A teaching intelligent management method and system suitable for course-post competition certificate integration
By using intelligent management systems and heterogeneous graph technology, the integration problem of learning path planning was solved, dynamic optimization of job training, courses, competitions and certifications was achieved, and the scientific nature of teaching management and learning efficiency were improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU VOCATIONAL COLLEGE OF TECH & BUSINESS
- Filing Date
- 2026-06-08
- Publication Date
- 2026-07-14
AI Technical Summary
Existing learning path planning methods cannot achieve deep integration of job training, courses, competitions, and certifications. They lack personalized learning paths and assessment mechanisms, resulting in learning paths that can only be adapted to a limited number of job requirements, leaving students with fewer employment options.
An intelligent management system is adopted to create student profiles by acquiring multi-dimensional individual information of students, connect to external information databases, generate optimal learning paths, and dynamically update student profiles. In combination with course, competition, certificate and employment information, heterogeneous graphs are used for path planning.
It integrates course learning, competition practice, certificate acquisition and job placement, dynamically optimizes learning paths, improves learning efficiency and results conversion rate, reduces human intervention, and enhances the scientific and automated level of teaching management.
Smart Images
Figure CN122390934A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent management technology for teaching data, specifically to an intelligent management method and system for integrated teaching management of courses, positions, competitions, and certifications. Background Technology
[0002] With the transformation of education models and the rapid development of technology, traditional teaching methods are gradually failing to meet the diverse learning needs of modern students. Existing teaching methods often focus on single-module learning, such as knowledge acquisition, skills certification, or practical competitions, but lack effective integration and consolidation. This leads to a disconnect between students' knowledge and skills application, making it difficult to achieve all-round development. Therefore, more and more universities are adopting a new teaching model that integrates job skills, courses, competitions, and certifications. This model emphasizes the organic combination of theory, vocational skills, and competitions, enabling students to apply their knowledge in real-world work scenarios and enhance their competitiveness through certification. However, current market-based auxiliary development platforms have not fully realized the deep integration of job skills, courses, competitions, and certifications, and lack personalized learning paths and evaluation mechanisms. Existing learning path planning methods can plan corresponding learning paths for students based on specific job requirements, but they rely on available industry information, focusing on cultivating specific vocational skills while ignoring the potential connections between various vocational skills. This results in learning paths that can only adapt to a limited number of job requirements, limiting students' employment options and creating significant limitations. Summary of the Invention
[0003] The technical problem this invention aims to solve is that existing learning path planning methods cannot effectively integrate job skills, courses, competitions, and certifications, and lack personalized learning paths and evaluation mechanisms. As a result, when planning learning paths for students, they rely on existing industry information, focus on cultivating specific professional skills, and ignore the potential connections between various professional skills. Consequently, the planned learning paths can only be adapted to a limited number of job requirements, resulting in fewer job options for students and significant limitations.
[0004] To solve the above-mentioned technical problems, the first aspect of the present invention adopts the following technical solution: a method for intelligent management of integrated teaching, including the following steps:
[0005] S1: The intelligent management system initializes, obtains a set of student profiles containing multi-dimensional individual student information, and connects to a preset external information database containing relevant course information, relevant competition information, relevant professional certificate information, and relevant job information;
[0006] S2: Call an external information database to generate the best learning path for students based on their profiles and preset time points;
[0007] S3: Obtain multi-dimensional individual information of students when they are learning according to the optimal learning path, update the student profile, and proceed to step S2. At the same time, output suggestions for participating in relevant competitions and / or obtaining relevant professional certificates according to the learning progress. When a student participates in a competition, proceed to step S4. When a student obtains a professional certificate, proceed to step S5.
[0008] S4: Obtain multidimensional individual information of students when they participate in the competition, update student profiles, and proceed to step S2;
[0009] S5: Obtain multi-dimensional individual information of students when they take professional certificates, update student profiles, and proceed to step S2.
[0010] When this invention is in operation, it can integrate course learning, competition practice, certificate acquisition, and job placement into a single framework, thereby solving the problem of fragmented links in traditional teaching management. The student profile can be dynamically updated based on multi-dimensional individual information according to learning progress, competitions, certificate acquisition, and other events, accurately reflecting the student's true status. It can also further optimize the optimal learning path at different time points, ensuring that the generated optimal learning path always matches the student's actual progress and ultimate goal, thus effectively avoiding the impact of rigid static paths on students. At the same time, it can also complete the time-series planning of learning activities, competition participation, and certificate acquisition, helping students grasp key nodes, improve learning efficiency and achievement conversion rate, and effectively improve the scientific nature and automation level of teaching management.
[0011] Preferably, in step S1, the student profile is created using the following steps:
[0012] A1: Obtain multidimensional individual information of students, which includes at least one of the following: hard skills information, soft skills information, preference information, goal information, learning stage, completed competitions, and obtained professional certificates. Create a student profile for each student based on the multidimensional individual information of each student.
[0013] A2: Cluster and correct the student portraits of several students according to the preset classification features, and make individual corrections to the student portraits in the set whose relevant feature deviation exceeds the preset threshold based on the average portrait of each set. After completing the update of the student portraits, output several student portraits to obtain a set of student portraits.
[0014] When this invention is working, it uses a clustering correction method to specifically correct individuals whose profiles deviate from the average profile of the set by more than a threshold. This can eliminate outliers caused by data collection errors or student self-assessment biases, making student profiles within the same category more stable. This preserves individual differences while avoiding extreme erroneous profiles from affecting subsequent path planning. It also reduces subjective human intervention, lowers the workload of teachers, improves the efficiency of student profile creation, and provides sufficiently reliable data references for the generation of the optimal learning path.
[0015] Preferably, the establishment of the external information database adopts the following steps:
[0016] B1: Select matching information sources as seed sources based on course information, competition information, professional certificate information, and job information related to teaching activities;
[0017] B2: Perform incremental crawling or comprehensive retrieval of relevant information according to the preset crawling frequency to obtain raw data of relevant course information, relevant competition information, relevant professional certificate information and relevant job information, and clean and standardize the raw data;
[0018] B3: Extract key features from the original data and store them in a structured manner in an external information database. Add an index to each key feature according to the data type, and establish a heterogeneous graph to represent the relationship between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information, thus completing the establishment of the external information database.
[0019] When this invention is in operation, it can integrate previously isolated and heterogeneous information such as courses, competitions, professional certificates, and job positions into a unified external information database. This lays a reliable data foundation for subsequent correlation analysis. At the same time, by using incremental crawling and comprehensive retrieval, it can obtain the latest dynamic information such as competition and job requirements and changes in professional certificates in real time or periodically, so that teaching management can always keep up with industry changes. Through key feature indexing and heterogeneous graph structure, it can quickly locate courses, competitions, professional certificates and corresponding job positions for a certain professional skill, which greatly improves the retrieval efficiency of path planning.
[0020] Preferably, in step B3, when extracting key features from the original data, storing them in a structured manner in an external information database, and adding an index to each key feature according to the data type to establish a heterogeneous graph representing the relationships between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information, the following steps are adopted:
[0021] C1: After feature extraction from the raw data of relevant course information, several professional knowledge items, several professional skills items, and several mastery levels for each professional skill item are generated. After extracting the raw data of relevant competition information, relevant professional certificate information, and relevant job information, several professional skill requirements are generated, and several mastery level requirements for each professional skill requirement are generated.
[0022] C2: Based on several professional knowledge items, several professional skills and their mastery levels, several professional skill requirements and their mastery level requirements, relevant competition information, relevant professional certificate information, and relevant job information, generate several nodes in the heterogeneous graph, and add an index to each node based on the relevant information of each node to generate edges of related nodes, resulting in a professional knowledge-professional skills-professional skill requirements-learning activity heterogeneous graph used to represent the relationship between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information.
[0023] When this invention is working, it can break down relevant course information into fine-grained professional knowledge and skills, and quantify the professional skill requirements for participating in competitions, obtaining professional certificates, and job positions to achieve a precise match between learning and application. It can also deeply mine the potential connections between various learning activities through heterogeneous graphs, without the need for manual association or AI intelligent judgment, effectively saving human and material resources. By converting the learning path generation into a path search problem on the graph, the algorithm has strong interpretability, making it convenient for students to manually adjust the learning path according to actual needs and facilitating personalized selection.
[0024] Preferably, step B1 further includes the following step: calling a preset supplementary information source containing actual work cases, performance appraisal standards and job promotion requirements corresponding to the job position as a seed source.
[0025] Step B3 further includes the following steps: extracting implicit key features from the original data containing actual work cases, performance appraisal standards, and job promotion requirements, generating several implicit capability requirements, adding an index to each implicit capability requirement based on the relevant information obtained for each implicit requirement, generating edges with associated nodes, and updating the external information database.
[0026] When this invention is in operation, it can extract implicit soft skills, project experience, teamwork and other implicit abilities from actual work cases, performance appraisals and promotion requirements, thereby further improving the heterogeneous graph and enabling the generated optimal learning path to further guide students to participate in specific types of learning activities, so as to cultivate students' ability to adapt to the implicit requirements of the job in advance, thereby helping students to plan a longer-term ability advancement path.
[0027] Preferably, in step S2, when calling an external information database to generate the optimal learning path for students based on their profiles and preset time points, the following steps are adopted:
[0028] D1: Determine the target employment direction based on the student profile, analyze the set of professional knowledge and skills mastered by the student at the current learning stage, and call on the target employment direction to call on the external information database to obtain the set of professional knowledge and skills that need to be further mastered;
[0029] D2: Based on the set of professional knowledge and skills that students have mastered at the current learning stage, locate the relevant nodes in the heterogeneous graph, and extract at least one learning path in the heterogeneous graph through a constrained heuristic search method based on the set of professional knowledge and skills that students need to further master.
[0030] D3: Based on the student profile, further optimize and sort the learning paths to generate the best learning path. Then, based on several related nodes in the best learning path, determine the specific time nodes and generate the time sequence plan corresponding to the best learning path.
[0031] When this invention is working, it obtains a set of professional knowledge and skills that need to be further mastered through comparison, which facilitates the planning of the best learning path for students with different learning progress. It also quickly finds feasible paths in a huge heterogeneous graph through a constrained heuristic search method. It can save computing resources while dynamically adjusting the best learning path according to the individual needs of students. Furthermore, it can determine the time nodes based on the time of each learning activity and generate a time sequence plan corresponding to the best learning path, making it easier for students to execute according to the plan and improving operability.
[0032] Preferably, in step D3, when further optimizing and sorting the learning path based on the student profile to generate the best learning path, the following steps are adopted: determining the student's corresponding preference characteristics based on the student profile, assigning personalized weights to the corresponding nodes on the learning path, and further optimizing and sorting the learning path by calculating the method that maximizes the comprehensive node benefit to generate the best learning path.
[0033] As a preferred option, the method also includes the following steps: after the student profile is updated, obtain the deviation items and the degree of deviation before and after the student profile is updated, and dynamically adjust the learning path based on the deviation items and the degree of deviation.
[0034] When this invention is in operation, it can incorporate student preferences while considering the efficiency of skill completion, making the optimized learning path more acceptable and implementable for students. Through weight allocation, it can select the most suitable learning path for students with different individual differences. At the same time, the weight allocation can be updated in real time according to changes in student profiles, ensuring that the optimal learning path always maintains a high degree of match with the student's actual progress and final goal. By calculating deviation items and deviation degrees, subsequent learning paths are adjusted in a targeted manner. Depending on the degree of deviation, only local fine-tuning is needed when small deviations occur, and the learning path is replanned only when large deviations occur. This ensures both path stability and timely response to important changes.
[0035] To solve the above-mentioned technical problems, the second aspect of the present invention adopts the following technical solution: a teaching intelligent management system suitable for integrating coursework, job performance, competition, and certification, applying a teaching intelligent management method suitable for integrating coursework, job performance, competition, and certification as described in any of the preceding aspects, comprising:
[0036] The main control module is responsible for system configuration, overall management, and task scheduling.
[0037] The optimal learning path generation module is used to generate and optimize the output of the best learning path for students based on their profiles.
[0038] The student profile management module is used to create and dynamically update student profiles.
[0039] The heterogeneous graph construction module is used to create heterogeneous graphs that represent the relationships between related course information, related competition information, related professional certificate information, and related job information.
[0040] The data storage module is used to store the collection of student portraits and heterogeneous graphs;
[0041] The interactive module is used to receive input data and display information;
[0042] The optimal learning path generation module, student profile management module, heterogeneous graph construction module, data storage module, and interaction module are all connected to the main control module. After completing the construction of the heterogeneous graph, the heterogeneous graph construction module outputs the heterogeneous graph to the data storage module. After completing the establishment of the student profile, the student profile management module outputs the student profile to the data storage module. After generating and optimizing the optimal learning path for the student based on the heterogeneous graph and the student profile, the optimal learning path generation module outputs the optimal learning path to the interaction module for information display.
[0043] Preferably, the system also includes a data acquisition module, which is connected to several seed source data sources and transmits the data to the heterogeneous graph construction module after comprehensively collecting relevant course information, competition information, professional certificate information, and job information related to the teaching activities.
[0044] The beneficial technical effects of this invention include:
[0045] 1. This invention integrates course learning, competition practice, certificate acquisition, and job placement into a single framework, thereby solving the problem of fragmented links in traditional teaching management. Student profiles are dynamically updated based on multi-dimensional individual information according to learning progress, competitions, certificate acquisition, and other events, accurately reflecting the student's true state. It also enables further optimization of the optimal learning path at different time points, ensuring that the generated optimal learning path always matches the student's actual progress and ultimate goal. This effectively avoids the impact of rigid static paths on students. Furthermore, it allows for the sequential planning of learning activities, competition participation, and certificate acquisition, helping students grasp key milestones, improve learning efficiency and outcome conversion rates, and effectively enhance the scientific nature and automation level of teaching management.
[0046] 2. This invention uses a clustering correction method to specifically correct individuals whose profiles deviate from the average profile of the set by more than a threshold. This can eliminate outliers caused by data collection errors or student self-assessment biases, making student profiles within the same category more stable. This preserves individual differences while avoiding extreme erroneous profiles from affecting subsequent path planning. It also reduces subjective human intervention, lowers teachers' workload, improves the efficiency of student profile creation, and provides sufficiently reliable data references for the generation of the optimal learning path.
[0047] 3. This invention can integrate previously isolated and heterogeneous information such as courses, competitions, professional certificates, and job positions into a unified external information database, laying a reliable data foundation for subsequent correlation analysis. At the same time, by adopting incremental crawling and comprehensive retrieval methods, it can obtain the latest dynamic information such as competition and job requirements and changes in professional certificates in real time or periodically, so that teaching management can always keep up with industry changes. Through key feature indexing and heterogeneous graph structure, it can quickly locate the courses, competitions, professional certificates and corresponding job positions for a certain professional skill, greatly improving the retrieval efficiency of path planning.
[0048] 4. This invention can break down relevant course information into fine-grained professional knowledge and skills, and quantify the professional skill requirements for participating in competitions, obtaining professional certificates, and job positions to achieve a precise match between learning and application. It can also deeply mine the potential connections between various learning activities through heterogeneous graphs, without the need for manual association or AI intelligent judgment, effectively saving human and material resources. By converting the learning path generation into a path search problem on the graph, the algorithm has strong interpretability, making it convenient for students to manually adjust the learning path according to actual needs and facilitating personalized selection.
[0049] 5. This invention can extract implicit soft skills, project experience, teamwork and other implicit abilities from actual work cases, performance appraisals and promotion requirements, thereby further improving the heterogeneous graph and enabling the generated optimal learning path to further guide students to participate in specific types of learning activities, so as to cultivate students to adapt to the implicit requirements of the job in advance, thereby helping students to plan a longer-term ability advancement path.
[0050] 6. This invention obtains a set of professional knowledge and skills that need to be further mastered through comparison, which facilitates the planning of the best learning path for students at different learning progresses. It also quickly finds feasible paths in a huge heterogeneous graph through a constrained heuristic search method. It can save computing resources while dynamically adjusting the best learning path according to the individual needs of students. Furthermore, it can determine the time nodes based on the time of each learning activity and generate a time sequence plan corresponding to the best learning path, making it easier for students to execute according to the plan and improving operability.
[0051] 7. This invention incorporates student preferences while considering the efficiency of skills acquisition, making the optimized learning path more acceptable and implementable for students. Through weighted allocation, it can select the most suitable learning path for students with different individual differences. At the same time, the weighted allocation can be updated in real time according to changes in student profiles, ensuring that the optimal learning path always maintains a high degree of match with the student's actual progress and final goal. By calculating deviation items and deviation degrees, subsequent learning paths are adjusted in a targeted manner. Depending on the degree of deviation, only local fine-tuning is needed when small deviations occur, and the learning path is replanned only when large deviations occur. This ensures both path stability and timely response to important changes.
[0052] Other features and advantages of the present invention will be described in detail in the following detailed description and accompanying drawings. Attached Figure Description
[0053] The invention will be further described below with reference to the accompanying drawings:
[0054] Figure 1 A workflow diagram for an integrated intelligent teaching management method that combines course content, job skills, competition certification, and certification.
[0055] Figure 2 A flowchart for establishing an external information database;
[0056] Figure 3 This is a flowchart of step S2 in a teaching intelligent management method that integrates course, job, competition, and certification.
[0057] Figure 4 This is a schematic diagram of a teaching intelligent management system suitable for integrating coursework, job posting, competition, and certification. Detailed Implementation
[0058] The technical solutions of the embodiments of the present invention will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of the present invention and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of the present invention.
[0059] In the following description, terms such as “inner,” “outer,” “upper,” “lower,” “left,” and “right” are used only to indicate orientation or positional relationship for the convenience of describing the embodiments and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.
[0060] Example 1:
[0061] Please see Figure 1 This embodiment discloses an intelligent management method for integrated teaching of courses, positions, competitions, and certifications, including the following steps:
[0062] S1: The intelligent management system initializes, obtains a set of student profiles containing multi-dimensional individual student information, and connects to a preset external information database containing relevant course information, relevant competition information, relevant professional certificate information, and relevant job information;
[0063] S2: Call an external information database to generate the best learning path for students based on their profiles and preset time points;
[0064] S3: Obtain multi-dimensional individual information of students when they are learning according to the optimal learning path, update the student profile, and proceed to step S2. At the same time, output suggestions for participating in relevant competitions and / or obtaining relevant professional certificates according to the learning progress. When a student participates in a competition, proceed to step S4. When a student obtains a professional certificate, proceed to step S5.
[0065] S4: Obtain multidimensional individual information of students when they participate in the competition, update student profiles, and proceed to step S2;
[0066] S5: Obtain multi-dimensional individual information of students when they take professional certificates, update student profiles, and proceed to step S2.
[0067] This example integrates course learning, competition practice, certificate acquisition, and job placement into a single framework, thus solving the problem of fragmented processes in traditional teaching management. Student profiles are dynamically updated based on multi-dimensional individual information according to learning progress, competitions, and certificate acquisition, accurately reflecting students' true status. It also allows for further optimization of the optimal learning path at different time points, ensuring that the generated optimal learning path always matches the student's actual progress and ultimate goal. This effectively avoids the impact of rigid static paths on students. Furthermore, it enables the sequential planning of learning activities, competition participation, and certificate acquisition, helping students grasp key milestones, improve learning efficiency and outcome conversion rates, and effectively enhance the scientific nature and automation level of teaching management.
[0068] In practice, the student profile is created in step S1 using the following steps:
[0069] A1: Obtain multidimensional individual information about students, including at least one of the following: hard skills, soft skills, preferences, goals, learning stage, completed competitions, and professional certificates obtained. Create a student profile for each student based on this multidimensional information. During the process, hard skills information can be obtained through objective tests and practical exercises, while soft skills, preferences, and goals can be obtained through subjective scales. The learning stage can be uniformly set by the teacher and automatically adjusted based on individual student differences. This approach improves the accuracy of student profiles while maintaining low computational resource consumption, providing reliable data support for subsequent optimal learning path planning.
[0070] A2: The student profiles of several students are clustered and corrected according to preset classification features. Based on the average profile of each set, individual corrections are made to student profiles whose relevant feature deviations exceed a preset threshold. After updating the student profiles, several student profiles are output to obtain a student profile set. In practice, students can be clustered based on their hard skills information, and their soft skills information can be corrected based on the average profile of each set. In practice, generally only positive corrections are made to compensate for the lag in learning path planning caused by low self-assessment, thereby fully leveraging students' learning potential and improving their industry competitiveness.
[0071] In this example, clustering correction is used to specifically correct individuals whose profiles deviate from the average profile of the set by more than a threshold. This eliminates outliers caused by data collection errors or student self-assessment biases, making student profiles within the same category more stable. This preserves individual differences while avoiding extreme erroneous profiles from affecting subsequent path planning. It also reduces subjective human intervention, lowers the workload of teachers, improves the efficiency of student profile creation, and provides sufficiently reliable data references for the generation of optimal learning paths.
[0072] Example 2:
[0073] Please see Figure 2 This embodiment provides an intelligent management method for integrated teaching of courses, positions, competitions, and certifications. The similarities with other embodiments will not be repeated here. The differences will be described in detail below.
[0074] In this embodiment, the establishment of the external information database adopts the following steps:
[0075] B1: Based on course information, competition information, professional certificate information, and job information related to teaching activities, select matching information sources as seed sources. During the work, in addition to the school's own course information, course information can also be obtained from the National Open Course Platform and related online course information according to actual needs. The obtained course information is classified according to professional knowledge and skills, with teaching quality and teaching time as the main index tags. Competition information can be obtained through the National Competition Platform and school notices, with the main index tags including the competition's prestige, competition time, and professional skills requirements. Professional certificate information can be obtained by crawling the official websites of certification institutions and the National Vocational Qualification Directory of the Ministry of Human Resources and Social Security, with the main index tags including exam subjects, time, and industry recognition. Job information can be obtained through various recruitment websites, with the main index tags including professional skills requirements, salary range, and industry type. At the same time, industry development planning information and potential industry development information can also be introduced to avoid students' low competitiveness in the industry due to changes in the employment environment after graduation and the lack of knowledge, thereby improving the employment rate of graduates.
[0076] B2: Perform incremental crawling or comprehensive retrieval of relevant information according to the preset crawling frequency to obtain raw data of relevant course information, relevant competition information, relevant professional certificate information and relevant job information, and clean and standardize the raw data;
[0077] B3: Extract key features from the raw data and store them in a structured manner in an external information database. Add an index to each key feature according to the data type. Ideally, when indexing, it is also necessary to further mark the possible hard requirements of each job position, so as to determine the learning activities that students must complete. Establish a heterogeneous graph to represent the relationship between relevant course information, relevant competition information, relevant professional certificate information and relevant job position information, and complete the establishment of the external information database.
[0078] This example integrates previously isolated and heterogeneous information such as courses, competitions, professional certificates, and job positions into a unified external information database. This lays a reliable data foundation for subsequent correlation analysis. At the same time, by using incremental crawling and comprehensive retrieval, it obtains dynamic information such as the latest competition and job requirements and changes in professional certificates in real time or periodically, ensuring that teaching management keeps up with industry changes. Furthermore, through key feature indexing and heterogeneous graph structures, it can quickly locate courses, competitions, professional certificates, and corresponding job positions for a specific professional skill, greatly improving the retrieval efficiency of path planning.
[0079] Preferably, in step B3, when extracting key features from the original data, storing them in a structured manner in an external information database, and adding an index to each key feature according to the data type to establish a heterogeneous graph representing the relationships between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information, the following steps are adopted:
[0080] C1: After feature extraction from the raw data of relevant course information, several professional knowledge items, several professional skills items, and several mastery levels for each professional skill item are generated. After extracting the raw data of relevant competition information, relevant professional certificate information, and relevant job information, several professional skill requirements are generated, and several mastery level requirements for each professional skill requirement are generated.
[0081] C2: Based on several professional knowledge items, several professional skills and their mastery levels, several professional skill requirements and their mastery level requirements, relevant competition information, relevant professional certificate information, and relevant job information, several nodes are generated in a heterogeneous graph. An index is added to each node based on its relevant information to generate edges connecting related nodes, resulting in a professional knowledge-professional skills-professional skill requirements-learning activity heterogeneous graph representing the relationships between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information. Taking Business Administration as an example, relevant professional knowledge and skills include courses such as Advanced Mathematics, Linear Algebra, Probability Theory and Mathematical Statistics, Principles of Management, Microeconomics, Macroeconomics, Principles of Accounting, Economic Law, Statistics, Marketing, Strategic Management, Innovation Management, Entrepreneurial Management, Corporate Governance, Financial Management, Human Resource Management, and Organizational Behavior. Relevant competitions include the National Enterprise Competition Simulation Competition, which requires students to participate in learning activities. Possessing professional skills in strategic management and operational decision-making, the "Sharp Moment" National Business Simulation Competition requires students to have comprehensive business decision-making and marketing skills, while the National College Student Human Resource Management Comprehensive Ability Competition requires students to have human resource management skills. Relevant professional certificates include Human Resource Administrator, Assistant Human Resource Manager, Human Resource Manager, Senior Human Resource Manager, Marketer, Marketing Specialist, Senior Marketing Specialist, Marketing Specialist, PMP-system Project Manager, and Project Manager managed by the China General Chamber of Commerce. Relevant job positions include marketing, brand planning, and operations specialists in the market sector; recruitment assistants, HRBPs, and compensation specialists in the human resources sector; and data analysts and BI analysts in the data analysis and operations management sector. By establishing an index, explicit connections between related nodes can be achieved. When planning a learning path, the end of the path can cover as many professional skills as possible, thereby improving the adaptability between students and the requirements of various related job positions and facilitating student employment.
[0082] In this example, relevant course information can be broken down into fine-grained professional knowledge and skills. It also quantifies the professional skill requirements for participating in competitions, obtaining professional certificates, and job positions, achieving a precise match between learning and application. Furthermore, it can deeply mine the potential connections between various learning activities through heterogeneous graphs, eliminating the need for manual association and AI-powered intelligent judgment, effectively saving human and material resources. By transforming the generated learning paths into a path search problem on a graph, the algorithm is highly interpretable, allowing students to manually adjust their learning paths according to actual needs and facilitating personalized choices. For example, students can customize their own optimal learning path based on their interests, personality, and other personalized factors, promoting more efficient student growth.
[0083] As a further improvement to this embodiment, step B1 also includes the following step: calling a preset supplementary information source containing actual work cases, performance appraisal standards and job promotion requirements corresponding to the job position as a seed source.
[0084] Step B3 also includes the following steps: extracting implicit key features from the original data containing actual work cases, performance appraisal standards, and job promotion requirements, generating several implicit capability requirements, adding an index to each implicit capability requirement based on the relevant information obtained for each implicit requirement, generating edges with associated nodes, and updating the external information database.
[0085] In this example, implicit soft skills, project experience, teamwork, and other tacit abilities can be extracted from real-world work cases, performance appraisals, and promotion requirements. This further improves the heterogeneous graph and enables the generated optimal learning path to guide students in participating in specific types of learning activities. This helps students adapt to the implicit requirements of their jobs in advance and helps them plan a longer-term ability advancement path.
[0086] Example 3:
[0087] Please see Figure 3 This embodiment provides an intelligent management method for integrated teaching of courses, positions, competitions, and certifications. The similarities with other embodiments will not be repeated here. The differences will be described in detail below.
[0088] In this embodiment, when calling an external information database in step S2 to generate the optimal learning path for students based on their profiles and preset time points, the following steps are taken:
[0089] D1: Determine the target employment direction based on the student profile, analyze the set of professional knowledge and skills mastered by the student at the current learning stage, and call on the target employment direction to call on the external information database to obtain the set of professional knowledge and skills that need to be further mastered;
[0090] D2: Based on the professional knowledge and skills students have acquired at their current learning stage, locate relevant nodes in the heterogeneous graph. Then, based on the further professional knowledge and skills students need to acquire, extract at least one learning path from the heterogeneous graph using a constrained heuristic search. During the process, use the A* algorithm to select several learning paths and simultaneously retain the node set associated with each learning path. Expanding the domain nodes of the master node can comprehensively improve students' professional abilities and provide them with more autonomy. For example, during idle time in the optimal learning path's timeline, low-cost, low-priority auxiliary learning activities can be introduced through parallel insertion, such as temporarily inserting a special lecture while students are participating in a competition. Furthermore, if the master node experiences an anomaly, it can be replaced promptly, avoiding wasting students' time and energy.
[0091] D3: Further optimize and rank the learning paths based on student profiles to generate the optimal learning path. Determine specific time nodes based on several relevant nodes in the optimal learning path and generate a time-series plan corresponding to the optimal learning path. During operation, preliminary node benefit calculations can be performed on several nodes, including hard skill benefits, soft skill benefits, and honor benefits. The comprehensive node benefit of each node can be calculated through weighted fusion based on the preference weights in the student profile. Ideally, node cost calculations can also be performed. By evaluating the time, money, and effort costs required to complete each node, the comprehensive node benefit of each node can be adjusted. Finally, the learning path with the highest comprehensive node benefit is output as the optimal learning path.
[0092] In this example, by comparing and identifying the set of professional knowledge and skills that need to be further mastered, it is convenient to plan the best learning path for students at different learning paces. It also uses a constrained heuristic search method to quickly find feasible paths in a huge heterogeneous graph. This method can save computing resources while dynamically adjusting the best learning path according to the individual needs of students. Furthermore, it can determine the time nodes based on the time of each learning activity and generate a time-series plan corresponding to the best learning path, making it easier for students to execute the plan and improving operability.
[0093] As a further improvement to this embodiment, in step D3, when further optimizing and sorting the learning path according to the student profile to generate the best learning path, the following steps are adopted: determining the student's corresponding preference features according to the student profile, assigning personalized weights to the corresponding nodes on the learning path, and further optimizing and sorting the learning path by calculating the method of maximizing the comprehensive node benefit to generate the best learning path.
[0094] Preferably, the method also includes the following steps: after the student profile is updated, obtain the deviation items and the degree of deviation before and after the student profile is updated, and dynamically adjust the learning path according to the deviation items and the degree of deviation. For example, if there are unexpected factors such as exceptional performance in a competition and obtaining a high ranking or failure to pass a professional certificate exam, the subsequent learning path can be adjusted in a timely manner.
[0095] In this example, student preferences are incorporated while considering the efficiency of skills acquisition, making the optimized learning path more acceptable and implementable for students. Through weighted allocation, the most suitable learning path can be selected for students with different individual differences. The weighted allocation can also be updated in real time according to changes in student profiles, ensuring that the optimal learning path always maintains a high degree of match with the student's actual progress and ultimate goal. By calculating deviation items and the degree of deviation, subsequent learning paths are adjusted in a targeted manner. Depending on the degree of deviation, only local fine-tuning is needed when small deviations occur, and the learning path is replanned only when large deviations occur. This ensures both path stability and timely response to important changes.
[0096] Example 4:
[0097] Please see Figure 4 This embodiment provides an intelligent teaching management system suitable for integrating coursework, job positions, competitions, and certification. It applies any of the above embodiments to a method for intelligent teaching management integrating coursework, job positions, competitions, and certification, including:
[0098] Main control module 1 is responsible for system configuration, overall management, and task scheduling;
[0099] Optimal learning path generation module 2 is used to generate and optimize the output of the best learning path for students based on their profiles.
[0100] Student Profile Management Module 3 is used to create and dynamically update student profiles;
[0101] Heterogeneous graph construction module 4 is used to build heterogeneous graphs that represent the relationships between related course information, related competition information, related professional certificate information, and related job information;
[0102] Data storage module 5 is used to store the collection of student portraits and heterogeneous graphs;
[0103] Interactive module 6 is used to receive input data and display information;
[0104] The optimal learning path generation module 2, student profile management module 3, heterogeneous graph construction module 4, data storage module 5, and interaction module 6 are all connected to the main control module 1. After completing the heterogeneous graph construction, the heterogeneous graph construction module 4 outputs the heterogeneous graph to the data storage module 5. Similarly, after completing the student profile creation, the student profile management module 3 outputs the student profile to the data storage module 5. The optimal learning path generation module 2 generates and optimizes the optimal learning path for each student based on the heterogeneous graph and student profile, and then outputs the optimal learning path to the interaction module 6 for information display. During operation, the responsibilities of each module are clearly defined, and the main control module 1 provides unified scheduling and overall control. This facilitates the development, testing, maintenance, and upgrading of the intelligent teaching management system, and also allows teachers or students to dynamically adjust the generation of the optimal learning path through the interaction module 6.
[0105] Preferably, it also includes a data acquisition module 7, which is connected to several seed source data. After comprehensively collecting relevant course information, competition information, professional certificate information, and job information of teaching activities, it is transmitted to the heterogeneous graph construction module 4. During operation, the module automatically crawls the raw data through the preset crawler scheduling configuration, and performs preprocessing work such as field extraction, unified mapping, and cleaning of the raw data, which can provide reliable data reference for subsequent learning path planning.
[0106] The beneficial technical effects of this embodiment include: the present invention can integrate course learning, competition practice, certificate acquisition, and job employment into the same framework, thereby solving the problem of fragmented links in traditional teaching management. The student profile can be dynamically updated based on multi-dimensional individual information according to learning progress, competitions, certificate acquisition, and other events, which can accurately reflect the student's true status and further optimize the optimal learning path at different time nodes. This ensures that the generated optimal learning path always matches the student's actual progress and final goal, thereby effectively avoiding the impact of rigid static paths on students. At the same time, it can also complete the time sequence planning of learning activities, competition participation, and certificate acquisition, helping students grasp key nodes, improve learning efficiency and achievement conversion rate, and effectively improve the scientificity and automation level of teaching management.
[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes, but is not limited to, the contents described in the accompanying drawings and the specific embodiments above. Any modifications that do not depart from the functional and structural principles of the present invention will be included within the scope of the claims.
Claims
1. A smart management method for integrated teaching of courses, positions, competitions, and certifications, characterized in that: Includes the following steps: S1: The intelligent management system initializes, obtains a set of student profiles containing multi-dimensional individual student information, and connects to a preset external information database containing relevant course information, relevant competition information, relevant professional certificate information, and relevant job information; S2: Call an external information database to generate the best learning path for students based on their profiles and preset time points; S3: Obtain multi-dimensional individual information of students when they are learning according to the optimal learning path, update the student profile, and proceed to step S2. At the same time, output suggestions for participating in relevant competitions and / or obtaining relevant professional certificates according to the learning progress. When a student participates in a competition, proceed to step S4. When a student obtains a professional certificate, proceed to step S5. S4: Obtain multidimensional individual information of students when they participate in the competition, update student profiles, and proceed to step S2; S5: Obtain multi-dimensional individual information of students when they take professional certificates, update student profiles, and proceed to step S2.
2. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 1, characterized in that: In step S1, the student profile is created using the following steps: A1: Obtain multidimensional individual information of students, which includes at least one of the following: hard skills information, soft skills information, preference information, goal information, learning stage, completed competitions, and obtained professional certificates. Create a student profile for each student based on the multidimensional individual information of each student. A2: Cluster and correct the student portraits of several students according to the preset classification features, and make individual corrections to the student portraits in the set whose relevant feature deviation exceeds the preset threshold based on the average portrait of each set. After completing the update of the student portraits, output several student portraits to obtain a set of student portraits.
3. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 1, characterized in that: The establishment of the external information database adopts the following steps: B1: Select matching information sources as seed sources based on course information, competition information, professional certificate information, and job information related to teaching activities; B2: Perform incremental crawling or comprehensive retrieval of relevant information according to the preset crawling frequency to obtain raw data of relevant course information, relevant competition information, relevant professional certificate information and relevant job information, and clean and standardize the raw data; B3: Extract key features from the original data and store them in a structured manner in an external information database. Add an index to each key feature according to the data type, and establish a heterogeneous graph to represent the relationship between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information, thus completing the establishment of the external information database.
4. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 3, characterized in that: In step B3, key features are extracted from the original data and structured and stored in an external information database. An index is added to each key feature according to its data type. When constructing a heterogeneous graph representing the relationships between related course information, related competition information, related professional certificate information, and related job information, the following steps are taken: C1: After feature extraction from the raw data of relevant course information, several professional knowledge items, several professional skills items, and several mastery levels for each professional skill item are generated. After extracting the raw data of relevant competition information, relevant professional certificate information, and relevant job information, several professional skill requirements are generated, and several mastery level requirements for each professional skill requirement are generated. C2: Based on several professional knowledge items, several professional skills and their mastery levels, several professional skill requirements and their mastery level requirements, relevant competition information, relevant professional certificate information, and relevant job information, generate several nodes in the heterogeneous graph, and add an index to each node based on the relevant information of each node to generate edges of related nodes, resulting in a professional knowledge-professional skills-professional skill requirements-learning activity heterogeneous graph used to represent the relationship between relevant course information, relevant competition information, relevant professional certificate information, and relevant job information.
5. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 3, characterized in that: Step B1 also includes the following step: calling a preset supplementary information source containing actual work cases, performance appraisal standards and job promotion requirements corresponding to the job position as a seed source. Step B3 further includes the following steps: extracting implicit key features from the original data containing actual work cases, performance appraisal standards, and job promotion requirements, generating several implicit capability requirements, adding an index to each implicit capability requirement based on the relevant information obtained for each implicit requirement, generating edges with associated nodes, and updating the external information database.
6. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 3, characterized in that: In step S2, when calling an external information database to generate the optimal learning path for students based on their profiles and preset time points, the following steps are taken: D1: Determine the target employment direction based on the student profile, analyze the set of professional knowledge and skills mastered by the student at the current learning stage, and call on the target employment direction to call on the external information database to obtain the set of professional knowledge and skills that need to be further mastered; D2: Based on the set of professional knowledge and skills that students have mastered at the current learning stage, locate the relevant nodes in the heterogeneous graph, and extract at least one learning path in the heterogeneous graph through a constrained heuristic search method based on the set of professional knowledge and skills that students need to further master. D3: Based on the student profile, further optimize and sort the learning paths to generate the best learning path. Then, based on several related nodes in the best learning path, determine the specific time nodes and generate the time sequence plan corresponding to the best learning path.
7. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 6, characterized in that: In step D3, when further optimizing and sorting the learning path based on the student profile to generate the optimal learning path, the following steps are adopted: determining the student's corresponding preference features based on the student profile, assigning personalized weights to the corresponding nodes on the learning path, and further optimizing and sorting the learning path by calculating the method that maximizes the overall node benefit to generate the optimal learning path.
8. The intelligent management method for integrated teaching of courses, positions, competitions, and certifications as described in claim 1, characterized in that: It also includes the following steps: after the student profile is updated, obtain the deviation items and the degree of deviation before and after the student profile is updated, and dynamically adjust the learning path based on the deviation items and the degree of deviation.
9. A teaching intelligent management system suitable for integrating coursework, job performance, competition, and certification, employing the teaching intelligent management method suitable for integrating coursework, job performance, competition, and certification as described in any one of claims 1 to 8, characterized in that, include: The main control module (1) is responsible for system configuration, overall management and task scheduling; The optimal learning path generation module (2) is used to generate and optimize the output of the optimal learning path for students based on their profiles. The student profile management module (3) is used to create and dynamically update student profiles; Heterogeneous graph construction module (4) is used to build a heterogeneous graph that represents the relationship between related course information, related competition information, related professional certificate information and related job information; Data storage module (5) is used to store the student portrait collection and heterogeneous graphs; The interactive module (6) is used to receive input data and display information; The optimal learning path generation module (2), student profile management module (3), heterogeneous graph construction module (4), data storage module (5), and interaction module (6) are all connected to the main control module (1). After completing the construction of the heterogeneous graph, the heterogeneous graph construction module (4) outputs the heterogeneous graph to the data storage module (5). After completing the establishment of the student profile, the student profile management module (3) outputs the student profile to the data storage module (5). After generating and optimizing the optimal learning path for the student based on the heterogeneous graph and the student profile, the optimal learning path generation module (2) outputs the optimal learning path to the interaction module (6) for information display.
10. A teaching intelligent management system suitable for integrating course content, teaching position, competition, and certification, as described in claim 9, characterized in that: It also includes a data acquisition module (7), which is connected to several seed source data and transmits the relevant course information, relevant competition information, relevant professional certificate information and relevant job information of the teaching activities to the heterogeneous graph construction module (4) after comprehensively collecting them.