Intelligent adaptive educational training system based on big data

Abstract

The invention relates to the technical field of information, and discloses an intelligent adaptive education training system based on big data, and the system comprises a data collection and integration module which is used for collecting learning behavior data and physiological data of learners from a plurality of education platforms and physiological data sources; the dynamic student portrait generation module is used for automatically generating and updating a dynamic student portrait according to the learning behavior data and the physiological data collected by the data acquisition and integration module; the personalized feedback and learning path optimization module is used for generating personalized learning feedback according to the dynamic student portrait and automatically adjusting a learning path by utilizing a multi-objective optimization algorithm; the system implementation and optimization module is used for detecting operation of the data acquisition and integration module, the dynamic student portrait generation module and the personalized feedback and learning path optimization module for integration, testing and optimization; and the data acquisition and integration module comprises a multi-source data integration unit.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to an intelligent adaptive education and training system based on big data. Background Art

[0002] With the rapid development of information technology and the widespread application of big data, digital transformation in the education sector has gradually become possible. However, traditional education and training systems are often based solely on static data analysis, lacking real-time dynamic monitoring of learning behavior and physiological data. Therefore, how to achieve personalized education optimization for learners through efficient data collection and integration technologies has become an important issue in the current development of educational informatization. This invention was developed in this context, aiming to build an intelligent and adaptive education and training system to improve educational effectiveness and learning experience.

[0003] Current education and training systems typically rely on single platforms such as learning management systems and online course platforms for data collection, primarily focusing on typical learning behavior data such as learners' homework completion and test scores. However, existing technological solutions often lack monitoring of learners' emotional and physiological states, making it difficult for the system to comprehensively assess learners' learning experience and outcomes. While these systems achieve centralized data management to some extent, they still fall short in handling individual learners' differences and real-time needs.

[0004] While existing technologies have made some progress in data management, several shortcomings remain: First, the lack of real-time monitoring of learners' physiological states prevents the system from effectively identifying learners' emotional fluctuations and learning stress, thus affecting the accuracy of personalized feedback. Second, traditional learning path optimization methods are often static and cannot be dynamically adjusted according to changes in learners' real-time status and needs. This results in the system being unable to provide timely and targeted support when learners encounter difficulties or experience low moods. Therefore, based on these deficiencies in existing technologies, this invention introduces an innovative solution involving comprehensive data collection, dynamic student profile generation, and personalized path optimization to improve the intelligence level of education and training and the overall learner experience. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent adaptive education and training system based on big data, which solves the problems of discontinuous control trajectory, static and unadjustable emotion mapping, lack of user feedback loop, weak adaptive capability, and insufficient fusion of multi-dimensional control information in existing technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent adaptive education and training system based on big data, comprising:

[0007] The data acquisition and integration module is used to collect learners' learning behavior data and physiological data from multiple educational platforms and physiological data sources;

[0008] The dynamic student profile generation module automatically generates and updates dynamic student profiles based on the learning behavior data and physiological data collected by the data acquisition and integration module.

[0009] The personalized feedback and learning path optimization module generates personalized learning feedback based on dynamic student profiles and automatically adjusts the learning path using a multi-objective optimization algorithm.

[0010] The system implementation and optimization module is used to integrate, test, and optimize the operation of the data acquisition and integration module, the dynamic student profile generation module, and the personalized feedback and learning path optimization module.

[0011] This invention provides an intelligent adaptive education and training system based on big data. It has the following beneficial effects:

[0012] 1. First, this system achieves comprehensive monitoring of learners' learning behaviors and physiological states through real-time collection and integration of multi-source data. This comprehensiveness and real-time nature of the data provides a solid foundation for updating dynamic student profiles, enabling the system to maintain an accurate understanding of learners' conditions and effectively support the implementation of personalized education programs.

[0013] 2. Through the dynamic student profile generation module, this invention can deeply explore learners' personalized needs and potential interests. This module not only considers learners' learning progress and habits but also their emotional state to conduct a comprehensive profile analysis of learners, thereby ensuring that the feedback and suggestions provided are practical and that optimal learning strategies are developed for learners.

[0014] 3. The personalized feedback and learning path optimization module of this invention utilizes a multi-objective optimization algorithm to intelligently adjust learning content and strategies during the learning process. This flexibility allows learners' learning paths to not only adapt to their current ability level but also respond to changes in needs caused by changes in learners' emotions, thereby improving learning efficiency and engagement.

[0015] 4. This system emphasizes the real-time collection and application of user feedback. By monitoring feedback from learners and teachers during use, the system's functions are continuously optimized. Since user feedback directly influences the direction of system improvement, this invention is learner-centered, thereby enhancing the learning experience and effectiveness. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the algorithm flow of the present invention;

[0017] Figure 2This is a module architecture diagram of the dynamic student profile generation module of the present invention;

[0018] Figure 3 This is a module architecture diagram of the personalized feedback and learning path optimization module of this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see the appendix Figure 1-3 This invention provides an intelligent adaptive education and training system based on big data, comprising:

[0021] The data acquisition and integration module is used to collect learners' learning behavior data and physiological data from multiple educational platforms and physiological data sources;

[0022] In this embodiment, the data acquisition and integration module is a core component of the big data-based intelligent adaptive education and training system. Its main function is to collect learners' learning behavior data and physiological data from various educational platforms and physiological data sources. The design and implementation of this module ensures that the system can acquire comprehensive, accurate, and timely learner data, providing a reliable foundation for subsequent dynamic student profile generation and personalized feedback.

[0023] In this embodiment, the data acquisition and integration module includes multiple functional units to achieve efficient data collection and processing. The multi-source data integration unit connects to various educational platforms via an application programming interface (API) to acquire learners' learning behavior data in real time. Specifically, the data that can be acquired includes online course viewing time, chapter progress, assignment submission status, assessment results, and frequency of participation in online discussions. Through careful selection and integration of this data, the system can generate data in a unified format for subsequent processing and analysis by subsequent modules.

[0024] In this embodiment, while collecting learning behavior data, the physiological state data acquisition unit connects to smart wearable devices, such as smartwatches and heart rate monitors, to monitor the learner's physiological state in real time. This physiological data covers various information about the learner during the learning process, such as heart rate changes, emotional state, and concentration. In this way, the physiological state data acquisition unit can provide real-time feedback on the learner's physiological and psychological state, thereby gaining a comprehensive understanding of the learner's learning status.

[0025] In this embodiment, particular emphasis is placed on the close collaboration between the data acquisition and integration module and other modules of the system. Specifically, through data interfaces, seamless integration of learning behavior data and physiological data is ensured to form a comprehensive learner profile, thereby providing crucial support for the effective operation of the dynamic student profile generation module. With this data, the dynamic student profile generation module can more accurately reflect learners' mastery of different knowledge points and subjects.

[0026] In this embodiment, to further improve the accuracy and timeliness of the data, a series of data processing algorithms are employed to analyze the collected data in real time. For example, after acquiring learning behavior data, data analysis methods are applied to evaluate learners' learning behaviors to identify their learning patterns and habits within a specific time period. Simultaneously, the analysis of physiological state data helps identify learners' emotional changes and stress levels during the learning process, thus providing a strong basis for personalized feedback.

[0027] In this embodiment, the system must also have data security and privacy protection mechanisms during the collection and integration of learners' learning behavior and physiological data. These mechanisms ensure that all collected data complies with relevant laws and regulations during transmission, storage, and use, protecting learners' information security and privacy rights.

[0028] The data acquisition and integration module is not only the foundation for realizing an intelligent adaptive education and training system, but also a crucial driving force for data-driven personalized education. Through efficient and accurate data acquisition strategies and technologies, the system can comprehensively reflect learners' learning status and its dynamic changes, thus laying a solid data foundation for subsequent dynamic student profile generation, personalized feedback, and learning path optimization. It is hoped that in future implementation, through continuous improvement of this module, it can fully meet the ever-changing educational needs, thereby providing learners with more targeted and effective learning support.

[0029] In this embodiment, the optimization formula related to the personalized feedback and learning path optimization module is as follows:

[0030] Optimize:f(x)=α1×f1(x)+α2×f2(x)+α3×f3(x);

[0031] In the formula, f(x) is the overall optimization goal, representing the learner's overall performance and effectiveness in the personalized learning process; f1(x) is the learning efficiency, representing the learner's efficiency in knowledge acquisition and application; f2(x) is the content difficulty, representing the relative difficulty of the learning content received by the learner, to adapt to the learner's ability level; f3(x) is the feedback of the learner's emotional state, representing the emotional state experienced by the learner during the learning process and its impact on the learning effect; α1, α2, and α3 are the corresponding weighting coefficients, which are used to adjust the importance of learning efficiency, content difficulty, and learner's emotional state in the overall optimization goal, so as to better meet the needs of personalized learning.

[0032] By setting and dynamically adjusting these parameters appropriately, the system can implement precise learning path optimization and improve the learner's overall learning experience.

[0033] The dynamic student profile generation module automatically generates and updates dynamic student profiles based on the learning behavior data and physiological data collected by the data collection and integration module.

[0034] The dynamic student profile generation module is a crucial component of the intelligent adaptive education and training system. Its main task is to generate dynamic profiles of learners using learning behavior and physiological data acquired from the data acquisition and integration module. This module analyzes and processes the collected data through a series of algorithms to clearly describe the learner's learning status and individual characteristics, thereby supporting the development of personalized education strategies.

[0035] In this embodiment, the first step is to conduct an in-depth analysis of learners' behavioral data. This data includes information on learners' participation in online courses, completion of assignments, assessment scores, and interactive behaviors during the learning process. After precise algorithmic processing, this data generates a comprehensive assessment of learners' learning efficiency, knowledge mastery, and potential learning problems. This in-depth assessment not only reflects the learner's current learning performance but also provides a solid foundation for understanding their learning background.

[0036] The fusion and analysis of physiological state data is a crucial step in generating dynamic student profiles. Supported by smart wearable devices, the system can collect learners' heart rate, emotional state, and concentration levels in real time. This data effectively supplements the lack of learning behavior data, ensuring a comprehensive understanding of learners. During the quantitative analysis of physiological states, the system obtains detailed information on learners' emotional fluctuations and attention changes during the learning process. This integration of physiological data not only promotes a comprehensive understanding of learners' learning status but also provides a reliable basis for personalized education.

[0037] In this embodiment, to create dynamic student profiles, the system employs a machine learning-based algorithm and utilizes multivariate analysis technology to comprehensively process various types of learner data. Specifically, the formula set in this process is:

[0038] P(x)=w1·b1(x)+w2·b2(x)+w3·b3(x);

[0039] In the formula, P(x) represents the learner's dynamic profile, which includes a comprehensive evaluation of their learning status; b1(x) represents the learner's learning behavior characteristics, specifically including indicators such as learning duration and assignment submission rate; b2(x) represents the learner's physiological state characteristics, covering physiological information such as heart rate and mood; b3(x) represents the learner's interaction characteristics, such as the frequency of participation in discussions. These multi-dimensional features combined lay the foundation for forming a complete dynamic student profile; w1, w2, and w3 are the weight coefficients of the corresponding features. The settings of different coefficients can be dynamically adjusted according to the specific situation of the learner, aiming to optimize the accuracy of the learner profile.

[0040] Using the formula described above, the system can automatically provide feedback on learners' learning characteristics and potential obstacles during the dynamic student profile generation process, enabling personalized teaching strategies to be adjusted to their actual needs. The generated dynamic student profile also has the ability to be continuously updated, ensuring that learner assessments and feedback remain real-time and accurate.

[0041] In this embodiment, the dynamic student profile generation module integrates learning behavior and physiological state data, leveraging advanced data analysis and machine learning technologies to effectively understand learners' learning characteristics and needs. The effective operation of this module not only helps teachers better understand the individual differences of each learner but also provides learners with personalized learning suggestions and support, thereby further improving learning outcomes and the learning experience. Through continuous improvement of this function, the module can better address the growing personalized needs in the education process, achieving educational equity and the full allocation of high-quality resources. Through comprehensive dynamic profile generation, learners can engage in deeper learning along their own suitable learning paths, promoting improved academic performance.

[0042] The personalized feedback and learning path optimization module generates personalized learning feedback based on dynamic student profiles and automatically adjusts the learning path using a multi-objective optimization algorithm.

[0043] The personalized feedback and learning path optimization module is a core component of the intelligent adaptive education and training system. It aims to provide learners with precise, personalized learning feedback and suggestions based on the results generated from dynamic student profiles. This module comprehensively analyzes learners' learning behaviors, learning states, and physiological data to formulate practical learning path optimization plans, helping learners acquire knowledge more efficiently in a conducive learning environment.

[0044] In this embodiment, the personalized feedback and learning path optimization module first conducts an in-depth analysis of learner data from the dynamic student profile generation module. This data includes various information such as learner learning efficiency, learning motivation, knowledge mastery, and psychological state. Through a comprehensive analysis of this information, the system can identify learners' strengths and weaknesses, providing a foundation for the subsequent development of personalized learning plans.

[0045] In this embodiment, the core of the personalized feedback and learning path optimization module is to perform a weighted summation of various learning indicators of the learner by setting an optimization formula. The formula for this process is as follows:

[0046] O(x)=a1·E1(x)+a2·E2(x)+a3·E3(x);

[0047] In the formula, O(x) represents the overall optimization effect; E1(x), E2(x), and E3(x) represent different learning effect indicators, such as learning efficiency, knowledge comprehension, and learner satisfaction. These indicators are calculated comprehensively based on the learner's behavior and physiological state; a1, a2, and a3 are the weighting coefficients of the corresponding indicators, designed to flexibly adjust the impact of these indicators on the overall optimization effect according to the learner's specific situation.

[0048] In this embodiment, by weighting different learning effectiveness indicators, the personalized feedback and learning path optimization module can generate an integrated learning suggestion, including recommended learning content, learning time arrangements, and learning method selection. All these suggestions are based on adjustments made to the learner's real-time learning status and frequently changing dynamic profile.

[0049] In this embodiment, to ensure the effectiveness of the generated learning path optimization suggestions, the system can also incorporate machine learning algorithms to continuously optimize the weight coefficients through ongoing data feedback. This data-driven optimization approach allows for real-time adjustments to personalized learning plans at different learning stages, ensuring that the plans always adapt to the learner's actual needs.

[0050] In this embodiment, the timeliness and accuracy of the system in providing personalized feedback are emphasized. The personalized feedback and learning path optimization module can monitor the learner's learning process in real time and quickly adjust the feedback strategy when the learner's state changes. This mechanism ensures that learners receive appropriate learning suggestions at the right time, significantly improving their learning motivation and effectiveness.

[0051] In this embodiment, the effective implementation of the personalized feedback and learning path optimization module not only helps learners clarify their learning goals and directions but also provides teachers with important reference information for their teaching. This module, through scientific data analysis and personalized suggestion generation, promotes learners' better mastery of knowledge, improves learning efficiency and quality, and ultimately achieves the goal of personalized education. Through continuous feedback and optimization, this system can effectively address the diverse needs in the learning process, thereby promoting educational equity and improving the overall quality of education.

[0052] The system implementation and optimization module is used to integrate, test, and optimize the operation of the data collection and integration module, the dynamic student profile generation module, and the personalized feedback and learning path optimization module.

[0053] The learning outcome assessment and improvement suggestion generation module is an important component of the intelligent adaptive education and training system. Its main function is to evaluate learners' performance over a learning cycle and generate corresponding improvement suggestions to further optimize learners' learning paths and improve learning outcomes.

[0054] In this embodiment, the learning effectiveness evaluation and improvement suggestion generation module first further analyzes the data collected by the learner in the dynamic student profile generation module and the personalized feedback and learning path optimization module. This data includes not only the learner's academic performance and study time, but also feedback received, emotional state, and physiological indicators. Through quantitative analysis, this data reflects the learner's true learning status, providing a foundation for effective evaluation.

[0055] In this embodiment, during the evaluation process, the system applies a pre-defined evaluation formula to score the learning performance data comprehensively. This evaluation formula can be expressed as:

[0056] R(x)=b1·P1(x)+b2·P2(x)+b3·P3(x);

[0057] In the formula, R(x) represents the learner's overall assessment score; P1(x), P2(x), and P3(x) represent learning performance data in different dimensions, such as knowledge mastery, homework completion, and feedback acceptance. The comprehensive calculation of these learning performance indicators, through a weighted approach, reflects the learner's overall level, helping educators clearly identify the learner's strengths and weaknesses; b1, b2, and b3 are weighting coefficients, set according to the degree of influence of each dimension on the learner's overall performance to ensure the scientific validity and accuracy of the assessment results.

[0058] In this embodiment, after the evaluation is completed, the system will generate targeted improvement suggestions based on the evaluation results. These suggestions will combine the learner's dynamic profile with the learning data accumulated in the personalized feedback and learning path optimization module to propose specific improvement measures. Specifically, the improvement suggestions may include adjusting the learning plan, recommending suitable learning resources, and suggesting adjustments to learning strategies.

[0059] In this embodiment, when the system assessment finds that a learner has insufficient knowledge in a specific subject, the system may suggest that the learner increase the study time for the relevant courses or recommend targeted practice materials; if the learner frequently shows anxiety signals in the emotional state data, the system may suggest seeking psychological counseling or adjusting the learning methods to improve learning motivation.

[0060] This embodiment emphasizes the timeliness of feedback suggestions. The learning effectiveness evaluation and improvement suggestion generation module will dynamically evaluate based on continuously updated data, ensuring that improvement suggestions can quickly respond to changes in the learner's situation. This mechanism can provide appropriate guidance and support as soon as learners encounter difficulties, thereby effectively reducing learning obstacles and improving learning efficiency.

[0061] In this embodiment, the learning outcome evaluation and improvement suggestion generation module scientifically evaluates learners' multi-dimensional learning data and, combined with a personalized feedback mechanism, generates targeted improvement suggestions. This aims to help learners continuously optimize their learning strategies and improve learning outcomes. This module not only provides guidance for learners but also offers educators a scientific basis for their teaching decisions, further promoting personalized and precise education. Through continuous learning evaluation and feedback, the system can effectively meet the diverse needs of learners and support them in achieving more efficient learning outcomes.

[0062] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A big data-based intelligent adaptive education and training system, characterized in that: include: The data acquisition and integration module is used to collect learners' learning behavior data and physiological data from multiple educational platforms and physiological data sources; The dynamic student profile generation module automatically generates and updates dynamic student profiles based on the learning behavior data and physiological data collected by the data acquisition and integration module. The personalized feedback and learning path optimization module generates personalized learning feedback based on dynamic student profiles and automatically adjusts the learning path using a multi-objective optimization algorithm. The system implementation and optimization module is used to integrate, test, and optimize the operation of the data acquisition and integration module, the dynamic student profile generation module, and the personalized feedback and learning path optimization module.

2. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The data acquisition and integration module includes: The multi-source data integration unit uses API interfaces to acquire learning behavior data from multiple education platforms in real time, including data from online courses, assessment tools, and learning management systems. The physiological state data acquisition unit is used to monitor the learner's physiological state information in real time through wearable devices and integrate it into the learning behavior data to ensure comprehensive data collection.

3. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The dynamic student profile generation module includes: The knowledge graph construction unit builds a dynamic knowledge graph based on real-time acquired learning behavior and physiological data, which is used to map learners' mastery of different subjects or knowledge points and to achieve deeper personalized learning analysis. The dynamic update mechanism unit adjusts and updates student profiles in real time based on learners' learning behavior data and physiological data to reflect learners' latest status, interests, and ability changes.

4. The intelligent adaptive education and training system based on big data according to claim 3, characterized in that, The knowledge graph construction unit further utilizes emotion analysis technology to incorporate learners' emotional feedback during the learning process into the knowledge graph construction process, thereby more comprehensively reflecting learners' learning status and psychological characteristics. The dynamic update mechanism unit analyzes the learning progress contained in the student profile based on real-time collected data and uses algorithms to adjust learning strategies in a timely manner.

5. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The personalized feedback and learning path optimization module includes: The multi-dimensional feedback generation unit generates personalized learning feedback based on real-time monitoring of students' learning status and emotional changes, and recommends corresponding learning content or learning methods. The learning path optimization unit uses the following formula to determine the optimal learning path: Optimize:f(x)=α1×f1(x)+α2×f2(x)+α3×f3(x); In the formula, f(x) represents the overall optimization goal; Optimize is the optimization instruction; f1(x) represents the learning efficiency; f2(x) represents the content difficulty; f3(x) represents the feedback of the learner's emotional state; and α1, α2, and α3 are the corresponding weight coefficients.

6. The intelligent adaptive education and training system based on big data according to claim 5, characterized in that, The multi-dimensional feedback generation unit, when generating feedback, takes into account the individual differences in learners' learning styles and habits, and puts forward personalized suggestions to improve learning effectiveness. The learning path optimization unit can adjust the learning path of an individual learner in real time according to the formula, ensuring that the learning content matches the learner's state and ability.

7. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The system implementation and optimization module includes: The system performance monitoring unit is used to continuously monitor the operating status of the data acquisition and integration module, the dynamic student profile generation module, and the personalized feedback and learning path optimization module, record the data flow, and monitor the collaborative work between the modules. The user feedback collection unit is used to collect feedback from learners and teachers in real time during the use of the product.

8. The intelligent adaptive education and training system based on big data according to claim 7, characterized in that, The user feedback collection unit includes: The user satisfaction survey sub-unit obtains learners' and teachers' subjective evaluations of the system's user experience by regularly sending out questionnaires or surveys. Collect system operation logs and usage data sub-units to delve deeper into potential directions for system improvement through data analysis techniques.

9. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The modules interact with each other through data interfaces, enabling the data collection and integration module, the dynamic student profile generation module, the personalized feedback and learning path optimization module, and the system implementation and optimization module to share data in real time.

10. The intelligent adaptive education and training system based on big data according to claim 1, characterized in that, The system can continuously adjust its functions and optimization strategies based on learners' real-time feedback and data analysis results during the education and training process, in order to achieve a personalized learning experience and efficient teaching results.

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