Big data analysis-based university student personalized occupational planning system

Abstract

The invention discloses a university student personalized career planning system based on big data analysis, and belongs to the technical field of education assistance and career planning. Comprising a data acquisition module, an occupational information database module, a data analysis module, a personalized occupational planning module and a user interaction interface, the data acquisition module is used for acquiring personal basic information, academic performance data, hobbies and interests data and campus activity data of college students; the occupational information database module is used for storing occupational classification and introduction information, updating industry dynamic data and establishing an occupational skill requirement database; the data analysis module is used for preprocessing the data acquired by the data acquisition module, analyzing the correlation between the preprocessed data and the data stored in the occupational information database module, and performing clustering analysis on the college student group; and the personalized occupational planning module recommends occupations to the college students through an occupational matching algorithm based on the analysis result of the data analysis module, and customizes a personalized occupational planning scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of educational assistance and career planning, and in particular to a personalized career planning system for college students based on big data analysis. Background Art

[0002] With the widespread adoption of higher education, a large number of college students face employment decisions each year. However, traditional career planning guidance often lacks specificity and fails to meet the diverse needs of college students. On the one hand, college students themselves have limited knowledge of the professional world and are unclear about the matching of their interests, abilities, and careers. On the other hand, career information provided by schools and society is fragmented and lacks systematic integration, leading to confusion in the career planning process. Existing career planning systems are mostly generalized and fail to fully consider the unique characteristics of college students, such as their stage of study, professional background, and campus activities, which influence career planning. In this era of information explosion, how to efficiently integrate massive amounts of career information and combine it with individual college students' data to provide them with accurate and personalized career planning advice has become a pressing issue. Summary of the Invention

[0003] The purpose of the present invention is to provide a personalized career planning system for college students based on big data analysis to solve the problems raised in the above background technology.

[0004] In view of the above problems, the technical solution proposed by the present invention is: A personalized career planning system for college students based on big data analysis, including data collection module, career information database module, data analysis module, personalized career planning module, and user interaction interface; The data collection module is used to collect basic personal information, academic performance data, hobby data, and campus activity data of college students. The academic performance data can be used to understand the strengths and weaknesses of students in mastering professional knowledge. The hobby data can be used to understand students' interest in careers. Campus activity data can be used to analyze their training in teamwork, leadership, communication skills, etc. The occupational information database module is used to store occupational classification and introduction information, update industry dynamic data, and establish an occupational skill requirement database. It classifies various occupations according to the international standard occupational classification system and provides detailed information on each occupation's work content, work environment, required skills, career prospects, salary levels, etc. The skills in the occupational skill requirement database are divided into professional skills and general skills. Professional skills include doctors' clinical diagnosis skills and teachers' teaching skills; general skills include communication skills and teamwork skills, etc., providing guidance for college students to improve their skills in a targeted manner after clarifying their career goals. The data analysis module is used to pre-process the data collected by the data collection module and analyze the correlation between the pre-processed data and the data stored in the career information database module. At the same time, it performs cluster analysis on the college student group, clusters the college student group according to factors such as college students' interests, skill levels, and career tendencies, and finds student groups with similar characteristics to provide a basis for formulating targeted career planning strategies; The personalized career planning module recommends careers for college students based on the analysis results of the data analysis module through a career matching algorithm and customizes personalized career planning plans. It also has a dynamic adjustment mechanism. The career matching algorithm selects highly matching careers from the career information database based on factors such as students' majors, interests, hobbies, and skill levels; The user interaction interface is used for inputting personal basic information and hobby data of college students, and at the same time displays the data in the career information database module and the results output by the personalized career planning module.

[0005] As a preferred technical solution of the present invention, the personal basic information includes but is not limited to the name, gender, age, school, major, and enrollment time of the college student; the academic performance data includes but is not limited to the college student's course grades, GPA, and ranking in each semester; the interest and hobby data includes but is not limited to obtaining from college students through questionnaires or interest testing platforms; the campus activity data includes but is not limited to the college student's club activities, student union work, volunteer activities, and subject competition experience.

[0006] As a preferred technical solution of the present invention, the occupational classification and introduction information includes but is not limited to the work content, working environment, required skills, career prospects and salary levels of various occupations. The industry dynamic data is obtained in real time from major recruitment websites, industry information platforms and government statistical departments. The occupational skill requirement database determines the correspondence between occupations and professional skills and general skills.

[0007] As a preferred technical solution of the present invention, the preprocessing includes cleaning duplicate, erroneous and missing data and standardizing the data. The correlation analysis uses a data analysis algorithm to find factors that affect career development; the cluster analysis divides student groups according to interests, skill levels and career tendencies.

[0008] As a preferred technical solution of the present invention, the career matching algorithm comprehensively considers the personal basic information, academic performance data, interest and hobby data, and campus activity data of college students to screen and match careers; the personalized career planning plan includes short-term, medium-term and long-term career development goal plans; the dynamic adjustment mechanism optimizes the career development goal plan in real time according to the personal situation of college students and changes in the career market. The short-term is during college, the medium-term is 1-5 years after graduation, and the long-term is 5-10 years after graduation. Short-term goals include obtaining relevant professional certificates and participating in internship programs during college; medium-term goals include entering the target enterprise to hold a junior position and accumulate experience; long-term goals include being promoted to a management position or technical expert.

[0009] As a preferred technical solution of the present invention, the data collection module collects college students' academic performance data and campus activity data by establishing data interfaces with the school's educational affairs system, student management system, and community management system.

[0010] As a preferred technical solution of the present invention, the occupational information database module uses web crawler technology to capture industry dynamic data and cooperates with industry associations and corporate human resources departments to update the occupational skill requirement database.

[0011] As a preferred technical solution of the present invention, the data analysis module uses a data cleaning algorithm to process missing data, uses the Pearson correlation coefficient to perform correlation analysis, and uses the K-Means clustering algorithm to perform cluster analysis.

[0012] As a preferred technical solution of the present invention, the personalized career planning generation module constructs a career matching model based on multiple factors and obtains the matching score between careers and college students through weighted calculation.

[0013] Compared with the existing technology, the beneficial effects of the present invention are as follows: This personalized career planning system for college students based on big data analysis, this system collects various data of college students through the data acquisition module, such as personal basic information, academic performance, interests and hobbies, and campus activity data. This data provides a comprehensive basis for subsequent analysis, enabling the system to gain an in-depth understanding of the characteristics of each student. The data analysis module uses this data for preprocessing, correlation analysis, and cluster analysis to explore students' strengths, interests, and potential abilities. On this basis, the personalized career planning module uses a career matching algorithm to screen out careers that are highly compatible with students from the career information database, and customizes personalized planning plans that include short-term, medium-term, and long-term goals, thereby achieving the purpose of recommending careers to college students based on the characteristics of the college population. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1This is a system block diagram of the personalized career planning system for college students based on big data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] See also Figure 1 ,The present invention provides a technical solution: a personalized career planning system for college students based on big data analysis, including a data acquisition module, a career information database module, a data analysis module, a personalized career planning module, and a user interaction interface; The data collection module is used to collect college students' basic personal information, academic performance data, hobbies and interests data, and campus activity data. Academic performance data can be used to understand students' strengths and weaknesses in mastering professional knowledge. Hobbies and interests data can be used to understand students' career interests. Campus activity data can be used to analyze their training in teamwork, leadership, communication skills, etc. The occupational information database module is used to store occupational classification and introduction information, update industry dynamic data, and establish an occupational skill requirement database. It classifies various occupations according to the international standard occupational classification system and provides detailed information on each occupation's work content, work environment, required skills, career prospects, salary levels, etc. The skills in the occupational skill requirement database are divided into professional skills and general skills. Professional skills include doctors' clinical diagnosis skills and teachers' teaching skills; general skills include communication skills and teamwork skills, etc., providing guidance for college students to improve their skills in a targeted manner after clarifying their career goals. The data analysis module is used to pre-process the data collected by the data acquisition module and analyze the correlation between the pre-processed data and the data stored in the career information database module. At the same time, it performs cluster analysis on the college student group. According to factors such as college students' interests, skill levels, and career tendencies, the college student group is clustered to find student groups with similar characteristics, providing a basis for formulating targeted career planning strategies; The personalized career planning module uses the analysis results of the data analysis module to recommend careers for college students through a career matching algorithm and customize personalized career planning plans. It also has a dynamic adjustment mechanism. The career matching algorithm selects highly matching careers from the career information database based on factors such as students' majors, interests, hobbies, and skill levels. The user interaction interface is used for inputting personal basic information and interests of college students, while displaying the data in the career information database module and the output results of the personalized career planning module.

[0017] As an embodiment of the present invention, further, personal basic information includes but is not limited to the name, gender, age, school, major, and enrollment time of the college student; academic performance data includes but is not limited to the college student's course grades, GPA, and ranking in each semester; interest and hobby data includes but is not limited to obtaining from college students through questionnaires or interest testing platforms; campus activity data includes but is not limited to the college student's club activities, student union work, volunteer activities, and subject competition experience.

[0018] As an embodiment of the present invention, further, occupational classification and introduction information includes but is not limited to the work content, work environment, required skills, career prospects and salary levels of various occupations. Industry dynamic data is obtained in real time from major recruitment websites, industry information platforms and government statistical departments, and the occupational skill requirement database determines the correspondence between occupations and professional skills and general skills.

[0019] As an embodiment of the present invention, further, preprocessing includes cleaning duplicate, erroneous and missing data and standardizing the data, correlation analysis uses data analysis algorithms to find factors that affect career development; cluster analysis divides student groups according to interests, skill levels and career tendencies.

[0020] As an embodiment of the present invention, further, the career matching algorithm comprehensively considers the personal basic information, academic performance data, interest and hobby data, and campus activity data of college students to screen and match careers; the personalized career planning plan includes short-term, medium-term and long-term career development goal plans; the dynamic adjustment mechanism optimizes the career development goal plan in real time according to the personal situation of college students and changes in the career market. The short-term is during college, the medium-term is 1-5 years after graduation, and the long-term is 5-10 years after graduation. Short-term goals include obtaining relevant professional certificates and participating in internship programs during college; medium-term goals include entering the target company as a junior position and accumulating experience; long-term goals include being promoted to a management position or technical expert.

[0021] As an embodiment of the present invention, further, the data collection module collects college students' academic performance data and campus activity data by establishing data interfaces with the school's academic affairs system, student management system, and community management system.

[0022] As an embodiment of the present invention, further, the occupational information database module utilizes web crawler technology to capture industry dynamic data, and cooperates with industry associations and corporate human resources departments to update the occupational skill requirement database.

[0023] As an embodiment of the present invention, further, the data analysis module adopts a data cleaning algorithm to process missing data, uses the Pearson correlation coefficient to perform correlation analysis, and uses the K-Means clustering algorithm to perform cluster analysis.

[0024] As an embodiment of the present invention, further, the personalized career planning generation module constructs a career matching model based on multiple factors, and obtains a matching score between the career and the college student through weighted calculation.

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1 The present invention provides a technical solution: a personalized career planning system for college students based on big data analysis, comprising the following steps: As an embodiment of the present invention, further, college students manually input personal basic information such as name, gender, age, school, major, enrollment time, etc. through the user interaction interface of the system. At the same time, interest and hobby data are entered using an interest test platform or by filling out a questionnaire. The system automatically collects college students' academic performance data such as course grades, GPAs, rankings, etc. for each semester, as well as campus activity data such as club activities, student union work, volunteer activities, and subject competition experience through the data interface established with the school's academic affairs system, student management system, and club management system. For example, the system obtains from the academic affairs system that the student's advanced mathematics score for this semester is 90 points and the GPA is 3.8; and from the club management system, it learns that the student has participated in the debate club and serves as the captain of the debate team.

[0027] As an embodiment of the present invention, further, the data analysis module pre-processes the collected data, uses a data cleaning algorithm to clean duplicate, erroneous and missing data, and standardizes the data. Then, a data analysis algorithm such as the Pearson correlation coefficient is used to analyze the correlation between the pre-processed data and the data stored in the career information database module to find out the factors affecting career development. For example, the analysis found that the programming language course scores of computer science students are positively correlated with the salary levels of software development occupations. Then, the K-Means clustering algorithm is used to perform cluster analysis on the college student group based on their interests, skill levels and career tendencies, and the students are divided into different groups such as technology research and development, management and marketing, and creative design.

[0028] As an embodiment of the present invention, further, the personalized career planning module constructs a career matching model based on multiple factors based on the results of the data analysis module. Taking into account the personal basic information, academic performance data, interest and hobby data, and campus activity data of college students, the matching score between the occupation and the college student is obtained through weighted calculation, and occupations with higher matching degrees are screened out from the occupation information database for recommendation. For example, for a statistics student with excellent math grades, a strong interest in data analysis, and who has won awards in mathematical modeling competitions, the system recommends occupations such as data analyst and algorithm engineer. At the same time, a personalized career planning plan covering short-term, medium-term and long-term is customized for students. Short-term goals include learning relevant data analysis tools and participating in data analysis internship projects during college; medium-term goals are to enter a data-driven company as a junior data analyst after graduation and accumulate project experience; long-term goals are to be promoted to data scientist, responsible for data analysis and decision support for important projects.

[0029] As an embodiment of the present invention, further, the user interaction interface displays various types of occupational data in the occupational information database module, including work content, work environment, required skills, career prospects and salary levels, etc., as well as recommended occupations and planning schemes output by the personalized career planning module. College students can view detailed information on the interface to understand the matching degree between the recommended occupations and their own future development paths. As the personal circumstances of college students change and the occupational market changes, the dynamic adjustment mechanism of the personalized career planning module is triggered. The system re-evaluates the data, optimizes the career development goal plan in real time, and updates the display content on the user interaction interface. For example, when a student obtains an artificial intelligence-related certificate during college, the system recalculates the matching degree, adds relevant occupational recommendations such as artificial intelligence engineer, and adjusts the career planning plan to add learning and practice suggestions in the field of artificial intelligence.

Claims

1. A personalized career planning system for college students based on big data analysis, characterized by: It includes data collection module, occupation information database module, data analysis module, personalized career planning module and user interaction interface; The data collection module is used to collect college students' basic personal information, academic performance data, hobbies and interests data, and campus activity data; The occupation information database module is used to store occupation classification and introduction information, update industry dynamic data and establish an occupation skill requirement database; The data analysis module is used to pre-process the data collected by the data collection module, analyze the correlation between the pre-processed data and the data stored in the occupational information database module, and perform cluster analysis on the college student group; The personalized career planning module recommends careers to college students based on the analysis results of the data analysis module through a career matching algorithm, customizes personalized career planning plans, and has a dynamic adjustment mechanism; The user interaction interface is used for inputting personal basic information and hobby data of college students, and at the same time displays the data in the career information database module and the results output by the personalized career planning module.

2. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The personal basic information includes but is not limited to the college student's name, gender, age, school, major, and enrollment date; the academic performance data includes but is not limited to the college student's course grades, GPA, and ranking in each semester; the interest and hobby data includes but is not limited to obtaining from college students through questionnaires or interest testing platforms; the campus activity data includes but is not limited to the college student's club activities, student union work, volunteer activities, and subject competition experience.

3. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The occupational classification and introduction information includes but is not limited to the work content, work environment, required skills, career prospects and salary levels of various occupations. The industry dynamic data is obtained in real time from major recruitment websites, industry information platforms and government statistical departments. The occupational skill requirement database determines the correspondence between occupations and professional skills and general skills.

4. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The preprocessing includes cleaning duplicate, erroneous and missing data and standardizing the data. The correlation analysis uses data analysis algorithms to find factors that affect career development. The cluster analysis divides student groups according to interests, skill levels and career tendencies.

5. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The career matching algorithm comprehensively considers the personal basic information, academic performance data, hobbies and interests data, and campus activity data of college students to screen and match careers; the personalized career planning program includes short-term, medium-term and long-term career development goal programs; The dynamic adjustment mechanism optimizes the career development goal plan in real time according to the personal situation of college students and changes in the career market.

6. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The data collection module collects college students' academic performance data and campus activity data by establishing data interfaces with the school's academic affairs system, student management system, and community management system.

7. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The occupation information database module uses web crawler technology to capture industry dynamic data and cooperates with industry associations and corporate human resources departments to update the occupational skill requirement database.

8. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The data analysis module uses a data cleaning algorithm to process missing data, uses the Pearson correlation coefficient to perform correlation analysis, and uses the K-Means clustering algorithm to perform cluster analysis.

9. The personalized career planning system for college students based on big data analysis according to claim 1 is characterized in that: The personalized career planning generation module constructs a career matching model based on multiple factors and obtains a matching score between careers and college students through weighted calculation.

Images