Post capability analysis system based on learning condition information

Abstract

The invention relates to the technical field of data processing, in particular to a post capability analysis system based on learning condition information, and the system comprises a score quantification unit which is used for collecting subject scores and subject credits of a user, and carrying out the calculation to obtain a comprehensive skill value and a professional skill value; the modeling unit is used for constructing a learning detection model; the score detection unit is used for collecting detection parameters of the learning detection model completed by the user; the potential analysis unit is used for calculating and generating a comprehensive potential value and calculating and generating a professional potential value; the concentration analysis unit is used for calculating a concentration degree value; the ability analysis unit is used for calculating a generation ability value based on the comprehensive potential value, the professional potential value and the concentration degree value, and generating a learning situation ability model; according to the invention, the above units cooperate with each other, so that the accuracy of the post capability analysis system based on the learning condition information is further improved while the learning state of the student is effectively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a job capability analysis system based on learning information. Background Art

[0002] With the development of the economy and society, the labor market's demand for talent is becoming increasingly diversified and specialized. By analyzing students' academic information to understand their knowledge, skills, and learning characteristics, and then matching this with specific job competency requirements, we can more accurately identify suitable talent for each position. When recruiting, companies often need to select qualified candidates from a large number of resumes and applicants. A job competency analysis system based on academic information can objectively evaluate students' learning potential and focus, helping companies gain a more comprehensive and accurate understanding of applicants' competency levels.

[0003] Chinese patent application publication number: CN119228336A discloses a student job recommendation method and system based on artificial intelligence. The invention provides a student job recommendation method and system based on artificial intelligence, which belongs to the field of artificial intelligence technology. The application obtains students' personal learning course information and corporate recruitment demand information, and at the same time obtains project information of employees in corresponding jobs, constructs a feature extraction model, imports students' personal learning course information, corporate recruitment demand information and work project information of employees in corresponding jobs into the feature extraction model for corresponding feature extraction, constructs a correlation evaluation model, imports the extracted corresponding features into the correlation evaluation model for evaluation, constructs a student job recommendation model, imports students' course comprehensive skill feature vectors, recruitment position comprehensive skill feature vectors and project comprehensive skill feature vectors into the student job recommendation model, generates recommendation results, and provides students with more matching jobs through comprehensive analysis of the courses learned, recruitment positions and skills required for job development.

[0004] Chinese patent application publication number CN113450087A discloses a job competency analysis system and method based on learning information. The system comprises a job competency analysis platform and a client installed on the student's mobile phone or computer. The platform registers the student's job information and assigns learning tasks related to the corresponding job based on the job information. As students progress through their learning tasks, the platform provides in-class quizzes and exams, records learning data from the learning process, and uses this data as the basis for job competency analysis. This basic data is then analyzed to generate a student's job competency analysis report. The client allows students to register and log in to the platform, and once logged in, they can study, take quizzes, and take exams according to their learning tasks. This invention completes the entire process from learning to job match analysis, generating a job competency analysis report that helps companies assign and transfer employees.

[0005] However, the above method has the following problems: it fails to objectively evaluate students' learning potential and learning concentration, and fails to classify, analyze and evaluate students' scores in various subjects according to job requirements. Summary of the Invention

[0006] To this end, the present invention provides a job competency analysis system and method based on learning information to overcome the problems in the prior art that the learning potential and learning concentration of students cannot be objectively evaluated, and the performance of students in various subjects cannot be classified, analyzed and evaluated according to job requirements.

[0007] To achieve the above objectives, the present invention provides a job competency analysis system based on learning information, comprising:

[0008] A performance quantification unit, which is used to collect the subject scores and subject credits of the user's several learning subjects within a preset learning time, and calculate the comprehensive skill value and professional skill value based on the subject scores and corresponding subject credits;

[0009] a modeling unit connected to the performance quantification unit, for constructing a learning detection model based on the comprehensive skill value and the professional skill value;

[0010] A performance detection unit, connected to the modeling unit, is used to collect detection parameters of the user's completion of the learning detection model, wherein the detection parameters include completion time, general subject completion value, professional subject completion value and no-operation time;

[0011] a potential analysis unit, connected to the modeling unit, the performance quantification unit, and the performance detection unit, respectively, for calculating and generating a comprehensive potential value by combining the comprehensive skill value and the general subject completion value, and for calculating and generating a professional potential value by combining the professional skill value and the professional subject completion value;

[0012] a concentration analysis unit connected to the performance detection unit, configured to calculate a concentration value based on the inactivity time and the completion time;

[0013] an ability analysis unit, connected to the potential analysis unit and the focus analysis unit, respectively, for calculating and generating an ability value based on the comprehensive potential value, the professional potential value, and the focus value, and combining the ability value with the comprehensive skill value and the professional skill value to generate a learning ability model;

[0014] The preset learning time is positively correlated with the user's total learning time.

[0015] Furthermore, the performance quantification unit includes:

[0016] A performance building sub-unit is used to identify the professional skills and general skills of a position, and to divide the collected learning subjects into a number of professional subjects and a number of general subjects based on the professional skills and the general skills;

[0017] The professional skills are the professional skills and knowledge required to ensure the normal operation of the position, and the general skills are the auxiliary skills and knowledge required to complete the process of learning professional skills and knowledge.

[0018] Furthermore, the performance quantification unit further includes:

[0019] A general subject subunit, which is connected to the score building subunit, is used to calculate and generate the comprehensive skill value based on the general subject scores and corresponding general subject credits of several general subjects;

[0020] A professional subject subunit, which is connected to the score building subunit, is used to calculate and generate the professional skill value based on the professional subject scores and corresponding professional subject credits of several professional subjects;

[0021] The subject scores include the general subject scores and the professional subject scores, and the subject credits include the general subject credits and the professional subject credits.

[0022] Furthermore, the modeling unit includes:

[0023] A dimension subunit, which is connected to the general subject subunit and the professional subject subunit respectively, and is used to calculate the position association value of each general subject and each professional subject with the position based on the semantic model, and calculate the learning dimension value by combining the position association value and the association weight;

[0024] A construction subunit is connected to the dimension subunit, and is used to select several experimental questions based on the comprehensive skill value and the professional skill value to construct the learning detection model, and to set experimental credits for several of the experimental questions based on each of the learning dimension values.

[0025] Furthermore, the performance detection unit includes:

[0026] The time detection subunit is connected to the modeling unit and is used to collect the completion time of the user to complete the learning detection model, and record a number of times without information input and compare them with the preset no-input time, and generate the no-operation time according to the comparison results. The preset no-input time is positively correlated with the total number of experimental questions included in the learning detection model.

[0027] Furthermore, the performance detection unit further includes:

[0028] The completion detection subunit is connected to the time detection unit and is used to divide the experimental questions in the learning detection model into general subject questions and professional subject questions based on keywords, and to record the completion values ​​of the general subject questions and the professional subject questions respectively.

[0029] Furthermore, the potential analysis unit includes:

[0030] a comprehensive potential subunit, connected to the performance quantification unit and the completion detection subunit, respectively, for calculating a general subject skill value based on each of the general subject completion values ​​and the corresponding experimental credits, and comparing the comprehensive skill value with the general subject skill value to calculate and generate the comprehensive potential value;

[0031] The professional potential sub-unit is connected to the performance quantification unit and the completion detection sub-unit respectively, and is used to calculate the professional subject skill value based on the completion value of each professional subject and the corresponding experimental credits, and compare the professional skill value with the professional subject skill value to calculate and generate the professional potential value.

[0032] Furthermore, the concentration analysis unit is connected to the performance detection unit to calculate the ratio of the no-operation time to the completion time to generate the concentration value, compare the concentration value with the preset concentration value, and determine whether the user is an active user based on the comparison result. The preset concentration value is positively correlated with the total number of experimental questions included in the learning detection model.

[0033] Furthermore, the capability analysis unit includes:

[0034] The capability calculation subunit is respectively connected to the modeling unit, the potential analysis unit and the focus analysis unit, and is used to determine the weighted values ​​of the comprehensive potential value, the professional potential value and the focus value according to the position association value, and generate the capability value based on the weighted value calculation.

[0035] Furthermore, the capability analysis unit further includes:

[0036] The ability storage subunit is respectively connected to the potential analysis unit and the focus analysis unit, and is used to generate and store the learning situation and ability model to increase the preset learning time of the next user or to call the learning detection model corresponding to the learning situation and ability model.

[0037] Compared with the existing technology, the beneficial effect of the present invention is that the present invention classifies the subjects learned by students by clarifying the professional skills and general skills required for the position, and generates comprehensive skill values ​​and professional skill values ​​for evaluating students' grades based on the classification calculation, which can effectively evaluate the students' past learning status and analyze the students' suitability for the positions they are applying for. The calculation of comprehensive skill values ​​and professional skill values ​​takes into account the students' performance in different subjects and the degree of correlation between these subjects and job skills. This makes the evaluation of students' learning status no longer limited to traditional grade rankings, but more comprehensively covers the students' development in professional skills and general skills. By classifying the subjects learned and the job skills, it is possible to accurately find out Students' strengths and weaknesses in professional skills and general skills can be used in the recruitment process to refer to the students' comprehensive skill values ​​and professional skill values ​​based on the professional skills and general skills required for the position, so as to more accurately screen out candidates with high suitability for the position, improve the efficiency and accuracy of recruitment, and reduce recruitment errors caused by information mismatch. This classification and evaluation method helps to strengthen the close connection between education and the employment market. Schools can optimize course settings and teaching content based on corporate job requirements and students' skill evaluation results. At the same time, students can also clarify their career direction during the learning process, better realize the transition from school to the workplace, improve employment quality and employment stability, and effectively improve the accuracy of the job ability analysis system based on academic information.

[0038] Furthermore, the present invention constructs a learning detection model based on the correlation between the subjects studied by students and the positions and the evaluation value of their learning performance. By detecting various parameters when students use the learning detection model, the learning ability of students is objectively evaluated. The evaluation is no longer based solely on the level of grades, but comprehensively considers the degree of fit between the knowledge learned by students and their future career direction. Due to the introduction of correlation, the model can more prominently evaluate the learning ability related to key skills of the position. Based on the evaluation results of the model, students can more accurately understand the gap between themselves and the target position, thereby making more informed decisions in their career development planning. During the job search process, students can use the evaluation results of the learning detection model to show employers their learning ability and advantages in areas related to the position. Compared with a simple transcript, this evaluation result based on the position correlation can more intuitively reflect the student's adaptability to the target position, increase the student's competitiveness in the job search process, and improve the chance of obtaining an ideal job. When recruiting, companies can more accurately screen out talents that meet the job requirements by referring to the evaluation results of the learning detection model, reduce information asymmetry in the recruitment process, and further improve the accuracy of the job ability analysis system based on academic information.

[0039] Furthermore, the present invention objectively evaluates students' learning potential and learning concentration by detecting various parameters when students use the learning detection model. Based on the students' learning potential and learning concentration, the present invention has some understanding of the students' learning habits, and can determine whether the students are focusing on personal research or like to participate in communication and discussion. The parameters in the learning detection model can comprehensively reflect the students' various ability performances in the learning process. Through the analysis of these parameters, the students' learning potential can be discovered more accurately. Learning concentration is an important factor affecting learning outcomes. By detecting the parameters of the learning detection model, the students' concentration level in the learning process can be accurately evaluated. Enterprises can conduct targeted screening of talents according to the job requirements for concentration, further improving the accuracy of the job capability analysis system based on learning information.

[0040] Furthermore, the present invention objectively evaluates the learning ability of students based on the correlation between each subject and the position, combined with the students' potential for learning improvement in professional subjects and comprehensive general subjects, and stores the students' grades and ability values ​​in correspondence, so as to provide targeted guidance for constructing a learning detection model for the next student with the same grade. By comprehensively considering the correlation between subjects and positions and the students' potential for learning improvement in professional subjects and general subjects, the present invention can make a more comprehensive and objective evaluation of students' learning ability. It is no longer simply judging a student by his or her grades, but from the perspective of job requirements, it measures whether the student has the ability to adapt to future career development. The students' grades and ability values ​​are stored in correspondence, which provides rich data support for building a more scientific and reasonable learning detection model. By analyzing a large amount of student data, the ability characteristics and development trends of students at different grade levels can be discovered, thereby optimizing and improving the learning detection model. Evaluating learning ability based on the correlation between subjects and positions helps students better adapt to future career development. During the learning process, students can clearly understand the connection between the knowledge they have learned and the target position, and improve their job-related abilities in a more targeted manner, further improving the accuracy of the job ability analysis system based on learning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a structural diagram of the job capability analysis system based on learning information of the present invention;

[0042] Figure 2 This is a structural block diagram of a performance quantification unit according to an embodiment of the present invention;

[0043] Figure 3 This is a determination diagram of the no-operation time according to an embodiment of the present invention;

[0044] Figure 4 This is a determination diagram of active users in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0049] See also Figure 1 As shown in FIG, which is a structural block diagram of a job capability analysis system based on learning information of the present invention, an embodiment of the present invention provides a job capability analysis system based on learning information, including:

[0050] A performance quantification unit is used to collect the subject scores and subject credits of several learning subjects of the user within the preset learning time, and to calculate the comprehensive skill value and professional skill value based on the subject scores and corresponding subject credits;

[0051] A modeling unit, which is connected to the performance quantification unit and is used to build a learning detection model based on the comprehensive skill value and the professional skill value;

[0052] The performance detection unit is connected to the modeling unit and is used to collect detection parameters of the user's completed learning detection model, including completion time, general subject completion value, professional subject completion value and no-operation time;

[0053] A potential analysis unit, which is connected to the modeling unit, the performance quantification unit, and the performance detection unit, and is used to combine the comprehensive skill value and the general subject completion value to calculate and generate the comprehensive potential value, and to combine the professional skill value and the professional subject completion value to calculate and generate the professional potential value;

[0054] a concentration analysis unit connected to the performance detection unit for calculating a concentration value based on the inactivity time and the completion time;

[0055] The ability analysis unit is connected to the potential analysis unit and the focus analysis unit respectively, and is used to calculate and generate the ability value based on the comprehensive potential value, professional potential value and focus value, and to combine the ability value with the comprehensive skill value and professional skill value to generate a learning ability model;

[0056] The preset learning time is positively correlated with the user's total learning time.

[0057] It is understandable that the longer the user's total learning time is, the more subjects the user has learned. In order to comprehensively evaluate the knowledge learned by the user and increase the collection time, the preset learning time is positively correlated with the user's total learning time.

[0058] Optionally, the user's total learning time is 16 years and the preset learning time is 3 years;

[0059] The total learning time for users is 19 years, and the preset learning time is 4 years;

[0060] The total learning time for users is 22 years, and the preset learning time is 5 years.

[0061] See also Figure 2 As shown in FIG, it is a structural block diagram of a performance quantification unit according to an embodiment of the present invention. The performance quantification unit includes:

[0062] The achievement building sub-unit is used to clarify the professional skills and general skills of the position, and the collected learning subjects are divided into several professional subjects and several general subjects based on professional skills and general skills;

[0063] Professional skills are the professional skills and knowledge required to ensure the normal operation of the position, and general skills are the auxiliary skills and knowledge required to complete the process of learning professional skills and knowledge.

[0064] During implementation, for AI engineer positions, the first step is to clarify job skills. Professional skills include machine learning algorithms, deep learning frameworks, data mining and analysis, natural language processing, or computer vision. General skills include teamwork, technical documentation, cross-departmental communication, and basic project management. The "Machine Learning" course systematically explains the principles of various classic machine learning algorithms, model training, and optimization methods, and is the foundation for mastering the core technologies of AI. The "Deep Learning" course focuses on deep neural network structures, training techniques, and applications in areas such as image and speech, and is closely related to the professional skills of deep learning frameworks in the position. The "Data Mining and Analysis" course teaches data preprocessing, feature extraction, association rule mining, and other technologies, providing data processing support for AI applications. These are all professional subjects. "College Chinese" helps improve language expression and technical documentation writing skills. The "Team Building and Management" course cultivates teamwork and communication skills, helping to collaborate efficiently with team members at work. "Computer Basics" can strengthen basic computer operations and system cognition, and is a general guarantee for carrying out subsequent professional work. These are all general subjects.

[0065] Specifically, the performance quantification unit also includes:

[0066] The general subject sub-unit is connected to the score building sub-unit and is used to calculate and generate a comprehensive skill value based on the general subject scores and corresponding general subject credits of several general subjects;

[0067] The professional subject sub-unit is connected to the score building sub-unit and is used to calculate and generate professional skill values ​​based on the professional subject scores and corresponding professional subject credits of several professional subjects;

[0068] Subject grades include general subject grades and professional subject grades, and subject credits include general subject credits and professional subject credits.

[0069] In implementation,

[0070]

[0071] Specifically, the present invention classifies the subjects learned by students by clarifying the professional skills and general skills required for the position, and generates comprehensive skill values ​​and professional skill values ​​for evaluating students' grades based on the classification calculation. It can effectively evaluate students' past learning status and analyze students' suitability for the positions they are applying for. The calculation of comprehensive skill values ​​and professional skill values ​​takes into account students' performance in different subjects and the degree of correlation between these subjects and job skills. This makes the evaluation of students' learning status no longer limited to traditional grade rankings, but more comprehensively covers the development of students in professional skills and general skills. By classifying the subjects learned and the job skills, it is possible to accurately find out students' professional skills. By considering the advantages and disadvantages of students' comprehensive skill values ​​and professional skill values ​​according to the professional and general skills required for the position in the recruitment process, candidates with high suitability for the position can be screened out more accurately, which improves the efficiency and accuracy of recruitment and reduces recruitment errors caused by information mismatch. This classification and evaluation method helps to strengthen the close connection between education and the employment market. Schools can optimize course settings and teaching content based on corporate job requirements and students' skill evaluation results. At the same time, students can also clarify their career direction during the learning process, better realize the transition from school to the workplace, improve employment quality and employment stability, and effectively improve the accuracy of the job ability analysis system based on academic information.

[0072] Specifically, the modeling units include:

[0073] The dimension subunit is connected to the general subject subunit and the professional subject subunit respectively, and is used to calculate the job association value of each general subject and each professional subject with the job based on the semantic model, and to calculate the learning dimension value by combining the job association value and the association weight;

[0074] The construction subunit is connected to the dimension subunit and is used to select a number of experimental questions based on the comprehensive skill value and professional skill value to construct a learning detection model, and to set experimental credits for a number of experimental questions based on the value of each learning dimension.

[0075] It can be understood that keywords are extracted from the job requirement text, the teaching syllabus of each general subject, and the teaching syllabus of each professional subject respectively, and the keywords of each general subject and each professional subject are compared with the keywords of the job requirements respectively, and the number of identical keywords is recorded as the job association value.

[0076] It can be understood that the sum of the associated weights of all general subjects and professional subjects is 1. Optionally, the sum of the associated weights of all general subjects is 0.3, and the associated weight of each general subject is 0.3 divided by the number of general subjects. The sum of the associated weights of all professional subjects is 0.7, and the associated weight of each professional subject is 0.7 divided by the number of professional subjects.

[0077] It can be understood that multiplying the association weight of each general subject by the corresponding job association value can obtain the learning dimension value of each general subject; multiplying the association weight of each professional subject by the corresponding job association value can obtain the learning dimension value of each professional subject.

[0078] It can be understood that based on the ratio of comprehensive skill value to professional skill value, a number of experimental questions of equal proportion are selected to construct a learning detection model.

[0079] It is understandable that the learning dimension value corresponding to each subject is set to the experimental credits of the corresponding experimental test questions.

[0080] See also Figure 3 As shown in FIG. 1 , which is a determination diagram of the no-operation time according to an embodiment of the present invention, the performance detection unit includes:

[0081] The time detection subunit is connected to the modeling unit and is used to collect the completion time of the user to complete the learning detection model, and record several times of no information input and compare them with the preset no-input time. The no-operation time is statistically generated based on the comparison results. The preset no-input time is positively correlated with the total number of experimental questions contained in the learning detection model.

[0082] If the no-input time is greater than or equal to the preset no-input time, the no-input time is determined to be the no-operation time;

[0083] If the time without information input is less than the preset time without input, the time without information input is determined to be thinking time;

[0084] In a specific embodiment, the preset no-input time is set to 60 seconds. If the no-input time is 76 seconds, which is greater than the preset no-input time, the no-input time is determined to be the no-operation time.

[0085] If the time without information input is 45 seconds, which is less than the preset time without input, the time without information input is considered as thinking time;

[0086] It is understandable that the more experimental questions the learning detection model contains, the longer the student's examination time will be, and the longer the time required to respond to and think about the questions will be. Therefore, the preset no-input time is positively correlated with the total number of experimental questions contained in the learning detection model.

[0087] Optionally, the total number of experimental questions included in the learning detection model is 100, and the preset no-input time is 60 seconds;

[0088] The total number of experimental questions included in the learning detection model is 150, and the preset no-input time is 70 seconds;

[0089] The total number of experimental questions included in the learning detection model is 200, and the preset no-input time is 80 seconds.

[0090] Specifically, the performance testing unit also includes:

[0091] The completion detection subunit is connected to the time detection unit and is used to divide the experimental questions in the learning detection model into general subject questions and professional subject questions based on keywords, and to record the completion values ​​of general subject questions and professional subject questions respectively.

[0092] It can be understood that the completion value of general subject test questions and professional subject test questions is based on the keyword comparison between the student's answer to the question and the model answer, and the completion value is obtained by multiplying the ratio of the number of identical keywords by the question score.

[0093] Specifically, the present invention constructs a learning detection model based on the correlation between the subjects studied by students and the positions and the evaluation value of their learning performance. The learning ability of students is objectively evaluated by detecting various parameters when students use the learning detection model. The evaluation is no longer based solely on the level of grades, but comprehensively considers the degree of fit between the knowledge learned by students and their future career direction. Due to the introduction of correlation, the model can more prominently evaluate the learning ability related to key skills of the position. Based on the evaluation results of the model, students can more accurately understand the gap between themselves and the target position, thereby making more informed decisions in their career development planning. During the job search process, students can use the evaluation results of the learning detection model to show employers their learning ability and advantages in areas related to the position. Compared with a simple transcript, this evaluation result based on the position correlation can more intuitively reflect the student's adaptability to the target position, increase students' competitiveness in the job search process, and improve their chances of obtaining an ideal job. When recruiting, companies can more accurately screen out talents that meet the job requirements by referring to the evaluation results of the learning detection model, reduce information asymmetry in the recruitment process, and further improve the accuracy of the job ability analysis system based on academic information.

[0094] Specifically, the potential analysis unit includes:

[0095] The comprehensive potential sub-unit is connected to the performance quantification unit and the completion detection sub-unit respectively. It is used to calculate the general subject skill value based on the completion value of each general subject and the corresponding experimental credits, and compare the comprehensive skill value with the general subject skill value to calculate the comprehensive potential value;

[0096] The professional potential sub-unit is connected to the performance quantification unit and the completion detection sub-unit respectively, and is used to calculate the professional subject skill value based on the completion value of each professional subject and the corresponding experimental credits, and compare the professional skill value with the professional subject skill value to calculate and generate the professional potential value.

[0097] In implementation,

[0098]

[0099] In practice, comprehensive potential value = (general subject skill value - comprehensive skill value) / comprehensive skill value;

[0100] Professional potential value = (professional subject skill value - professional skill value) / professional skill value.

[0101] See also Figure 4As shown, it is a determination diagram of active users in an embodiment of the present invention. The focus analysis unit is connected to the performance detection unit to calculate the ratio of the no-operation time to the completion time to generate a focus value, compare the focus value with the preset focus value, and determine whether the user is an active user based on the comparison result. The preset focus value is positively correlated with the total number of experimental questions included in the learning detection model.

[0102] If the concentration value is greater than or equal to the preset concentration value, the user is determined to be an active user;

[0103] If the concentration value is less than the preset concentration value, the user is determined to be a focused user;

[0104] In a specific embodiment, the preset concentration value is set to 0.3, and if the concentration value is 0.47 and is greater than the preset concentration value, the user is determined to be an active user;

[0105] If the concentration value is 0.24, which is less than the preset concentration value, the user is determined to be a focused user.

[0106] It is understandable that the more experimental questions the learning detection model contains, the more the students' concentration in answering the questions decreases. Therefore, the preset concentration value is positively correlated with the total number of experimental questions contained in the learning detection model.

[0107] Optionally, the total number of experimental questions included in the learning detection model is 100, and the preset concentration value is 0.1;

[0108] The total number of experimental questions included in the learning detection model is 150, and the preset concentration value is 0.2;

[0109] The total number of experimental questions included in the learning detection model is 200, and the preset concentration value is 0.3.

[0110] Specifically, the present invention objectively evaluates students' learning potential and learning concentration by detecting various parameters when students use the learning detection model. Based on the students' learning potential and learning concentration, the present invention has some understanding of the students' learning habits, and can determine whether the students are focusing on personal research or like to participate in communication and discussion. The parameters in the learning detection model can comprehensively reflect the students' various ability performances in the learning process. By analyzing these parameters, the students' learning potential can be discovered more accurately. Learning concentration is an important factor affecting learning outcomes. By detecting the parameters of the learning detection model, the students' concentration level in the learning process can be accurately evaluated. Enterprises can conduct targeted screening of talents according to the job requirements for concentration, further improving the accuracy of the job ability analysis system based on learning information.

[0111] Specifically, the capability analysis unit includes:

[0112] The capability calculation subunit is connected to the modeling unit, potential analysis unit and focus analysis unit respectively, and is used to determine the weighted values ​​of the comprehensive potential value, professional potential value and focus value according to the position-related values, and generate the capability value based on the weighted value calculation.

[0113] It can be understood that the sum of the weighted values ​​of the comprehensive potential value, professional potential value and concentration value is 1. Optionally, the sum of the weighted values ​​of the comprehensive potential value and professional potential value is 0.6, and the weighted value of the concentration value is 0.4. A weighted value of 0.6 is assigned according to the ratio of the job-related values ​​corresponding to the comprehensive potential value and professional potential value respectively.

[0114] It can be understood that the comprehensive potential value, professional potential value and concentration value are multiplied by the corresponding weighted values ​​respectively, and the products are added together to obtain the ability value.

[0115] Specifically, the capability analysis unit also includes:

[0116] The ability storage sub-unit is connected to the potential analysis unit and the focus analysis unit respectively, and is used to generate and store the learning ability model to increase the preset learning time of the next user or call the learning detection model corresponding to the learning ability model.

[0117] It can be understood that the ability values ​​corresponding to the comprehensive skill values ​​and professional skill values ​​are recorded to generate a learning situation and ability model. If the ability value is less than or equal to the preset ability value, it is determined to increase the preset learning time. If the ability value is greater than the preset ability value, it is determined to keep the preset learning time unchanged. When encountering the next student with the same comprehensive skill value and professional skill value, the learning detection model corresponding to the learning situation and ability model is called up.

[0118] It can be understood that the preset ability value is positively correlated with the sum of the comprehensive skill value and the professional skill value.

[0119] It can be understood that the greater the sum of the comprehensive skill value and the professional skill value, the stronger the student's learning ability and the greater the corresponding ability value. Therefore, the preset ability value is positively correlated with the sum of the comprehensive skill value and the professional skill value.

[0120] Specifically, the present invention objectively evaluates the learning ability of students based on the correlation between each subject and the position, combined with the students' potential for learning improvement in professional subjects and comprehensive general subjects, and stores the students' grades and ability values ​​in correspondence. Targeted guidance is used to construct a learning detection model for the next student with the same grade. By comprehensively considering the correlation between subjects and positions and the students' potential for learning improvement in professional subjects and general subjects, a more comprehensive and objective evaluation of students' learning ability can be made. Instead of simply judging a student's ability based on their grades, the evaluation is made from the perspective of job requirements to measure whether the student has the ability to adapt to future career development. The corresponding storage of students' grades and ability values ​​provides rich data support for building a more scientific and reasonable learning detection model. By analyzing a large amount of student data, the ability characteristics and development trends of students at different grade levels can be discovered, thereby optimizing and improving the learning detection model. Evaluating learning ability based on the correlation between subjects and positions helps students better adapt to future career development. During the learning process, students can clearly understand the connection between the knowledge they have learned and the target position, and more specifically improve their job-related abilities, further improving the accuracy of the job ability analysis system based on learning information.

[0121] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0122] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A job competency analysis system based on learning information, characterized in that: include: A performance quantification unit, which is used to collect the subject scores and subject credits of the user's several learning subjects within a preset learning time, and calculate the comprehensive skill value and professional skill value based on the subject scores and corresponding subject credits; a modeling unit connected to the performance quantification unit, for constructing a learning detection model based on the comprehensive skill value and the professional skill value; A performance detection unit, connected to the modeling unit, is used to collect detection parameters of the user's completion of the learning detection model, wherein the detection parameters include completion time, general subject completion value, professional subject completion value and no-operation time; a potential analysis unit, connected to the modeling unit, the performance quantification unit, and the performance detection unit, respectively, for calculating and generating a comprehensive potential value by combining the comprehensive skill value and the general subject completion value, and for calculating and generating a professional potential value by combining the professional skill value and the professional subject completion value; a concentration analysis unit connected to the performance detection unit, configured to calculate a concentration value based on the inactivity time and the completion time; an ability analysis unit, connected to the potential analysis unit and the focus analysis unit, respectively, for calculating and generating an ability value based on the comprehensive potential value, the professional potential value, and the focus value, and combining the ability value with the comprehensive skill value and the professional skill value to generate a learning ability model; The preset learning time is positively correlated with the user's total learning time.

2. The job competency analysis system based on learning information according to claim 1 is characterized in that: The performance quantification unit includes: A performance building sub-unit is used to identify the professional skills and general skills of a position, and to divide the collected learning subjects into a number of professional subjects and a number of general subjects based on the professional skills and the general skills; The professional skills are the professional skills and knowledge required to ensure the normal operation of the position, and the general skills are the auxiliary skills and knowledge required to complete the process of learning professional skills and knowledge.

3. The job competency analysis system based on learning information according to claim 2 is characterized in that: The performance quantification unit also includes: A general subject subunit, which is connected to the score building subunit, is used to calculate and generate the comprehensive skill value based on the general subject scores and corresponding general subject credits of several general subjects; A professional subject subunit, which is connected to the score building subunit, is used to calculate and generate the professional skill value based on the professional subject scores and corresponding professional subject credits of several professional subjects; The subject scores include the general subject scores and the professional subject scores, and the subject credits include the general subject credits and the professional subject credits.

4. The job competency analysis system based on learning information according to claim 3 is characterized in that: The modeling unit includes: A dimension subunit, which is connected to the general subject subunit and the professional subject subunit respectively, and is used to calculate the position association value of each general subject and each professional subject with the position based on the semantic model, and calculate the learning dimension value by combining the position association value and the association weight; A construction subunit is connected to the dimension subunit, and is used to select several experimental questions based on the comprehensive skill value and the professional skill value to construct the learning detection model, and to set experimental credits for several of the experimental questions based on each of the learning dimension values.

5. The job competency analysis system based on learning information according to claim 4 is characterized in that: The performance detection unit comprises: The time detection subunit is connected to the modeling unit and is used to collect the completion time of the user to complete the learning detection model, and record a number of times without information input and compare them with the preset no-input time, and generate the no-operation time according to the comparison results. The preset no-input time is positively correlated with the total number of experimental questions included in the learning detection model.

6. The job competency analysis system based on learning information according to claim 5 is characterized in that: The performance detection unit also includes: The completion detection subunit is connected to the time detection unit and is used to divide the experimental questions in the learning detection model into general subject questions and professional subject questions based on keywords, and to record the completion values ​​of the general subject questions and the professional subject questions respectively.

7. The job competency analysis system based on learning information according to claim 6 is characterized in that: The potential analysis unit includes: a comprehensive potential subunit, connected to the performance quantification unit and the completion detection subunit, respectively, for calculating a general subject skill value based on each of the general subject completion values ​​and the corresponding experimental credits, and comparing the comprehensive skill value with the general subject skill value to calculate and generate the comprehensive potential value; The professional potential sub-unit is connected to the performance quantification unit and the completion detection sub-unit respectively, and is used to calculate the professional subject skill value based on the completion value of each professional subject and the corresponding experimental credits, and compare the professional skill value with the professional subject skill value to calculate and generate the professional potential value.

8. The job competency analysis system based on learning information according to claim 7 is characterized in that: The concentration analysis unit is connected to the performance detection unit and is used to calculate the ratio of the no-operation time to the completion time to generate the concentration value, compare the concentration value with the preset concentration value, and determine whether the user is an active user based on the comparison result. The preset concentration value is positively correlated with the total number of experimental questions included in the learning detection model.

9. The job competency analysis system based on learning information according to claim 8 is characterized in that: The capability analysis unit includes: The capability calculation subunit is respectively connected to the modeling unit, the potential analysis unit and the focus analysis unit, and is used to determine the weighted values ​​of the comprehensive potential value, the professional potential value and the focus value according to the position association value, and generate the capability value based on the weighted value calculation.

10. The job competency analysis system based on learning information according to claim 9 is characterized in that: The capability analysis unit further includes: The ability storage subunit is respectively connected to the potential analysis unit and the focus analysis unit, and is used to generate and store the learning situation and ability model to increase the preset learning time of the next user or to call the learning detection model corresponding to the learning situation and ability model.

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