Method and system for generating vocational ability cultivation scheme for students

Abstract

The invention provides a vocational ability cultivation scheme generation method and system for students, and the method comprises the steps: determining the learning resources of the students based on the specialty of the students, and enabling the learning resources to comprise courses and practice items; learning data in the learning process of the student is acquired, and the learning data comprises learning result data and learning behavior data; on the basis of the learning data, vocational ability portraits of the students are constructed, and the vocational ability portraits comprise post professional ability, vocational basic quality and vocational development potential; according to the learning resources and the vocational ability portrait, generating a vocational ability cultivation scheme of the student, thereby realizing visualization of student ability, enabling the student to clearly understand self learning conditions, and facilitating adjustment of a learning strategy; a teacher can optimize a training scheme according to the vocational ability portrait, and personalized learning environments and conditions are provided for students.

Description

TECHNICAL FIELD

[0001] The present application generally relates to the technical field of smart education. More specifically, the present application relates to a method, system, medium and device for generating a professional ability training plan for a student. BACKGROUND

[0002] In the field of vocational education, the training of skilled personnel faces many challenges. In the prior art, it is difficult to dynamically and accurately assess the ability of students in the training of professional ability, which leads to the fact that students cannot learn in a targeted manner and teachers also have difficulty in dynamically adjusting teaching arrangements. At the same time, students have difficulty in obtaining suitable personalized guidance at any time and any place during the training process, and the existing practice environment cannot meet the training needs of all courses. In addition, learning resources are generally lacking in classification and organization according to learning paths and are not targeted and systematic, which cannot achieve effective and intelligent learning guidance. These problems restrict the training of high-quality professional personnel, so it is urgent to develop an effective and feasible training system to solve the above problems. SUMMARY

[0003] In order to at least solve one or more technical problems as mentioned above, the present application proposes a method, system, medium and device for generating a professional ability training plan for a student in multiple aspects.

[0004] In a first aspect, the present application provides a method for generating a professional ability training plan for a student, comprising the following steps:

[0005] determining learning resources of the student based on a major of the student, wherein the learning resources comprise courses and internship projects;

[0006] obtaining learning data in a learning process of the student, wherein the learning data comprises learning result data and learning behavior data;

[0007] constructing a professional ability portrait of the student based on the learning data, wherein the professional ability portrait comprises post professional ability, professional basic quality and professional development potential;

[0008] generating a professional ability training plan for the student according to the learning resources and the professional ability portrait.

[0009] In some examples, determining the learning resources of the student based on the major of the student comprises:

[0010] determining a type of the student according to an entrance test score and a learning style questionnaire result of the student, wherein the type of the student comprises a basic type, an advanced type and an innovative type;

[0011] determine target capabilities required by the student according to a major of the student, wherein a number of the target capabilities is at least one;

[0012] determine a learning path matched with the target capabilities according to a type of the student and the target capabilities;

[0013] generate learning resources of the student according to the learning path matched with the target capabilities.

[0014] In some examples, constructing the career capability profile of the student based on the learning data comprises:

[0015] analyzing the learning data by using a machine learning model to obtain an analysis result;

[0016] constructing the career capability profile of the student according to the analysis result.

[0017] In some examples, after determining the learning path matched with the target capabilities according to the type of the student and the target capabilities, the method further comprises:

[0018] periodically obtaining current skill requirements of enterprises on employees according to the major of the student;

[0019] judging whether to update the learning path matched with the target capabilities according to the skill requirements.

[0020] In some examples, after generating the learning resources of the student according to the learning path matched with the target capabilities, the method further comprises:

[0021] if it is determined to update the learning path matched with the target capabilities, updating the learning resources of the student according to the updated learning path.

[0022] In some examples, the learning result data comprises daily homework completion, examination scores and practical training scores.

[0023] In some examples, the learning behavior data comprises operation steps, error types and solution times in practical training projects.

[0024] In a second aspect, the application provides a career capability training scheme generation system for students, comprising:

[0025] a determination module configured to determine learning resources of a student based on a major of the student, wherein the learning resources comprise courses and internship projects;

[0026] an obtaining module configured to obtain learning data in a learning process of the student, wherein the learning data comprises learning result data and learning behavior data;

[0027] a construction module configured to construct a professional competence portrait of the student based on the learning data, wherein the professional competence comprises post professional competence, professional basic quality, and professional development potential;

[0028] a generation module configured to generate a professional competence training scheme of the student according to the learning resource and the professional competence portrait.

[0029] In a third aspect, the present application provides a computer readable storage medium containing program instructions, when the program instructions are executed by a processor, the method disclosed in the first aspect is realized.

[0030] In a fourth aspect, the electronic device provided by the present application comprises:

[0031] a processor; and

[0032] a memory storing computer instructions, when the computer instructions are run by the processor, the electronic device executes the method disclosed in the first aspect.

[0033] The professional competence training scheme generation method, system, medium and device for students provided by the present application realize the visualization of student competence by constructing a professional competence portrait, so that students can clearly understand their own learning situation and adjust their learning strategies; teachers can optimize the training scheme according to the professional competence portrait to provide personalized learning environment and conditions for students, effectively improving the learning effect and the quality of professional competence training; enterprises can also more intuitively understand the professional competence of students, improving the scientific nature and efficiency of vocational education. BRIEF DESCRIPTION OF DRAWINGS

[0034] The above and other objects, features and advantages of the present application exemplary embodiments will become more apparent from the following detailed description read in conjunction with the accompanying drawings. In the drawings, several embodiments of the present application are shown by way of example and not limitation, and the same or corresponding reference numbers indicate the same or corresponding parts, in which:

[0035] Figure 1 a professional competence training scheme generation method for students provided by the present application is shown;

[0036] Figure 2 a professional competence training scheme generation system for students provided by the present application is shown;

[0037] Figure 3 a professional competence training scheme generation system for students provided by the present application is shown; DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative work are within the protection scope of the present application.

[0039] It should be understood that the terms “comprising” and “including” used in the specification and claims of the present application indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0040] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and claims of the present application, the singular forms “a”, “an” and “the” are intended to include plural forms unless the context clearly indicates otherwise. It should be further understood that the term “and / or” used in the specification and claims of the present application refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0041] As used in the specification and claims of the present application, the term “if” can be interpreted as “when” or “upon” or “in response to a determination” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be interpreted as meaning “upon determining” or “in response to determining” or “upon detecting [a described condition or event]” or “in response to detecting [a described condition or event]” depending on the context.

[0042] The specific embodiments of the present application will be described in detail below in combination with the drawings.

[0043] Embodiment 1

[0044] As Figure 1 shown, the method for generating a career ability training plan for a student provided by the present application includes the following steps:

[0045] S101, determining learning resources of a student based on a major of the student, wherein the learning resources include courses and internship projects.

[0046] In some examples, step S101 specifically includes:

[0047] determine a type of the student according to the entrance test result and the learning style questionnaire result, wherein the type of the student includes a basic type, an advanced type and an innovative type;

[0048] determine target abilities required by the student according to a major of the student, wherein a number of the target abilities is at least one;

[0049] determine a learning path matched with the target abilities according to the type of the student and the target abilities required by the student;

[0050] generate learning resources of the student according to the learning path matched with the target abilities.

[0051] In some examples, after determining the learning path matched with the target abilities according to the type of the student and the target abilities required by the student, the method further includes:

[0052] periodically acquire a current skill requirement of an enterprise for an employee according to the major of the student;

[0053] determine whether to update the learning path matched with the target abilities according to the skill requirement.

[0054] Specifically, by periodically acquiring the enterprise post requirement and timely adjusting the training scheme, the problem that students are difficult to adapt to the dynamic requirements of the industry is solved, and the pertinence and adaptability of the professional ability training are improved.

[0055] In some examples, after generating the learning resources of the student according to the learning path matched with the target abilities, the method further includes:

[0056] if it is determined to update the learning path matched with the target abilities, update the learning resources of the student according to the updated learning path.

[0057] Specifically, by using stable learning, causal reasoning, geometric deep learning, hierarchical fuzzy clustering and other technologies, virtual simulation laboratories, virtual factories and intelligent learning tools and other learning services that are suitable for the learning path are screened, and the learning path and the learning resources are iteratively optimized based on learning big data.

[0058] S102, acquire learning data in a learning process of the student, wherein the learning data includes learning result data and learning behavior data.

[0059] In some examples, the learning result data includes daily homework completion, examination results and practical training results.

[0060] Specifically, after the learning data is acquired, preprocessing operations such as cleaning, integration and standardization are performed on the learning data.

[0061] In some examples, the learning behavior data includes operation steps in the practical training project, error types, and solution times.

[0062] S103, based on the learning data, constructing a professional competence portrait of the student, wherein the professional competence portrait includes post professional competence, basic professional quality, and professional development potential.

[0063] In some examples, step S103 specifically includes:

[0064] In some examples, based on the learning data, constructing the professional competence portrait of the student includes:

[0065] Using a machine learning model, analyzing the learning data to obtain an analysis result;

[0066] According to the analysis result, constructing the professional competence portrait of the student.

[0067] Specifically, the machine learning model is a Bayesian network model, and the Bayesian network model is used to generate the professional competence portrait of an individual student.

[0068] S104, generating a professional competence training scheme for the student according to the learning resource and the professional competence portrait.

[0069] Specifically, according to the professional competence portrait, the visualization of the student's ability is realized, so that the student can clearly understand his / her own learning situation and adjust the learning strategy; teachers can optimize the training scheme according to the professional competence portrait, provide personalized learning environment and conditions for students, and effectively improve the learning effect and the quality of professional competence training; enterprises can also more intuitively understand the professional competence of students, and improve the scientificity and efficiency of vocational education.

[0070] Embodiment 2

[0071] As shown in Figure 2 The professional competence training scheme generation system for students provided by the present application includes:

[0072] A determination module configured to determine learning resources of a student based on a major of the student, wherein the learning resources include courses and practical training projects;

[0073] An acquisition module configured to acquire learning data in a learning process of the student, wherein the learning data includes learning result data and learning behavior data;

[0074] A construction module configured to construct a professional competence portrait of the student based on the learning data, wherein the professional competence includes post professional competence, basic professional quality, and professional development potential;

[0075] a generating module configured to generate a professional ability training scheme for the student according to the learning resource and the professional ability profile.

[0076] Embodiment 3

[0077] Taking students of a big data major in a higher vocational college as an example, the specific steps of the method for generating a professional ability training scheme for a student provided in the application are described:

[0078] Learning result data such as examination scores, practical training scores and daily homework completion of the students are collected through a learning management system of the school;

[0079] Learning behavior data such as operation steps, error types and solution time of the students in practical training projects such as big data analysis and data visualization are recorded through a virtual simulation experiment platform. At the same time, the skill requirements of big data positions and the latest technical dynamic data of the industry are obtained by connecting with cooperative enterprises;

[0080] Based on the OBE concept, target abilities that the students of the big data major need to have are determined, such as data collection, data processing, data analysis and data visualization;

[0081] The collected data are analyzed by using a machine learning model to determine a learning path from learning of basic statistical knowledge to application of a complex data analysis tool, and the corresponding online courses, case library and virtual simulation training are matched;

[0082] Knowledge points and ability points of the big data major are divided into data preprocessing, data mining algorithm and data visualization units. According to the entrance test scores and learning style questionnaire results of the students, the students are divided into basic type, advanced type and innovative type. Different learning paths are designed for students of different types. For example, basic type students start learning from basic operations of data preprocessing, advanced type students directly enter the learning of data mining algorithm, and innovative type students participate in actual big data project case analysis. A hierarchical fuzzy clustering algorithm is used to filter and match learning resources for each learning path, such as matching Python programming tutorial, machine learning algorithm virtual laboratory and enterprise real data case for the data mining algorithm learning path.

[0083] A machine learning model is used to construct a professional ability profile of an individual student. For example, student A performs outstandingly in data collection ability, but has deficiencies in data analysis ability, and the profile clearly shows that he needs to strengthen the learning of statistical analysis methods and machine learning algorithms. In addition, an integrated clustering method is used to construct a group profile of students of the big data major, and it is found that most students have common problems in the application of data visualization tools, which provides a basis for teachers to adjust the teaching focus.

[0084] According to the student's professional ability image and learning path, personalized learning resources are pushed to students. For example, student A receives data analysis related course recommendations and additional practical projects; in view of the lack of group students in data visualization, the system automatically pushes more visualization tool tutorials and practical cases. At the same time, the demand for new skills of big data positions of cooperative enterprises is regularly obtained, such as the requirement of new big data real-time analysis technology, and the learning path and course recommendation are updated in time.

[0085] In use, students log in to the smart learning platform to obtain personalized learning plans and resource recommendations. The system tracks students' learning progress and behavior data in real time, dynamically adjusts the learning path and recommended content. Teachers can view students' professional ability image and learning through the platform, and provide targeted guidance and teaching optimization. Enterprises can understand the development of students' professional ability through the platform, and participate in the adjustment of the training plan to ensure that talent training and industry demand are synchronized.

[0086] Embodiment 4

[0087] The embodiments of the present application also provide an electronic device, see Figure 3 , Figure 3 is an exemplary block diagram of an electronic device according to an embodiment of the present application, as shown in Figure 3 The electronic device includes a processor and a memory, the memory stores computer instructions, and the processor executes the computer instructions to perform the method provided by the present application.

[0088] Specifically, the processor 601 can include a central processing unit (CPU) or a graphics processing unit (GPU), or an application specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that embody the embodiments of the present application. The memory 602 can include a memory for data or instructions. For example, the memory 602 can be at least one of a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, a universal serial bus (USB) drive, or other physical / tangible memory storage device. Also, for example, the memory 602 includes a removable or non-removable (or fixed) medium. Further, for example, the memory 602 can be internal or external to the integrated gateway disaster recovery device. The memory 602 can be a non-volatile solid-state memory. In other words, generally, the memory 602 includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with executable instructions, where the stored executable instructions, when executed by the processor 601 (such as by one or more processors), implement the methods in the embodiments of the present application.

[0089] In one example, Figure 3 The electronic device shown can also include a communication interface 603 and a bus 610. The processor 601, the memory 602, the communication interface 603 are connected through the bus 610 and complete the communication between each other. The communication interface 603 is mainly used to realize the communication between the modules, devices, units and / or devices in the electronic device. The bus 610 includes hardware, software or both, which can couple the components of the online data flow billing device to each other. For example, the bus can include at least one of an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front side bus (FSB), a hyper transport (HT) interconnect, an industry standard architecture (ISA) bus, an infiniband interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus. The bus 610 can include one or more buses. Although the embodiments of the present application describe or show a specific bus, the embodiments of the present application can consider any suitable bus or interconnection method.

[0090] In another aspect, the embodiments of the present application further provide a computer readable storage medium, having stored thereon computer program instructions, which, when executed by a processor, implement the method described above. The computer readable storage medium is, for example, a classical computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, an electrical, optical, or other physical / tangible storage device.

[0091] In another aspect, the embodiments of the present application further provide a computer program product, comprising computer program instructions, which, when executed by a processor, implement the method provided by the embodiments of the present application. The computer program product is, for example, a software installation package, a plug-in compatible with a related software system, etc.

[0092] The flowcharts and / or block diagrams above describe the flow of the method and system of the embodiments of the present application exemplarily, and describe the related aspects. It should be understood that each block in the flowcharts and / or block diagrams, or a combination thereof, can be implemented by computer program instructions, or by special hardware that performs specified functions or actions, or by a combination of special hardware and computer instructions. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc.; when implemented in software, it is a program or a code segment used to perform the required tasks. The program or code segment can be stored in a memory, or transmitted in a data signal carried in a carrier wave over a transmission medium or a communication link. The code segment can be downloaded via a computer network, such as the Internet, an intranet, etc.

[0093] Although the present application has shown and described several embodiments, it is obvious that such embodiments are provided by way of example only. Numerous changes and modifications can be made by those skilled in the art without departing from the spirit and scope of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be employed. The appended claims are intended to define the scope of the present application and thus cover any equivalents or alternatives within the scope of these claims.

Claims

1. A method for generating a vocational skills training program for students, characterized in that, include: Based on the student's major, the student's learning resources are determined, including courses and internship projects; Acquire learning data during the student's learning process, wherein the learning data includes learning outcome data and learning behavior data; Based on the learning data, a career competency profile of the student is constructed, wherein the career competency profile includes job-specific professional skills, basic professional qualities, and career development potential. Based on the learning resources and the professional competence profile, a professional competence development plan is generated for the student.

2. The method for generating a vocational skills training program according to claim 1, characterized in that, Based on the student's major, the student's learning resources include: Based on the students' entrance test scores and learning style questionnaire results, the types of students are determined, including basic, intermediate, and innovative types. Based on the student's major, determine the target competencies the student needs to possess, wherein the number of target competencies is at least one; Based on the type of student and the target abilities required, determine a learning path that matches the target abilities; Learning resources for the student are generated based on a learning path that matches the target ability.

3. The method for generating a vocational skills training program according to claim 2, characterized in that, Based on the learning data, constructing the student's career competency profile includes: The learning data is analyzed using a machine learning model to obtain analysis results; Based on the analysis results, a professional competence profile of the student is constructed.

4. The method for generating a vocational skills training program according to claim 3, characterized in that, After determining a learning path that matches the target abilities based on the student's type and required target abilities, the method further includes: Based on the students' majors, regularly obtain information on the current skill requirements of companies for their employees; Based on the skill requirements, determine whether to update the learning path that matches the target ability.

5. The method for generating a vocational skills training program according to claim 4, characterized in that, After generating learning resources for the student based on a learning path matching the target ability, the method further includes: If it is determined that the learning path matching the target ability needs to be updated, then the student's learning resources are updated according to the updated learning path.

6. The method for generating a vocational skills training program according to claim 1, characterized in that, The learning outcome data includes daily homework completion status, exam scores, and practical training scores.

7. The method for generating a vocational skills training program according to claim 1, characterized in that, The learning behavior data includes the operation steps, error types, and resolution times in the training projects.

8. A system for generating vocational skills training programs for students, characterized in that, include: The determination module is configured to determine the student's learning resources based on the student's major, wherein the learning resources include courses and internship projects; The acquisition module is configured to acquire learning data during the student's learning process, wherein the learning data includes learning outcome data and learning behavior data; The construction module is configured to construct a professional competence profile of the student based on the learning data, wherein the professional competence includes job-specific skills, basic professional qualities, and career development potential. The generation module is configured to generate a career skills development plan for the student based on the learning resources and the career skills profile.

9. A computer-readable storage medium, characterized in that, It includes program instructions that, when executed by a processor, cause the method according to any one of claims 1-7 to be implemented.

10. An electronic device, characterized in that, include: processor; as well as A memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method according to any one of claims 1-7.

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