717 results about "Learning behavior" patented technology

Provided is a system of generating multi-style learning tutorials based on learning preference evaluation. The system dynamically explores preferences of learners in a learning process from a time dimension through a proposed deep reinforcement learning model, so that a deep reinforcement learning agent can accurately express learning behavior features of learners and realize the personalized and accurate construction of course tutorials. At the same time, using an internal knowledge base and an external knowledge base as resource support, a multi-modal knowledge map with a plurality of definition relationships is designed to overcome the limitations of construction of a course tutorial method.

The invention relates to the technical field of online network teaching, and discloses an online network teaching practice method based on a virtual technology, and the method comprises the steps: obtaining virtual scene data, and carrying out the integration and building of a dynamic knowledge graph and a multi-modal teaching resource library; learning behavior features are extracted according to the cognitive features of the learner, knowledge difficulty levels are divided, and immersive learning is guided; constructing a cognitive evaluation model, optimizing a learning path through hierarchical reinforcement learning, and generating a self-adaptive teaching instruction; collecting interaction track data to generate an operation thermodynamic diagram, and evaluating the skill mastering degree; complexity parameters of the virtual scene are dynamically adjusted, a virtual-real linkage experiment simulation environment is constructed, and skill training closed-loop feedback is achieved. The invention further relates to specific implementation modes of resource integration, cognitive feature extraction, learning path optimization and the like. According to the method, the personalized and immersive experience and skill training effect of online teaching are improved, and the method is suitable for the field of online network teaching.

The invention discloses a multi-agent collaborative reasoning system for intelligent teaching intervention, and relates to the technical field of artificial intelligence and education, and the system comprises a data collection and convergence module which collects data of learning behaviors, emotional states, academic scores and knowledge point mastering conditions of students by means of a classroom behavior analysis system, a camera, a microphone and a learning management system; according to the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the precision and individuation of teaching intervention are realized by constructing the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the system collects multi-dimensional data of students through the data acquisition and convergence module, and through the processing of the data conversion and cleaning module, the accuracy and availability of the data are ensured; the teaching strategy planning module generates a dynamic decision model by means of a large language model, a customized intervention strategy can be generated according to the learning condition and problem root of students, and the multi-agent collaborative reasoning module further improves the intelligent level of the system.

The invention discloses a self-adaptive intelligent teaching content recommendation system, and relates to the technical field of intelligent teaching, and the system comprises a data collection and analysis module which is used for collecting multiple types of data of students in the learning process, including learning behavior data, physiological signals, environment data and learning achievement cognition feedback data of the students, and after collection, sending the data to a database; performing preprocessing of noise reduction, standardization and analysis feature extraction on the multi-type data to generate multi-modal data; according to the method, the three-dimensional knowledge graph of knowledge points, error types and thinking paths is constructed, and the TCN analysis is combined, so that explicit knowledge defects can be identified, implicit knowledge vulnerabilities can be diagnosed, students can accurately know knowledge system vulnerabilities of themselves, and targeted defect checking, leak repairing and intensified training are carried out; learning interest information is extracted from multi-modal data, an explicit and implicit multi-dimensional interest model is established, and teaching content is screened in combination with a knowledge short board diagnosis result.

The invention provides an intelligent learning behavior monitoring and abnormity early warning method and device, and relates to the technical field of education analysis, and the device architecture comprises a multi-modal data collection module, a behavior analysis module, a dynamic abnormity grading early warning module and a processing measure module. The multi-modal data acquisition unit integrates learning platform operation records, facial expression data and physiological data as well as environment and situation information collected by the situation awareness data acquisition unit, so that omnibearing and multi-dimensional data acquisition is realized; the long-time monitoring and personalized model construction unit uses a plurality of data mining algorithms and LSTM in deep learning to combine with an attention mechanism, accurately mines student behavior rules according to long-term data and constructs a personalized model, and can flexibly and accurately judge abnormal behaviors according to student individual conditions and learning situations. Graded early warning is provided for related personnel in time, deterioration of learning problems is effectively prevented, and timeliness and effectiveness of education intervention are improved.

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention relates to the technical field of computers, in particular to a personalized learning recommendation method and device based on AI workflow and electronic equipment, after a user authorizes, learning behavior data of the user on a teaching platform is collected in real time, based on the collected data, the current learning state and ability level of the user are evaluated, the learning requirement is determined, and according to the learning requirement, the learning state and ability level of the user are recommended. According to the method, the difficulty encountered when a user learns course content is predicted, a course set suitable for current learning of the user is screened out from a preset course library according to a prediction result and learning requirements, and a personalized course recommendation list is generated in combination with interest preference and real-time learning behavior data of the user. Meanwhile, by collecting and analyzing feedback suggestions of the user on the recommended course and adjusting the recommendation strategy in real time according to the feedback, immediate feedback and recommendation adjustment on new questions or demands of the user are achieved.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention relates to the technical field of artificial intelligence, and discloses an AI intelligent auxiliary question answering system for student practice. According to the system, various interactive data such as texts, voices and handwritten formula images of students are collected through a multi-modal data acquisition module, semantic feature vectors of all modals are generated through a multi-dimensional feature analysis module, and then the semantic feature vectors are mapped to a unified semantic space through a cross-modal fusion module. The double-layer graph construction module constructs a domain knowledge graph and a learning behavior graph, and the dynamic reasoning and recommendation module generates a personalized problem solving path recommendation and knowledge point completion strategy based on fusion semantic representation and a graph library. In addition, the system also has the functions of interactive behavior log anomaly detection, path backtracking, knowledge forgetting curve prediction and the like. The system can comprehensively understand questions of students, provides personalized question answering service, and effectively improves the learning efficiency and knowledge mastering degree of the students.

The invention discloses an online resource adaptive recommendation method based on multi-modal learning behavior analysis, and relates to the technical field of resource recommendation. The method comprises the following steps: firstly, dynamically collecting multi-modal data by using a heterogeneous sensor array, and carrying out noise reduction, probability distribution matching normalization and time-space alignment preprocessing; features are extracted through a hierarchical network, modeling learning behaviors such as a variational auto-encoder are combined, and the learning state is evaluated from multiple dimensions; recommendation decisions are generated based on reinforcement learning, recommendation is optimized in combination with personalized presentation and multi-source feedback analysis, meanwhile, the system has the functions of dynamic strategy adjustment, intelligent resource creation, cross-scene migration recommendation and the like, and accurate self-adaptive recommendation is achieved. According to the method, multi-modal data are comprehensively collected and deeply processed, learning behaviors and evaluation states are accurately analyzed, personalized resource recommendation is provided through intelligent recommendation and dynamic optimization strategies, recommendation accuracy and learning effects can be improved, user experience can be enhanced, and the utilization rate and competitiveness of platform resources can be improved.

The invention relates to the technical field of online education, in particular to a componentized page rendering method and device for online education, equipment and a medium, and the method comprises the steps: obtaining a page rendering request, and determining component configuration information; constructing a component dependency graph; calculating component loading priority scores, and generating a loading queue; loading the execution component and managing the life cycle; establishing an inter-component communication bus; and executing a rendering process and performing performance optimization. According to the method, the user learning behavior and the component dependency relationship are intelligently analyzed, the component loading and rendering strategy is dynamically adjusted, and a dual-priority event management mechanism is introduced, so that efficient intelligent rendering of the education scene component is realized. Therefore, the problems of low online education component rendering efficiency, poor scene adaptability, insufficient learning behavior analysis, lack of performance optimization automation and the like in the prior art are solved, and the performance of an online education platform and the user experience are remarkably improved.

The invention discloses an interactive processing method and device based on course management, and relates to the technical field of intelligent education, and the method comprises the steps: collecting student information, carrying out the preprocessing of the student information, and generating an initial learning path according to the preprocessed student information; in the process that the student learns according to the initial learning path, learning behavior data of the student is monitored, and a personalized learning path is generated according to the learning behavior data of the student; monitoring emotion change data, cognitive load data and attention level data of the trainee in the personalized learning path in real time, constructing a learning state evaluation model, and evaluating the current learning state of the trainee; and dynamically generating an immersive learning situation according to the current learning state of the student. According to the invention, the pertinence and utilization efficiency of educational resources are improved, and the enthusiasm and effect of learning are enhanced.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention relates to a multi-mode tumble detection method, device and equipment, and belongs to the technical field of intelligent nursing. The method comprises the steps of obtaining fall detection data; performing motion point extraction on the radar point cloud data based on time sequence analysis, screening out motion points whose positions change in continuous time, and generating motion point cloud data; projecting the moving point cloud data to a coordinate system corresponding to the thermal infrared imaging data to generate a radar point cloud projection matrix; combining the radar point cloud projection matrix with the thermal infrared imaging data, calculating the three-dimensional coordinates of the target, and generating target height change data; and when the target height change data meets a preset tumble condition, inputting the constructed time sequence multi-modal fusion data into a deep learning behavior recognition module, and outputting a classification result of tumble behaviors. According to the method, the radar sensor and the thermal infrared imaging data are fused, and time sequence analysis and deep learning behavior recognition are combined, so that the privacy of the user is protected while high-precision fall detection is realized.

The invention discloses a personalized homework generation method based on an artificial intelligence algorithm. The personalized homework generation method comprises the following steps: S1, collecting multi-dimensional learning behavior data of a learner; s2, constructing a knowledge point mastering model of the learner based on the knowledge graph; s3, generating a behavior feature vector set of the learner; s4, constructing a cognitive load model of the learner according to the behavior feature vector set and the knowledge point mastering model in combination with the knowledge point relevance; s5, generating and dynamically adjusting a personalized job; s6, adjusting relevance weights of recommendation contents and knowledge points of the personalized homework; s7, updating the generation strategy of the personalized operation in real time; s8, enabling the learner to complete proper exercise content at a proper time; and S9, providing a personalized homework generation scheme for the learner according to the updated behavior feature vector set, the knowledge point mastering model and the cognitive load model. According to the method and the device, the homework generation and pushing dynamically adaptive to the requirements of the learner are realized, and the accuracy and the adaptability of personalized homework generation are improved.

The invention relates to the technical field of behavior portrait construction, and discloses a learner behavior portrait construction system and method based on data analysis, and the system comprises a learning behavior collection module, a learning rhythm mutation detection module, a behavior track stability evaluation module, a knowledge point backtracking index calculation module, and a portrait dynamic adjustment module. According to the method, the learning behavior fluctuation rate is calculated, the knowledge point jumping frequency and the task completion interval fluctuation condition are combined, the sudden change characteristics of the learning behaviors are judged, the recognition capacity for sudden learning behavior changes is enhanced, the behavior path overlapping degree of different time periods is compared, and the page switching frequency and the dwell time change range are calculated; the method comprises the steps of analyzing the stability of learning behaviors, performing hierarchical analysis on a knowledge point access mode of a learner according to a backtracking condition of a learning path, and optimizing the sensitivity of a portrait to learning path adjustment and enhancing the portrait adaptability by dynamically adjusting a portrait updating strategy and self-adapting to real-time behavior changes of the learner.

The invention discloses a learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

The invention is suitable for the technical field of education management, and provides a college student education evaluation management method and system based on deep learning, and the method comprises the steps: obtaining college student information which comprises basic information and learning behavior data, and obtaining a course arrangement of a school which comprises course content and a teaching plan; the learning progress of the college students is obtained, the learning attitudes of the students are obtained, the learning attitudes comprise emotional tendencies, and a classification model is adopted for multi-dimensional comprehensive evaluation; acquiring evaluation results, classifying the evaluation results, acquiring teaching intervention strategies corresponding to classification, and performing differentiated management according to teaching intervention; according to the method, learning progress improvement schemes under different management conditions are obtained, personalized learning resource recommendation and course rhythm adjustment are performed according to the schemes, and the problems that teaching schemes cannot be adjusted in time according to the learning progress of college students and teaching strategies cannot be changed according to local conditions according to the learning conditions of different students are solved.

The invention discloses an intelligent teaching assistance system and method based on behavior data and a knowledge graph, and relates to the technical field of teaching assistance, and the method comprises the steps: evaluating the mastering degree of a student for different learned knowledge points, and obtaining knowledge mastering data; obtaining defect knowledge points from the knowledge mastering data, obtaining a knowledge graph of the student, and evaluating the learning influence degree of the knowledge points in the knowledge graph on learning of the defect knowledge points by the student; acquiring historical learning behavior records and historical knowledge mastering data of other students, and analyzing learning reference values of the other students to the student in combination with the historical learning behavior records and defect learning data of the student to obtain a target reference student; and acquiring historical learning data of the target reference student, generating teaching reference data of the student in combination with the defect learning data of the student, and pushing the teaching reference data to a course teacher of the student to assist the course teacher in teaching the student.

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

The invention discloses a teaching management method and system based on electronic technology courses, and relates to the technical field of teaching management, and the method comprises the steps: collecting and preprocessing multi-source data, constructing an interactive network diagram of students in courses, and carrying out the classification of student groups; constructing a knowledge graph in combination with the course data and the learning behavior data, and initializing a personalized learning path based on the knowledge graph and student group features; and learning resources of different paths are recommended through a multi-view attention mechanism. The multi-level learning path constructed through the knowledge graph ensures that the students grasp knowledge step by step, can adapt to individual differences of the students, performs resource recommendation by using a multi-view attention mechanism, and can realize high-matching-degree resource pushing on the basis of capturing individual features of the students, thereby effectively improving learning efficiency and resource utilization rate, and improving learning efficiency. The dynamic optimization of the learning path is realized, and the adaptability and pertinence of teaching management are remarkably improved.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

The invention discloses a fine-grained knowledge point mastering analysis method based on local graph convolution, and the method comprises the steps: S1, collecting the learning behavior data of students, and constructing an initial knowledge graph based on the learning behavior data; s2, establishing a local graph convolution model based on the initial knowledge graph; s3, updating and generating a knowledge graph with personalized features; s4, based on the personalized knowledge graph, generating a knowledge point mastering condition table by calculating a mastering degree index of each knowledge point; s5, performing clustering analysis on the knowledge points of which the mastering degree indexes are lower than a preset threshold value in the knowledge point mastering condition table; s6, generating knowledge blind spot group feature vectors; and S7, generating a learning strategy for each knowledge blind spot group according to the feature vectors of the knowledge blind spot groups. According to the invention, refined evaluation and real-time dynamic tracking of knowledge point mastering are realized.

The invention provides a course reconstruction method and system based on a knowledge graph, and relates to the technical field of intelligent education. The method comprises the following steps: collecting course resource data and student learning behavior data; constructing a knowledge graph fusing multiple courses; generating a personalized learning path based on the atlas and the behavior data; arranging experiment training tasks according to a path and collecting feedback data to update a graph state; and converting the student training result into a technical manuscript in a structured manner and accessing the technical manuscript to a management platform. Closed-loop optimization of teaching resource organization, path recommendation and achievement management is realized.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention relates to the technical field of education informatization, in particular to a teaching informatization management system which comprises a data acquisition module, a data processing module, an intelligent analysis module, a decision support module and a resource scheduling module. Wherein the data acquisition module is used for acquiring student learning behavior data, teaching content data and teacher teaching data in real time; the data processing module is used for preprocessing various data; the intelligent analysis module is used for identifying the learning law of students, the teaching effect of teachers and the use condition of teaching resources and generating a teaching analysis report; the decision support module is used for generating teaching decision suggestions by utilizing an optimization algorithm; and the resource scheduling module is used for scheduling and distributing teaching resources in real time. According to the invention, through intelligent data acquisition, analysis and optimization scheduling, teaching resources can be accurately allocated in real time, the resource utilization rate and the teaching quality are improved, and education informatization management is promoted to develop towards an efficient and intelligent direction.

The invention provides a full-cycle intelligent education evaluation system based on a knowledge graph, and relates to the technical field of data processing, and the system is used for carrying out the correlation analysis of a learning behavior data set and a knowledge point data set of a student, calculating the mastering degree of the student on each knowledge point, recognizing the relation between the student and each knowledge point, and carrying out the calculation of the mastering degree. The method comprises the following steps: acquiring knowledge points of students, mapping the knowledge points into a graph structure to obtain a staged knowledge graph, updating the staged knowledge graph, adjusting the relationship between the students and each knowledge point to obtain a dynamic knowledge graph, extracting the real-time mastery degree of each knowledge point according to the dynamic knowledge graph, and periodically analyzing the knowledge points in combination with time dimensions. And obtaining periodic evaluation data, analyzing the mastering degree, the progress trend and the weak knowledge points of the students at each knowledge point, and generating an individual evaluation data set. According to the invention, the knowledge graph can be constructed to periodically evaluate the learning condition of the student.

The invention relates to the technical field of education informatization, in particular to an online learning behavior recognition method and system based on multi-view deep behavior recognition. According to the method, front and side RGB video data and skeleton data during online learning of a student are acquired, after preprocessing, feature vectors of the video and the skeleton data are extracted by using a deep learning technology, feature alignment of the video and the skeleton data is performed by using an inter-modal cyclic generative adversarial network, and a total fusion feature vector is obtained; and inputting the fusion feature vector into a multi-layer perceptron for classification, and finally outputting a learning behavior classification result of the student. According to the method, data of different view angles and modals can be effectively fused, and the accuracy and reliability of online learning behavior recognition are improved. Through combination of multi-view and multi-modal information, not only can behavior states of students be comprehensively captured, but also accurate data support can be provided for personalized learning recommendation, behavior prediction and the like.

The invention discloses a touch and talk pen interactive content generation method based on adaptive learning. The method comprises the following steps: S1, acquiring a learning behavior data set of a user; s2, based on the learning behavior data set, constructing a dynamic knowledge graph of the user through an adaptive learning algorithm; s3, analyzing the learning behavior data set by using a query committee algorithm; s4, triggering a content generation module according to a bifurcation calculation result, wherein the content generation module generates learning content adaptive to the learning demand of the user based on the dynamic knowledge graph and the learning behavior data set of the user; s5, performing multi-mode interactive presentation of the learning content through the touch and talk pen; s6, performing feedback based on a learning behavior data set of the user for the learning content; s7, outputting a learning feedback report, wherein the learning feedback report is analyzed and generated based on the learning behavior data set of the user; and S8, repeating the steps S1 to S7. The interactive experience of the user is remarkably improved, the touch and talk pen is converted into an intelligent learning assistant from a passive content presentation tool, and efficient learning support and feedback are provided for the user.