prompting the user that their current learning method may have a problem and needs adjustment. In a negative feedback state, the system can activate adjustment mechanisms, such as reducing the difficulty of the learning content, providing more visual or auditory feedback, guiding the user to rest, or switching learning content, to help the user restore focus and improve learning efficiency.

[0062] In one embodiment, the method further includes:

[0063] The system acquires the user's behavioral data in real time and identifies the learning stage corresponding to the behavioral data.

[0064] Based on the learning stage and the behavioral data, predict the user's future attention feedback state;

[0065] When the predicted result is that the future attention feedback state is negative, an advance adjustment strategy is generated based on the judgment result. This strategy is used to generate the cognitive load adjustment strategy and the stress balance strategy in advance before the negative feedback state occurs.

[0066] As mentioned above, real-time user behavior data is acquired to monitor behavioral indicators such as learning status, attention changes, and emotional fluctuations. This data can come from various sensors, monitoring systems, or user interactions (such as mouse clicks, keyboard input, eye trackers, etc.). Real-time acquisition of this data allows for dynamic strategy adjustments during the learning process, rather than relying on static or post-hoc analysis. Real-time capture of user changes ensures timely responses. This makes subsequent feedback mechanisms and adjustment strategies more precise, maximizing user learning efficiency. The user's learning process is divided into different stages, such as the initial learning stage, mastery stage, and review stage, each with different cognitive loads, attention levels, and behavioral patterns. By identifying patterns in behavioral data, it's possible to determine which learning stage the user is in. This identification can be performed using machine learning algorithms, pattern recognition technology, etc. Appropriate strategies are then adjusted based on the user's learning stage; for example, the initial learning stage may require more guidance and support, while the review stage may focus on automated feedback and knowledge reinforcement. This feature helps personalize learning paths and improve learning efficiency. During the learning process, a user's attention often fluctuates over time, and this fluctuation may be related to various factors, such as task difficulty, fatigue, and environmental influences. By combining learning stage and behavioral data, predictive algorithms (such as time series forecasting, regression analysis, or machine learning methods) can be used to predict future attention states. This process is based on the analysis of users' past behavioral data to infer their attention feedback at a future point in time. It can predict users' attention states in advance, especially negative feedback states. This provides a basis for early intervention, preventing users from falling into a state of inefficient learning or excessive fatigue. Negative feedback states typically refer to situations such as decreased user attention, low learning efficiency, or negative emotions. By predicting attention states, if the system detects a potential decline or instability in future attention, it can react promptly. Signs of negative feedback may include slow user response, increased error rates, and prolonged interaction time. Timely detection of declining attention trends prevents users from entering an inefficient state and reduces the decrease in learning efficiency caused by lack of focus. This feature can effectively avoid problems such as excessive cognitive load or emotional breakdown. After predicting the user's future negative attention states, the system automatically generates adjustment strategies to ensure that measures are taken before problems occur. These strategies may include cognitive load adjustment, task difficulty adjustment, rest time scheduling, and environmental optimization. This process proactively addresses user needs and behavioral patterns by analyzing them in advance, rather than passively waiting for problems to arise. Cognitive load and stress levels are closely related to the learning process. When cognitive load is too high, learning efficiency decreases, and users' concentration is easily distracted. By monitoring users' cognitive load and stress levels in real time, adjustments can be made before negative feedback occurs, ensuring users remain in an optimal learning state.For example, appropriate rest, task allocation, and adjustments can effectively reduce the cognitive burden on users.

[0067] In one embodiment, after the step of generating an advance adjustment strategy based on the judgment result, the method further includes:

[0068] Real-time monitoring of user behavior data and emotional state after strategy execution;

[0069] Based on the behavioral data and emotional state, determine whether the advance adjustment strategy meets the user's expectations;

[0070] When the strategy fails to achieve the expected results, a backup strategy is triggered.

[0071] As mentioned above, real-time monitoring of user behavior data and emotional state is crucial to ensuring the effectiveness of the generated adjustment strategies. By monitoring behavioral data (such as the frequency, duration, and task completion of user actions) and emotional states (such as mood swings, heart rate, facial expressions, and tone of voice), the system can accurately obtain immediate user feedback. This process is similar to a "closed-loop feedback" mechanism, used to observe the effects of strategy implementation. Strategy adjustments during the learning process rely not only on static data but also on the user's immediate reactions. User behavior and emotional state directly reflect their acceptance and effectiveness of the strategy. If the strategy fails to effectively improve the user's learning state after implementation, real-time monitoring helps the system detect this and adjust or replace the strategy. Through real-time monitoring, the system can promptly detect changes in user behavior after strategy implementation and further verify whether the user feels happy or experiences reduced stress through emotional state feedback. This helps to dynamically adjust strategies during the learning process to improve learning effectiveness and avoid user frustration or inefficient learning due to poor strategy performance. This part evaluates the effectiveness of the strategy by comprehensively analyzing user behavior data and emotional states. Behavioral data may include task completion status, error rate, and task completion time, while emotional state may be detected through physiological sensors or facial recognition technology. The system will use this data to assess the user's focus, emotional stability, and other factors, thereby evaluating whether the pre-adjusted strategy has achieved the expected goals, such as improving focus or reducing anxiety. The "expected" goals in the learning process are not only about improving knowledge acquisition but also include improving emotional state, reducing stress, and avoiding fatigue. Monitoring behavioral data and emotional state allows the system to evaluate not only the cognitive effectiveness of the strategy but also its adaptability and comfort at the emotional level. This comprehensive evaluation ensures that users are not only efficient in their learning but also maintain a pleasant mood and healthy cognitive load. If, after strategy execution, monitoring data shows that the user's behavior or emotional state has not achieved the expected results, the system will trigger alternative strategies based on the assessment results. Alternative strategies may include different learning methods, adjusting the difficulty of the task, providing more support or feedback, scheduling rest periods, or changing the learning style. The design of alternative strategies needs to be adjusted based on the user's individual needs and data feedback. For example, if a user shows fatigue or anxiety at a certain stage of learning, alternative strategies may include short breaks, emotionally relaxing activities, or adjusting the difficulty of the learning content. In personalized learning, not every adjustment strategy can suit every user. Especially when faced with fluctuating emotional states and complex behavioral patterns, a single strategy may not fully meet the user's needs. Therefore, introducing backup strategies can prevent users from falling into inefficient learning or negative emotions due to ineffective strategies, thereby improving the adaptability and fault tolerance of the entire learning process.

[0072] In one embodiment, the triggering alternative strategy includes converting text content into visual charts and providing a progressive chain of prompts. Transforming plain text information into more intuitive charts or visualizations. For example, data tables can be converted into bar charts, line charts, pie charts, etc., or conceptual descriptions can be converted into mind maps, flowcharts, etc. Visual charts help users understand abstract information more intuitively and improve information acceptability and memorability. Plain text may be difficult for some users to digest, especially when dealing with complex data or concepts, where textual information can easily cause confusion. Converting information into charts or visual elements helps users quickly grasp key information and reduce cognitive load. This approach is particularly effective for visual learners (i.e., learners who prefer to acquire information through visual perception). Through this strategy, learning content becomes more actionable, effectively improving user comprehension and learning motivation. A progressive chain of prompts refers to providing prompts gradually according to the user's needs and learning progress, rather than displaying all information at once. Such a strategy helps users better understand learning content through gradual guidance while avoiding information overload. Hint chains can gradually reveal the methods, steps, or knowledge points for solving problems in stages, encouraging users to explore and solve problems independently step by step.

[0073] Reference Figure 3 This application also provides a personalized course learning adjustment system for empowering systems, including:

[0074] The receiving module 1 is used to obtain the user's user information and historical learning data when it receives the user's learning instruction, and to identify the user's target course data based on the user information and historical learning data.

[0075] The first acquisition module 2 is used to acquire the user's historical behavior data, identify the user's attention decay trend based on the historical behavior data, and divide the target course data into multiple course segments based on the attention decay trend.

[0076] The second acquisition module 3 is used to acquire the user's behavior data in real time when the user is learning a course segment;

[0077] Module 4 is used to determine the user's attention feedback state based on the behavioral data;

[0078] The first generation module 5 is used to generate a learning efficiency enhancement strategy and a compound incentive strategy when the user's attention feedback state is positive.

[0079] The second generation module 6 is used to generate a cognitive load adjustment strategy and a stress balance strategy when the user's attention feedback state is negative feedback.

[0080] As described above, it is understood that each component of the course learning personalization adjustment system of the empowerment system proposed in this application can realize the function of any of the course learning personalization adjustment methods of the empowerment system as described above, and the specific structure will not be repeated.

[0081] Reference Figure 4 This application also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data such as monitoring data. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a personalized adjustment method for curriculum learning within an empowering system.

[0082] The processor described above executes the personalized course learning adjustment method of the empowerment system, including: when receiving a user's learning instruction, acquiring the user's user information and historical learning data; identifying the user's target course data based on the user information and historical learning data; acquiring the user's historical behavior data; identifying the user's attention decay trend based on the historical behavior data; segmenting the target course data into multiple course segments based on the attention decay trend; acquiring the user's behavior data in real time when the user is learning a course segment; determining the user's attention feedback state based on the behavior data; generating a learning efficiency enhancement strategy and a compound incentive strategy when the user's attention feedback state is positive; and generating a cognitive load adjustment strategy and a stress balancing strategy when the user's attention feedback state is negative.

[0083] One embodiment of this application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for personalized adjustment of course learning in an empowerment system, comprising the steps of: when receiving a user's learning instruction, acquiring the user's user information and historical learning data; identifying the user's target course data based on the user information and historical learning data; acquiring the user's historical behavior data; identifying the user's attention decay trend based on the historical behavior data; segmenting the target course data into multiple course segments based on the attention decay trend; acquiring the user's behavior data in real time while the user is learning a course segment; determining the user's attention feedback state based on the behavior data; generating a learning efficiency enhancement strategy and a compound incentive strategy when the user's attention feedback state is positive; and generating a cognitive load adjustment strategy and a stress balancing strategy when the user's attention feedback state is negative.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media provided in this application and in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0086] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for personalized adjustment of course learning in an empowerment system, characterized in that, The method includes: When a user's learning instruction is received, the user's user information and historical learning data are obtained, and the user's target course data is identified based on the user information and historical learning data. The user's historical behavior data is obtained, and the user's attention decay trend is identified based on the historical behavior data. Based on the attention decay trend, the target course data is divided into multiple course segments. When the user is learning a course segment, the user's behavior data is acquired in real time; The user's attention feedback state is determined based on the behavioral data; When the user's attention feedback state is positive, a learning efficiency enhancement strategy and a compound incentive strategy are generated. When the user's attention feedback state is negative, a cognitive load adjustment strategy and a stress balance strategy are generated. The steps for generating the learning efficiency enhancement strategy and the composite incentive strategy include: analyzing the user's current attention decay trend and eye-tracking focus rate based on the behavioral data; constructing a prediction model, inputting the attention decay trend and eye-tracking focus rate into the prediction model, and predicting the current peak attention duration using the prediction model; inferring a time compression ratio parameter based on the predicted peak attention duration; adjusting the learning duration of the current course data based on the time compression ratio parameter; determining the composite incentive strategy based on the adjusted learning duration; constructing a knowledge association model based on the target course data and historical learning data, inputting the current knowledge point into the knowledge association model, identifying corresponding higher-order knowledge nodes through the knowledge association model, and generating interdisciplinary related learning content; and determining the learning efficiency enhancement strategy based on the interdisciplinary related learning content. The steps for generating cognitive load adjustment strategies and stress balancing strategies include: analyzing the user's current attention decay trend based on the behavioral data, and predicting the user's cognitive load threshold based on the attention decay trend; breaking down the knowledge points of the current learning course segment into the smallest logical units based on the target course data and historical learning data; determining a stress balancing strategy based on the decomposition results; activating a multimodal guidance mechanism to reduce learning difficulty when the user's cognitive level reaches the cognitive load threshold; and determining a cognitive load adjustment strategy based on the multimodal guidance mechanism.

2. The method for personalized adjustment of course learning in the empowerment system according to claim 1, characterized in that, The step of determining the user's attention feedback state based on the behavioral data includes: Based on the behavioral data, analyze the user's current eye-tracking focus rate and page scrolling frequency; Obtain the attention decay trend from historical behavioral data, and determine the attention feedback threshold based on the attention decay trend; Calculate the user's current focus index based on the eye-tracking focus rate and page scrolling frequency; When the focus index is continuously greater than the focus feedback threshold, it is determined to be a positive feedback state; When the focus index is consistently less than or equal to the focus feedback threshold, it is determined to be a negative feedback state.

3. The method for personalized adjustment of course learning in the empowerment system according to claim 1, characterized in that, The method further includes: The system acquires the user's behavioral data in real time and identifies the learning stage corresponding to the behavioral data. Based on the learning stage and the behavioral data, predict the user's future attention feedback state; When the predicted result is that the future attention feedback state is negative, an advance adjustment strategy is generated based on the judgment result. This strategy is used to generate the cognitive load adjustment strategy and the stress balance strategy in advance before the negative feedback state occurs.

4. The method for personalized adjustment of course learning in the empowerment system according to claim 3, characterized in that, After the step of generating an advance adjustment strategy based on the judgment result, the method further includes: Real-time monitoring of user behavior data and emotional state after strategy execution; Based on the behavioral data and emotional state, determine whether the advance adjustment strategy meets the user's expectations; When the strategy fails to achieve the expected results, a backup strategy is triggered.

5. The method for personalized adjustment of course learning in the empowerment system according to claim 4, characterized in that, The triggering alternative strategies include: converting text content into visual charts and providing a progressive chain of prompts.

6. A personalized course learning adjustment system for an empowerment system, used in the method described in any one of claims 1-5, characterized in that, include: The receiving module is used to obtain the user's user information and historical learning data when it receives the user's learning instruction, and to identify the user's target course data based on the user information and historical learning data. The first acquisition module is used to acquire the user's historical behavior data, identify the user's attention decay trend based on the historical behavior data, and divide the target course data into multiple course segments based on the attention decay trend. The second acquisition module is used to acquire the user's behavior data in real time when the user is learning a course segment; The judgment module is used to judge the user's attention feedback state based on the behavioral data; The first generation module is used to generate a learning efficiency enhancement strategy and a compound incentive strategy when the user's attention feedback state is positive. The second generation module is used to generate a cognitive load adjustment strategy and a stress balance strategy when the user's attention feedback state is negative feedback.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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