Teaching management system with interaction function

Abstract

The invention relates to the technical field of education, in particular to a teaching management system with an interaction function. Comprising an information acquisition module, an information analysis module, an assessment analysis module, an answering module and a level analysis module. The information acquisition module, the information analysis module, the assessment analysis module, the answering module and the level analysis module are connected in sequence; the information acquisition module is used for acquiring the learning content, the hot content and the basic information of the learner at the current moment; the information analysis module is used for analyzing the learning content and determining knowledge veins of the learning content; the assessment analysis module is used for analyzing the hotspot content, the basic information and the knowledge venation and determining assessment content; the answering module is used for displaying the examination content for a learner to answer; the level analysis module is used for receiving the answering result of the learner; and analyzing the answering result, and determining the learning level of the learner.

Description

Technical Field

[0001] The present application relates to the field of educational technology, and in particular to a teaching management system with interactive functions. Background Art

[0002] With the development of the information age, how to improve education and management levels through advanced technical means has become a key issue in enterprise construction and management innovation.

[0003] Education was originally intended to utilize the fragmented learning of learners, but due to the fragmented learning method, it is impossible to establish an effective knowledge context.

[0004] Current teaching management systems often lack dynamic updates and interactivity, especially in analyzing learners' learning situations. They still have certain limitations, which leads to the inability to fully mobilize learners' enthusiasm and fail to effectively deal with cognitive deficiencies and personalized needs that arise in the learning process. Summary of the invention

[0005] The present application provides a teaching management system with interactive functions to solve the above problems.

[0006] In a first aspect, the present application provides a teaching management system with interactive functions, the system comprising an information acquisition module, an information analysis module, an assessment analysis module, an answering module, and a level analysis module;

[0007] The information acquisition module, the information analysis module, the assessment analysis module, the answering module, and the level analysis module are connected in sequence;

[0008] The information acquisition module is used to obtain the current learning content, hot content and basic information of learners;

[0009] The information analysis module is used to analyze the learning content and determine the knowledge context of the learning content;

[0010] The assessment analysis module is used to analyze the hot content, the basic information and the knowledge context to determine the assessment content;

[0011] The answering module is used to display the assessment content for the learner to answer;

[0012] The level analysis module is used to receive the answer results of the learner; analyze the answer results, and determine the learning level of the learner.

[0013] Through this solution, the latest learning content and hot information can be obtained in real time to ensure the timeliness and relevance of learning materials. Collect the basic information of learners to provide data support for subsequent personalized learning planning and assessment. Integrate data from different sources to provide a comprehensive data foundation for the information analysis module. Analyze the learning content and build the knowledge context to help learners understand the connection between knowledge points. Analyze the personal information and behavior data of learners to build user portraits to provide a basis for personalized recommendations. Identify the current hot content and provide a basis for hot participation analysis for the assessment analysis module. Customize personalized assessment content based on hot content, basic information of learners and knowledge context. Evaluate learners' knowledge mastery and hot participation by analyzing the assessment results. Diagnose learners' cognitive deficiencies to provide a basis for subsequent learning adjustments. Provide simulation scenarios and assessment interfaces to enhance learners' sense of participation and interactivity. Real-time feedback during the answering process helps learners understand their learning status and errors in a timely manner. Enhance learners' learning motivation and interest through interaction and feedback. Collect and count learners' answer results to provide quantitative data for learning effect evaluation. Analyze the answer results and evaluate the learners' actual learning level and progress. Provide learners with personalized learning content and recommendations based on the learning level evaluation results.

[0014] Optionally, when the information analysis module analyzes the learning content and determines the knowledge context of the learning content, it is used to:

[0015] Analyze the learning content to determine what has been learned and what is being learned;

[0016] Determine the categories involved based on the content learned;

[0017] Determine content cross-category based on the learned content and the current learning content;

[0018] The knowledge context of the learning content is determined based on the involved categories and the content cross-categories.

[0019] Through this solution, by analyzing the learning content, it is possible to distinguish between the knowledge that learners have already mastered (learned content) and the knowledge that they are currently learning (currently learning content), thereby providing a data basis for personalized learning path planning. By identifying keywords and topics in the learned content and classifying them into corresponding categories, learners can be helped to establish a knowledge framework and provide a reference for subsequent learning. By comparing the learned content and the current learning content, the intersection between the two can be found and the content cross-category can be determined, which is crucial to deepening learners' understanding and application of knowledge. The constructed knowledge context diagram can clearly show the relationship between knowledge points, help learners understand the structure and logic of the knowledge system, and improve the systematicness and depth of learning.

[0020] Optionally, the teaching management system further includes a learning planning module, which is connected to the information acquisition module and the information analysis module and is used to:

[0021] Analyze the basic information and determine the responsible section of the learner;

[0022] Analyze the knowledge context and the hot topics to determine the current level of understanding of the hot topics;

[0023] Generate new learning content based on the responsible section and the level of understanding of the current hot spots.

[0024] Through this solution, by analyzing the basic information of learners, it is possible to accurately identify the responsibilities and work priorities of learners, thereby recommending learning content closely related to their work and improving the practicality and pertinence of learning. By analyzing the knowledge context and hot content, it is possible to evaluate the learners' understanding of current hot topics, provide a basis for formulating personalized learning plans, and ensure that the learning content is in sync with the pulse of the times. According to the learners' responsible sections and understanding of hot topics, new learning content is automatically generated to achieve dynamic updating and personalized customization of learning, meeting the differentiated needs of different learners.

[0025] Optionally, the assessment analysis module analyzes the hot content, the basic information and the knowledge context to determine the assessment content, and is used to:

[0026] Determining the hot participation degree of the hot content according to the knowledge context and the hot content;

[0027] Obtaining browsing data of the hot content; and determining browsing popularity of the hot content according to the browsing data;

[0028] Analyze the basic information to determine whether the hotspot participation level meets the cognitive standard;

[0029] If the recognition standard is not met, the recognition deficiency share is determined according to the hotspot participation and the browsing popularity;

[0030] The assessment content is determined based on the cognitive deficiency ratio and the knowledge context.

[0031] Through this solution, we can understand the degree of participation of learners in current hot content, reflecting the interests and attention of learners. By analyzing the participation of hot topics, we can adjust the teaching content and assessment direction in a targeted manner to improve the effectiveness of education. Collecting browsing data can reveal the learning habits and preferences of learners and provide data support for personalized learning. At the same time, this also helps to find out which hot content is more popular, thereby optimizing the allocation of learning resources. Through browsing heat analysis, we can evaluate the importance and influence of hot content, providing a basis for formulating learning plans and assessment plans. Content with high browsing heat may require more attention and in-depth learning. Ensure that the participation of learners meets the established cognitive standards to ensure the quality of education. If the participation does not meet the standards, timely measures can be taken to intervene. Identifying the cognitive deficit share helps to clarify in which areas the learners have insufficient knowledge or understanding, and provides direction for subsequent educational intervention and personalized learning. Ensure that the assessment content can be designed for the cognitive deficits of learners, help learners fill in the knowledge gaps, and improve their learning effects. At the same time, this also helps to optimize the feedback mechanism of the entire teaching management system, making it more accurate and efficient.

[0032] Optionally, when the assessment analysis module determines the assessment content according to the cognitive deficiency share and the knowledge context, it is used to:

[0033] Obtaining the assessment record of the learned content;

[0034] Analyze the assessment records to determine historical assessment results;

[0035] Generate a learning heat map according to the knowledge context, the historical assessment results and the hotspot participation;

[0036] The assessment content is determined according to the learning heat map and the cognitive deficiency share.

[0037] Through this solution, by collecting the assessment records of learners, detailed historical data can be provided for subsequent analysis. These data help to understand the learning process and performance changes of learners, and provide a basis for personalized assessment. Analysis of assessment records can reveal the strengths and weaknesses of learners, determine their historical assessment results, and provide quantitative indicators for evaluating the knowledge mastery of learners. The learning heat map presents the knowledge mastery and interests of learners in a visual way. Combined with the knowledge context, it can clearly see the strengths and weaknesses of learners in the knowledge system, providing an intuitive basis for formulating targeted assessment content.

[0038] Optionally, when the answering module displays the assessment content for the learner to answer, it is used to:

[0039] Generate a simulation scenario according to the basic information, the assessment content and the knowledge context;

[0040] The assessment content is displayed according to the simulation scenario for the learner to answer.

[0041] Through this solution, simulation scenarios are generated based on basic information, assessment content, and knowledge context, so that learners can have an intuitive understanding of the assessment content before answering. This simulation scenario can provide a situation similar to the actual working environment, helping learners apply abstract theoretical knowledge to specific situations. Through simulation scenarios, learners can learn and apply knowledge in specific situations, improving the contextuality and practicality of learning. By simulating actual work scenarios, learners can have a deeper understanding of knowledge and learn how to apply this knowledge in different situations. Displaying assessment content in simulation scenarios allows learners to test their knowledge mastery in a more realistic environment, while also receiving instant feedback and adjusting their learning strategies. The combination of assessment content and simulation scenarios makes learners' answers more targeted and better reflects their knowledge application capabilities. When answering in simulation scenarios, learners can interact with the system, and this interactivity helps to improve learners' participation and learning experience.

[0042] Optionally, when the level analysis module analyzes the answer result to determine the learning level of the learner, it is used to:

[0043] Receiving the learner's analysis of the simulation scenario;

[0044] Determining the learner's sensitivity to the hot content based on the discussion analysis;

[0045] Based on the sensitivity, the answer results are analyzed to determine the learning level of the learner.

[0046] Through this program, by receiving learners' analysis of their discussions on simulation scenarios, we can understand learners' unique insights into simulation scenarios, which helps to reveal their personalized learning needs. By analyzing the depth and logic of the discussion, we can evaluate learners' critical thinking and problem-solving abilities. By analyzing the discussion and analysis, we can identify which hot topics learners are more sensitive to, which helps to adjust teaching content and focus, understand learners' emotional tendencies towards hot topics, and provide a basis for emotional education and value guidance. Combining learners' sensitivity and answer results, through the combination of sensitivity analysis and answer results, we can more comprehensively evaluate learners' knowledge mastery and application capabilities, provide learners with targeted feedback, and point out their strengths and areas for improvement.

[0047] Optionally, when the answering module generates a simulation scenario according to the basic information, the assessment content and the knowledge context, it is used to:

[0048] According to the knowledge context, determining whether there are similar scenes in the main learning content;

[0049] Determine the learning requirements of the learners according to the responsible sections;

[0050] If there are similar scenarios, the similar scenarios are adjusted according to the hot content and the learning requirements to obtain the simulated scenario.

[0051] Through this solution, by matching the knowledge context, we ensure that the simulation scenario is closely related to the learning content, and improve the pertinence and relevance of learning. Help learners transfer theoretical knowledge to actual situations and improve their application capabilities. Customize learning scenarios according to the roles and responsibilities of learners to provide a more personalized learning experience. Improve the efficiency and effectiveness of learners by meeting specific learning requirements. The adjusted simulation scenarios are more in line with the actual needs and hot content of learners, enhancing the practicality and timeliness of learning. The adjusted simulation scenarios can better reflect the actual situation of current work and improve the actual effect of learning.

[0052] Optionally, the teaching management system further includes a content adjustment module, connected to the information acquisition module, for:

[0053] Analyze the learning content to determine whether the hot content exists in the main learning content;

[0054] If not, analyzing the learning content to determine the learning progress of the main learning content;

[0055] Adjust the main learning content according to the learning progress and the hot content.

[0056] Through this solution, by analyzing the learning content, learning resources can be updated and optimized in a timely manner to ensure that the learning content is consistent with the current work hotspots. By analyzing the learning progress, we can better understand the knowledge mastery of learners and provide data support for personalized learning. We can provide learners with personalized learning suggestions and resources based on their learning progress to help them learn more effectively. By dynamically adjusting the learning content, we can better meet the personalized needs of learners and improve learning results.

[0057] Optionally, the teaching management system further includes a learning adjustment module, which is connected to the level analysis module and the answering module and is used to:

[0058] Obtaining the answer result and the learning level;

[0059] Determine the type of error according to the answer result and the learning level;

[0060] Determine whether to perform supplementary learning based on the error type and the learning requirement.

[0061] This solution provides a comprehensive understanding of the learning effects and levels of learners, which is the basis for subsequent personalized teaching and tutoring. By analyzing the answer results and learning levels, the weaknesses of learners can be identified, providing a basis for targeted improvement of learning methods and strategies. It ensures the rational allocation of learning resources, avoids waste of resources, and improves learning efficiency. Use information technology and data analysis methods to adjust learning, support the innovative development of education, adapt to the needs of work in the new era, and promote the deep integration of work and information technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0063] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0064] Figure 2 A schematic diagram of the structure of a teaching management system with interactive functions provided in one embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0067] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0068] With the development of the information age, how to improve education and management levels through advanced technical means has become a key issue in enterprise construction and management innovation.

[0069] Education was originally intended to utilize the fragmented learning of learners, but due to the fragmented learning method, it is impossible to establish an effective knowledge context.

[0070] Current teaching management systems often lack dynamic updates and interactivity, especially in analyzing learners' learning situations. They still have certain limitations, which leads to the inability to fully mobilize learners' enthusiasm and fail to effectively deal with cognitive deficiencies and personalized needs that arise in the learning process.

[0071] Based on this, the present application provides a teaching management system with interactive functions, including an information acquisition module, an information analysis module, an assessment analysis module, an answer module, and a level analysis module; the information acquisition module, the information analysis module, the assessment analysis module, the answer module, and the level analysis module are connected in sequence; the information acquisition module is used to obtain the current learning content, hot content and basic information of the learners; the information analysis module is used to analyze the learning content and determine the knowledge context of the learning content; the assessment analysis module is used to analyze the hot content, basic information and knowledge context to determine the assessment content; the answer module is used to display the assessment content for the learners to answer; the level analysis module is used to receive the answer results of the learners; the answer results are analyzed to determine the learning level of the learners. It can obtain the latest learning content and hot information in real time to ensure the timeliness and relevance of learning materials. Collect the basic information of learners to provide data support for subsequent personalized learning planning and assessment. Integrate data from different sources to provide a comprehensive data foundation for the information analysis module. By analyzing the learning content, build a knowledge context to help learners understand the connection between knowledge points. Analyze the personal information and behavior data of learners, build user portraits, and provide a basis for personalized recommendations. Identify current hot topics and provide a basis for hot topic participation analysis for the assessment analysis module. Customize personalized assessment content based on hot topic content, basic information of learners, and knowledge context. Evaluate learners' knowledge mastery and hot topic participation by analyzing assessment results. Diagnose learners' cognitive deficiencies and provide a basis for subsequent learning adjustments. Provide simulation scenarios and assessment interfaces to enhance learners' sense of participation and interactivity. Real-time feedback during the answering process helps learners understand their learning status and errors in a timely manner. Enhance learners' learning motivation and interest through interaction and feedback. Collect and count learners' answer results to provide quantitative data for learning effect evaluation. Analyze answer results to evaluate learners' actual learning level and progress. Provide learners with personalized learning content and recommendations based on learning level evaluation results.

[0072] Figure 1 A schematic diagram of an application scenario provided for this application is used to analyze the learning situation of learners. Specifically, the system provided by this application is applied to any server, and the server interacts with the education platform to update the latest learning content and hot information from the database of the education platform in real time to ensure the timeliness and relevance of the learning materials. Through user identity authentication and authority management, the basic information of the learners is collected to provide data support for personalized learning. Data synchronization technology is used to ensure that the data transmission between the information acquisition module and the information analysis module is timely and accurate. Natural language processing (NLP) is performed on the acquired learning content to extract keywords, themes and key points of knowledge. Graph theory and data mining technology are used to construct the knowledge context of the learning content and identify the association between knowledge points. Through machine learning algorithms, the personal information and learning behavior of learners are analyzed, and user portraits are constructed to provide a basis for personalized recommendations. The trend and importance of hot content are analyzed, and the key points of assessment are determined in combination with the knowledge context and basic information of learners. Ensure that the learning content is consistent with the current work hotspots and improve the timeliness and relevance of learning materials. Data mining technology is used to obtain browsing data and interaction data of hot content, and evaluate hot participation and browsing heat. Provide learners with personalized learning plans and recommendations based on user portraits and knowledge context. Determine the difficulty and coverage of the assessment content based on the assessment standards and the learners' cognitive level. Generate simulation scenarios and assessment interfaces based on the assessment content using multimedia technology and interactive design. Ensure that the assessment content is clearly presented and easy to operate through user interface (UI) design. Collect and store learners' answers, and use data analysis technology to perform score statistics and evaluate learning effects. Enhance learners' interactivity and sense of participation through simulation scenarios and assessment interfaces. Analyze the answer results to identify learners' error types and cognitive deficiencies. Evaluate learners' actual learning level and progress by collecting and analyzing answer results.

[0073] For specific implementation methods, please refer to the following embodiments.

[0074] Figure 2 A structural diagram of a teaching management system with interactive functions provided in one embodiment of the present application is shown in FIG. Figure 2As shown, the teaching management system 200 with interactive functions of this embodiment includes an information acquisition module 201, an information analysis module 202, an assessment analysis module 203, an answering module 204, and a level analysis module 205; the information acquisition module 201, the information analysis module 202, the assessment analysis module 203, the answering module 204, and the level analysis module 205 are connected in sequence; the information acquisition module 201 is used to obtain the learning content, hot content and basic information of the learners at the current moment; the information analysis module 202 is used to analyze the learning content and determine the knowledge context of the learning content; the assessment analysis module 203 is used to analyze the hot content, basic information and knowledge context and determine the assessment content; the answering module 204 is used to display the assessment content for the learners to answer; the level analysis module 205 is used to receive the answer results of the learners; analyze the answer results and determine the learning level of the learners.

[0075] The learning content can be the learning materials specified by the enterprise, including but not limited to knowledge in theory, policy, regulations, history, construction, etc.

[0076] Hot topics or events can be hot topics or events in the current social, economic and other fields.

[0077] Basic information can be basic information about the learner, such as name, position, work unit, learning history, assessment records, etc.

[0078] Knowledge context can be the logical relationship and structure between knowledge points in the learning content.

[0079] The assessment content can be test questions or tasks determined based on the learning content, hot topics and knowledge context.

[0080] The answer result can be the learner's answer or reaction to the assessment content, which can reflect the learner's understanding and application of knowledge.

[0081] The learning level can be the degree to which the learner has mastered the knowledge, including the ability to understand, apply, and analyze.

[0082] Specifically, the latest learning content and hot information are obtained from the database of the education platform in real time. Through user identity authentication and permission management, basic information of learners, such as personal information, learning history, and responsibility sections, is collected. Data synchronization technology is used to ensure timely and accurate data transmission between the information acquisition module and the information analysis module. Natural language processing (NLP) is performed on the acquired learning content to extract keywords, topics, and key knowledge points. Graph theory and data mining technology are used to construct the knowledge context of the learning content and identify the associations between knowledge points. Through machine learning algorithms, the personal information and learning behavior of learners are analyzed to construct user portraits. The trend and importance of hot content are analyzed, and the key points of assessment are determined in combination with the knowledge context and basic information of learners. Data mining technology is used to obtain browsing data and interaction data of hot content, and hot participation and browsing popularity are evaluated. According to the assessment standards and the cognitive level of learners, the difficulty and coverage of the assessment content are determined. According to the assessment content, multimedia technology and interactive design are used to generate simulation scenarios and assessment interfaces. Through user interface (UI) design, the assessment content is ensured to be clearly displayed and easy to operate. Collect and store the answers of learners, and use data analysis technology to perform score statistics and evaluate learning effects. Analyze the answer results, identify the types of errors and cognitive deficiencies of learners, and provide guidance for subsequent learning. Use visualization technology to generate learning heat maps to intuitively display the knowledge mastery and hot spot participation of learners.

[0083] Through this solution, the latest learning content and hot information can be obtained in real time to ensure the timeliness and relevance of learning materials. Collect the basic information of learners to provide data support for subsequent personalized learning planning and assessment. Integrate data from different sources to provide a comprehensive data foundation for the information analysis module. Analyze the learning content and build the knowledge context to help learners understand the connection between knowledge points. Analyze the personal information and behavior data of learners to build user portraits to provide a basis for personalized recommendations. Identify the current hot content and provide a basis for hot participation analysis for the assessment analysis module. Customize personalized assessment content based on hot content, basic information of learners and knowledge context. Evaluate learners' knowledge mastery and hot participation by analyzing the assessment results. Diagnose learners' cognitive deficiencies to provide a basis for subsequent learning adjustments. Provide simulation scenarios and assessment interfaces to enhance learners' sense of participation and interactivity. Real-time feedback during the answering process helps learners understand their learning status and errors in a timely manner. Enhance learners' learning motivation and interest through interaction and feedback. Collect and count learners' answer results to provide quantitative data for learning effect evaluation. Analyze the answer results and evaluate the learners' actual learning level and progress. Provide learners with personalized learning content and recommendations based on the learning level evaluation results.

[0084] In some embodiments, when the information analysis module analyzes the learning content and determines the knowledge context of the learning content, it is used to: analyze the learning content to determine the learned content and the content being learned; determine the involved categories based on the learned content; determine the cross-categories of the content based on the learned content and the content being learned; determine the knowledge context of the learning content based on the involved categories and the cross-categories of the content.

[0085] The learned content can be the knowledge points, courses or topics that the learner has been exposed to and mastered in the past learning process.

[0086] The content of formal learning can be the knowledge points, courses or topics that the learners are currently learning or are about to learn.

[0087] The involved categories can be the topics or knowledge areas included in the learned content. They can be macro. The cross-content categories can be the topics or knowledge areas included in both the learned content and the current content. They reflect the continuity and depth of the learners in the knowledge system, and also show the correlation between different learning contents.

[0088] Specifically, the learning content is preprocessed to remove irrelevant formatting information, such as HTML tags, and retain the plain text content. Use Chinese word segmentation technology to segment the learning content text into word units to provide basic data for subsequent analysis. Extract information about the learned content from the learner's historical learning records, such as learned chapters and topics. Analyze the current learning content and identify the key concepts and knowledge points. Based on the learned content, use natural language processing technology to identify the subject categories involved. Analyze the relationship between the learned content and the categories and build a category network. Compare the learned content with the current content to find the intersection between the two, that is, the subject categories involved in common. Calculate the weight for each cross-category to reflect its importance in the learning content. Use a graph database to build a relationship graph between knowledge points to form a knowledge context. In the knowledge graph, analyze the path between the learned content and the current content to determine the structure of the knowledge context.

[0089] In some specific implementations, the learning data of learners can also be monitored, and the knowledge context can be updated in real time to reflect the latest learning dynamics. A user interface is provided to allow learners to interact with the system, such as asking questions and obtaining explanations. The knowledge context is presented to users in a graphical manner to help learners intuitively understand the relationship between knowledge points. In the knowledge context diagram, the current hot content is marked and highlighted.

[0090] Through this solution, by analyzing the learning content, it is possible to distinguish between the knowledge that learners have already mastered (learned content) and the knowledge that they are currently learning (currently learning content), thereby providing a data basis for personalized learning path planning. By identifying keywords and topics in the learned content and classifying them into corresponding categories, learners can be helped to establish a knowledge framework and provide a reference for subsequent learning. By comparing the learned content and the current learning content, the intersection between the two can be found and the content cross-category can be determined, which is crucial to deepening learners' understanding and application of knowledge. The constructed knowledge context diagram can clearly show the relationship between knowledge points, help learners understand the structure and logic of the knowledge system, and improve the systematicness and depth of learning.

[0091] In some embodiments, the teaching management system also includes a learning planning module, which is connected to the information acquisition module and the information analysis module, and is used to: analyze basic information to determine the responsible sections of the learners; analyze knowledge context and hot content to determine the degree of understanding of current hot spots; and generate new learning content based on the responsible sections and the degree of understanding of current hot spots.

[0092] The responsible section can be the specific duties and tasks of the learners in their work.

[0093] The degree of understanding of current hot topics can be the degree to which learners are aware of hot topics or events in the current social, economic and other fields.

[0094] Specifically, the basic information of learners, such as positions, job responsibilities, and learning history, is collected from the information acquisition module. The collected data is cleaned and standardized to ensure the accuracy and consistency of the data. According to the positions and job responsibilities of learners, the corresponding learning sections and topics are matched. By analyzing the learning history and preferences of learners, their personalized learning needs are identified. The logical relationship between learning contents is analyzed using the knowledge context constructed by the information analysis module. Current hot content and trends are identified through data analysis technology. The participation of learners in hot content, such as the number of views and interaction frequency, is analyzed. The mastery of hot content by learners is evaluated through the assessment analysis module. New learning content, including courses, articles, videos, etc., is recommended based on the responsible sections and the degree of understanding of hot topics. Personalized learning plans and courses are customized according to the knowledge level and learning needs of learners. The progress and feedback of learners are monitored in real time, and learning plans and content are adjusted in a timely manner.

[0095] Through this solution, by analyzing the basic information of learners, it is possible to accurately identify the responsibilities and work priorities of learners, thereby recommending learning content closely related to their work and improving the practicality and pertinence of learning. By analyzing the knowledge context and hot content, it is possible to evaluate the learners' understanding of current hot topics, provide a basis for formulating personalized learning plans, and ensure that the learning content is in sync with the pulse of the times. According to the learners' responsible sections and understanding of hot topics, new learning content is automatically generated to achieve dynamic updating and personalized customization of learning, meeting the differentiated needs of different learners.

[0096] In some embodiments, the assessment analysis module analyzes hot content, basic information and knowledge context, and when determining the assessment content, it is used to: determine the hot engagement of the hot content based on the knowledge context and the hot content; obtain browsing data of the hot content; determine the browsing popularity of the hot content based on the browsing data; analyze the basic information to determine whether the hot engagement meets the cognitive standard; if it does not meet the cognitive standard, determine the cognitive defect share based on the hot engagement and browsing popularity; determine the assessment content based on the cognitive defect share and the knowledge context.

[0097] Hotspot participation can be the degree of participation of learners in current hot content, which is measured by the learners' activeness in interactive behaviors such as discussion, comment, and sharing.

[0098] Browsing data can be the records of learners' access to hot content in the teaching management system, including information such as the number of visits, stay time, and access path.

[0099] Browsing popularity can be the popularity of hot content within a certain period of time, usually measured by indicators such as number of views, number of likes, and number of reposts.

[0100] Cognitive standards can be the basic requirements set by enterprises for learners to understand and master hot content, and are used to evaluate whether the learners' cognitive level meets the expected goals.

[0101] The cognitive deficit share can be the deficiencies or missing parts of the learners' cognition of hot content, which is usually determined by comparing the learners' actual performance with the cognitive standard.

[0102] Specifically, the information acquisition module collects the interactive data of learners on hot content, such as comments, sharing, and discussion. Using data analysis technology, calculate the learners' participation in hot content, such as participation times, activity, and other indicators. Monitor learners' browsing behavior on hot content and collect browsing time, frequency, and other data. Analyze browsing data to understand the learners' attention to hot content. Based on browsing data, calculate the browsing popularity of hot content, which can be calculated by methods such as heat index and ranking. Analyze the browsing trend of hot content to understand its changes over time. Set cognitive standards for hot participation, such as the minimum number of participations and interaction quality. Compare the actual participation of learners with cognitive standards to determine whether they meet the standards. If the hot participation does not meet the cognitive standards, analyze in which aspects the learners have cognitive defects. According to the severity of cognitive defects, calculate the cognitive defect share, that is, the proportion of cognitive defects in the entire hot content. According to the cognitive defect share and knowledge context, customize targeted assessment content to help learners make up for cognitive defects. According to the actual level of learners, adjust the difficulty of assessment content to ensure the rationality and effectiveness of assessment.

[0103] Through this solution, we can understand the degree of participation of learners in current hot content, reflecting the interests and attention of learners. By analyzing the participation of hot topics, we can adjust the teaching content and assessment direction in a targeted manner to improve the effectiveness of education. Collecting browsing data can reveal the learning habits and preferences of learners and provide data support for personalized learning. At the same time, this also helps to find out which hot content is more popular, thereby optimizing the allocation of learning resources. Through browsing heat analysis, we can evaluate the importance and influence of hot content, providing a basis for formulating learning plans and assessment plans. Content with high browsing heat may require more attention and in-depth learning. Ensure that the participation of learners meets the established cognitive standards to ensure the quality of education. If the participation does not meet the standards, timely measures can be taken to intervene. Identifying the cognitive deficit share helps to clarify in which areas the learners have insufficient knowledge or understanding, and provides direction for subsequent educational intervention and personalized learning. Ensure that the assessment content can be designed for the cognitive deficits of learners, help learners fill in the knowledge gaps, and improve their learning effects. At the same time, this also helps to optimize the feedback mechanism of the entire teaching management system, making it more accurate and efficient.

[0104] In some embodiments, when the assessment analysis module determines the assessment content based on the cognitive deficiency share and the knowledge context, it is used to: obtain the assessment records of the learned content; analyze the assessment records to determine the historical assessment results; generate a learning heat map based on the knowledge context, historical assessment results and hot spot participation; determine the assessment content based on the learning heat map and the cognitive deficiency share.

[0105] Assessment records can be various records and data left by learners participating in assessment activities during the learning process.

[0106] Historical assessment results can be a summary of the scores obtained by learners in past assessment activities.

[0107] The learning heat map can be a data visualization tool that graphically displays the knowledge mastery of learners and the hot spots of learning activities.

[0108] Specifically, retrieve the assessment records of the content that the learner has learned, including test scores, assessment questions, assessment time and other information. Integrate the records of different assessments to form a complete assessment history record. Perform statistical analysis on the assessment records to calculate the learner's average score and score distribution in each assessment. Analyze the learner's historical assessment score trends to understand their learning progress or regression. Combine the knowledge context with historical assessment scores and hot spot participation to establish data associations. Use data visualization technology to generate a learning heat map, which can show the learning heat of different knowledge points and the distribution of learners' assessment scores. Analyze the learning heat map, identify the learner's strengths and weaknesses, and determine the key content of the assessment based on the cognitive deficiency share. Customize personalized assessment content based on the analysis results, including strengthening the assessment of weak knowledge and consolidating the assessment of strong knowledge.

[0109] Through this solution, by collecting the assessment records of learners, detailed historical data can be provided for subsequent analysis. These data help to understand the learning process and performance changes of learners, and provide a basis for personalized assessment. Analysis of assessment records can reveal the strengths and weaknesses of learners, determine their historical assessment results, and provide quantitative indicators for evaluating the knowledge mastery of learners. The learning heat map presents the knowledge mastery and interests of learners in a visual way. Combined with the knowledge context, it can clearly see the strengths and weaknesses of learners in the knowledge system, providing an intuitive basis for formulating targeted assessment content.

[0110] In some embodiments, when the answer module displays the assessment content for learners to answer, it is used to: generate a simulation scenario based on basic information, assessment content and knowledge context; and display the assessment content for learners to answer based on the simulation scenario.

[0111] A simulation scenario can be a virtual environment created through technical means described in text, simulating a specific situation or working environment in the real world.

[0112] Specifically, relevant data is integrated based on the learner's basic information, assessment content, and knowledge context. This data is used to generate simulation scenarios, which may include simulated work environments, historical backgrounds, policy making, and other situations, aiming to allow learners to apply what they have learned in real situations. In the simulation scenario, the assessment content is displayed, which may be questions, case studies, decision-making simulations, etc. Learners can interact with the assessment content in the simulation scenario, for example, answer questions, complete specific tasks, or solve simulated problems through discussion. Provide instant feedback based on the learner's answers, indicate whether they are correct or not, and give explanations or suggestions.

[0113] Through this solution, simulation scenarios are generated based on basic information, assessment content, and knowledge context, so that learners can have an intuitive understanding of the assessment content before answering. This simulation scenario can provide a situation similar to the actual working environment, helping learners apply abstract theoretical knowledge to specific situations. Through simulation scenarios, learners can learn and apply knowledge in specific situations, improving the contextuality and practicality of learning. By simulating actual work scenarios, learners can have a deeper understanding of knowledge and learn how to apply this knowledge in different situations. Displaying assessment content in simulation scenarios allows learners to test their knowledge mastery in a more realistic environment, while also receiving instant feedback and adjusting their learning strategies. The combination of assessment content and simulation scenarios makes learners' answers more targeted and better reflects their knowledge application capabilities. When answering in simulation scenarios, learners can interact with the system, and this interactivity helps to improve learners' participation and learning experience.

[0114] In some embodiments, when the level analysis module analyzes the answer results and determines the learner's learning level, it is used to: receive the learner's discussion and analysis of the simulation scenario; determine the learner's sensitivity to hot content based on the discussion analysis; and analyze the answer results based on the sensitivity to determine the learner's learning level.

[0115] Argument analysis can be the process of analyzing and understanding the text content provided by learners.

[0116] Sensitivity can be the degree of response and attention of learners to a specific topic or issue; in the context of education, sensitivity analysis is often used to assess learners' sensitivity and understanding of knowledge, policy trends or social hot spots.

[0117] Specifically, collect the text of learners' answers in the simulation scenarios, including discussion, analysis, case analysis, and decision-making process. Use natural language processing (NLP) technology to analyze learners' discussions and extract key information and opinions. Use sentiment analysis technology to judge learners' attitudes and emotional tendencies towards hot topics, such as positive, negative, or neutral. Identify keywords and phrases in the discussion and analyze learners' focus on hot topics. Combine the analysis of learners' discussion and sensitivity analysis results to conduct a comprehensive analysis of the answer results. Based on the analysis results, evaluate learners' depth of understanding of knowledge, application ability, and comprehensive quality.

[0118] Through this program, by receiving learners' analysis of their discussions on simulation scenarios, we can understand learners' unique insights into simulation scenarios, which helps to reveal their personalized learning needs. By analyzing the depth and logic of the discussion, we can evaluate learners' critical thinking and problem-solving abilities. By analyzing the discussion and analysis, we can identify which hot topics learners are more sensitive to, which helps to adjust teaching content and focus, understand learners' emotional tendencies towards hot topics, and provide a basis for emotional education and value guidance. Combining learners' sensitivity and answer results, through the combination of sensitivity analysis and answer results, we can more comprehensively evaluate learners' knowledge mastery and application capabilities, provide learners with targeted feedback, and point out their strengths and areas for improvement.

[0119] In some embodiments, when the answering module generates a simulation scenario based on basic information, assessment content and knowledge context, it is used to: determine whether there are similar scenarios in the regular content based on the knowledge context; determine the learning requirements of the learners based on the responsible sections; if there are similar scenarios, adjust the similar scenarios based on the hot content and learning requirements to obtain a simulation scenario.

[0120] Similar scenarios can be other scenarios or cases in the learning content or knowledge context that are similar to the topic or knowledge point currently being discussed.

[0121] Learning requirements can be specific learning goals and standards set for learners based on their roles, responsibilities and goals.

[0122] Specifically, use natural language processing technology to analyze the knowledge context and understand the relationship between knowledge points. Use pattern recognition and matching algorithms to determine whether there are existing scenarios in the content that match the knowledge context. Analyze the roles and responsibilities of learners in education and determine their learning requirements. Map learning requirements to specific responsible sections, such as policy formulation, publicity and education, and corporate management. If there are similar scenarios, make appropriate adjustments to the scenarios based on hot content and learning requirements to meet the actual needs of learners. Design interactive elements in the scenarios to ensure that learners can effectively learn and practice in simulated scenarios.

[0123] Through this solution, by matching the knowledge context, we ensure that the simulation scenario is closely related to the learning content, and improve the pertinence and relevance of learning. Help learners transfer theoretical knowledge to actual situations and improve their application capabilities. Customize learning scenarios according to the roles and responsibilities of learners to provide a more personalized learning experience. Improve the efficiency and effectiveness of learners by meeting specific learning requirements. The adjusted simulation scenarios are more in line with the actual needs and hot content of learners, enhancing the practicality and timeliness of learning. The adjusted simulation scenarios can better reflect the actual situation of current work and improve the actual effect of learning.

[0124] In some embodiments, the teaching management system also includes a content adjustment module, which is connected to the information acquisition module and is used to: analyze the learning content to determine whether there is hot content in the main learning content; if not, analyze the learning content to determine the learning progress of the main learning content; adjust the main learning content according to the learning progress and hot content.

[0125] Learning progress can be the degree to which learners have mastered the learning content and the stage they are in the learning journey during the learning process.

[0126] Specifically, data mining technology is used to extract keywords and phrases from learning content to identify possible hot content. Through machine learning algorithms or natural language processing technology, the correlation between learning content and identified hot content is analyzed to determine whether there is hot content. Track the reading or learning progress of learners and determine the learning progress by analyzing the frequency and time of learning content access. Analyze the learning behavior of learners, such as the length and frequency of watching videos and reading documents, to evaluate their learning progress. Based on the learning progress and hot content, relevant learning resources such as articles, videos or interactive courses can be recommended. If the learning content lacks hot content, the learning content can be dynamically updated and relevant hot cases or news can be introduced to enhance the timeliness and attractiveness of learning.

[0127] Through this solution, by analyzing the learning content, learning resources can be updated and optimized in a timely manner to ensure that the learning content is consistent with the current work hotspots. By analyzing the learning progress, we can better understand the knowledge mastery of learners and provide data support for personalized learning. We can provide learners with personalized learning suggestions and resources based on their learning progress to help them learn more effectively. By dynamically adjusting the learning content, we can better meet the personalized needs of learners and improve learning results.

[0128] In some embodiments, the teaching management system also includes a learning adjustment module, which is connected to the level analysis module and the answering module, and is used to: obtain the answering results and learning levels; determine the error type based on the answering results and learning levels; and determine whether to conduct supplementary learning based on the error type and learning requirements.

[0129] The error types may be the types of errors that learners may make during the learning or answering process.

[0130] Specifically, the answer results and learning level data of the learners are obtained from the answer module and the level analysis module. The obtained data is cleaned to ensure the quality and accuracy of the data. The answer results are analyzed using machine learning algorithms to identify common types of errors, such as conceptual misunderstanding errors, application errors, logical errors, etc. Through pattern recognition technology, the patterns and regularities of learners' mistakes are found. According to the types of errors and learning requirements, a decision can be made on whether supplementary learning is needed. If supplementary learning is needed, corresponding learning resources or supplementary courses can be recommended based on the types of errors and learning requirements of the learners.

[0131] This solution provides a comprehensive understanding of the learning effects and levels of learners, which is the basis for subsequent personalized teaching and tutoring. By analyzing the answer results and learning levels, the weaknesses of learners can be identified, providing a basis for targeted improvement of learning methods and strategies. It ensures the rational allocation of learning resources, avoids waste of resources, and improves learning efficiency. Use information technology and data analysis methods to adjust learning, support the innovative development of education, adapt to the needs of work in the new era, and promote the deep integration of work and information technology.

Claims

1. A teaching management system with interactive function, characterized in that: It includes information acquisition module, information analysis module, assessment analysis module, answering module and level analysis module; The information acquisition module, the information analysis module, the assessment analysis module, the answering module, and the level analysis module are connected in sequence; The information acquisition module is used to obtain the current learning content, hot content and basic information of learners; The information analysis module is used to analyze the learning content and determine the knowledge context of the learning content; The assessment analysis module is used to analyze the hot content, the basic information and the knowledge context to determine the assessment content; The answering module is used to display the assessment content for the learner to answer; The level analysis module is used to receive the answer results of the learner; analyze the answer results, and determine the learning level of the learner.

2. The system according to claim 1, characterized in that When the information analysis module analyzes the learning content and determines the knowledge context of the learning content, it is used to: Analyze the learning content to determine what has been learned and what is being learned; Determine the categories involved based on the content learned; Determine content cross-category based on the learned content and the current learning content; The knowledge context of the learning content is determined based on the involved categories and the content cross-categories.

3. The system according to claim 2, characterized in that The teaching management system further includes a learning planning module, which is connected to the information acquisition module and the information analysis module and is used to: Analyze the basic information and determine the responsible section of the learner; Analyze the knowledge context and the hot topics to determine the current level of understanding of the hot topics; Generate new learning content based on the responsible section and the level of understanding of the current hot spots.

4. The system according to claim 3, characterized in that The assessment analysis module analyzes the hot content, the basic information and the knowledge context to determine the assessment content, and is used to: Determining the hot participation degree of the hot content according to the knowledge context and the hot content; Obtaining browsing data of the hot content; and determining browsing popularity of the hot content according to the browsing data; Analyze the basic information to determine whether the hotspot participation level meets the cognitive standard; If the recognition standard is not met, the recognition deficiency share is determined according to the hotspot participation and the browsing popularity; The assessment content is determined based on the cognitive deficiency ratio and the knowledge context.

5. The system according to claim 4, characterized in that When the assessment analysis module determines the assessment content according to the cognitive deficiency share and the knowledge context, it is used to: Obtaining the assessment record of the learned content; Analyze the assessment records to determine historical assessment results; Generate a learning heat map according to the knowledge context, the historical assessment results and the hotspot participation; The assessment content is determined according to the learning heat map and the cognitive deficiency share.

6. The system according to claim 5, characterized in that When the answering module displays the assessment content for the learner to answer, it is used to: Generate a simulation scenario according to the basic information, the assessment content and the knowledge context; The assessment content is displayed according to the simulation scenario for the learner to answer.

7. The system according to claim 6, characterized in that When the level analysis module analyzes the answer result and determines the learning level of the learner, it is used to: Receiving the learner's analysis of the simulation scenario; Determining the learner's sensitivity to the hot content based on the discussion analysis; Based on the sensitivity, the answer results are analyzed to determine the learning level of the learner.

8. The system according to claim 6, characterized in that When the answering module generates a simulation scenario based on the basic information, the assessment content and the knowledge context, it is used to: According to the knowledge context, determining whether there are similar scenes in the main learning content; Determine the learning requirements of the learners according to the responsible sections; If there are similar scenarios, the similar scenarios are adjusted according to the hot content and the learning requirements to obtain the simulated scenario.

9. The system according to claim 2, characterized in that The teaching management system further includes a content adjustment module, which is connected to the information acquisition module and is used to: Analyze the learning content to determine whether the hot content exists in the main learning content; If not, analyzing the learning content to determine the learning progress of the main learning content; Adjust the main learning content according to the learning progress and the hot content.

10. The system according to claim 8, characterized in that The teaching management system further includes a learning adjustment module, which is connected to the level analysis module and the answering module and is used to: Obtaining the answer result and the learning level; Determine the type of error according to the answer result and the learning level; Determine whether to perform supplementary learning based on the error type and the learning requirement.

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