Construction and Practice of Cloud Computing-Based Service Platform for College Career Planning
Published Online: Sep 29, 2025
Received: Jan 28, 2025
Accepted: May 11, 2025
DOI: https://doi.org/10.2478/amns-2025-1095
Keywords
© 2025 Yadong Chai, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.
In recent years, the number of higher education graduates has surged and the employment problem has become more and more serious [1]. In view of the rapid progress of science and technology and the profound adjustment of economic structure, there is an urgent need to update the employment guidance service model of colleges and universities to meet the diversified and personalized job-seeking needs of graduates [2-4]. As the core driving force of the fourth industrial revolution, the rise of cloud computing technology has profoundly affected the social and economic structure, and its applications have been widely penetrated into the fields of education, healthcare, finance, etc., showing powerful intelligent processing and personalized service capabilities [5-7]. In this context, integrating cloud computing technology into the employment guidance service system of colleges and universities and constructing an online platform has become a key initiative to crack the employment problem and improve service efficiency [8-9].
Research is carried out on the construction and optimization of the college employment information service network platform. The platform aims to utilize technological innovation to provide graduates with more accurate and efficient employment support and help their careers set sail smoothly, thus effectively alleviating the pressure of the employment market [10-12]. On the other hand, the rapid growth of information resources, the user’s service demand for literature and information presents the characteristics of diversity, accuracy and efficiency, the generation of resource catalogs, subject information navigation does not fully meet the needs of users, they need a kind of information platform that can be directly accessed valuable, more personalized and easy to use [13-15]. College libraries should develop a personalized information service platform for career planning based on the professional settings of higher vocational education in schools, on the basis of analyzing the career information needs of students and combining with the actual work of libraries [16-17].
College students’ career planning prediction is of great significance for students’ career planning work. Literature [18] reveals the excellent performance of gray prediction algorithms in college students’ career planning prediction, and specifies the diversified trend of college students’ career choices. Literature [19] clarified that computer cognitive level can be used to predict students’ career planning choices and affirmed the feasibility of distributed platforms for targeted career planning for a large number of students in colleges and universities through research. How to improve the efficiency and effectiveness of career planning services has been the focus of research by scholars in related fields. Literature [20] proposes a career counseling platform for colleges and universities based on the WeChat public platform, which facilitates the use of resources for students’ career planning services in the context of the Internet, and improves the efficiency and relevance of career education. Literature [21] in-depth study of deep learning as the underlying architecture of career counseling effectively solves and improves the problem of college students’ career counseling and planning, and promotes the improvement of students’ career awareness and self-competence. Literature [22], in evaluating a career-focused career planning education model based on data analysis and text mining techniques, found that the model stimulated the development of students’ core skills and graduate employability to a certain extent. Literature [23] constructed an online career planning teaching system based on information technology, which has better flexibility and expandability than the traditional career planning teaching model. Literature [24] emphasized the significance of continuous assessment of career planning, and believed that it is necessary to integrate career planning resources to focus on the accessibility of resources to provide tailor-made services for college students’ career planning.
This paper designs a college career planning service platform based on cloud computing and multidimensional association rule algorithm, which integrates students’ multidimensional data information by utilizing cloud computing’s powerful computing and storage capabilities. Through the multidimensional association rule algorithm, the platform is able to mine the potential association between students’ interests and vocational skills data, and provide personalized career planning suggestions for students. Experiments are conducted to verify the effectiveness of the platform in mining the association rules of students’ information data. The career planning service platform of this paper is applied in practice to explore the changes and relevance of students in multiple career-related dimensions.
The design concept of the career planning platform refers to the general objective of the career planning course, which is to guide students to master the basic knowledge and common methods of career planning, to establish correct career ideals and views on career, career choice, entrepreneurship and success, to cultivate the ability of career planning, to improve the professional quality and vocational skills, and to be ready to adapt to the society, to integrate into the society, and to be ready for employment and entrepreneurship.
Based on the above concept, the career planning platform is mainly for three groups, namely, students and teachers at the front end, and administrators who provide technical support at the back end. The demand setting integrates the main content of career planning education and specific operational needs, and is divided into four modules: resource space, interactive space, task space and personal space, and the specific demands are shown in Figure 1.
Career planning platform specific requirements
Resource space is the basic module of career planning platform, which consists of three parts: public resources, professional resources and resource uploading. Among them, public resources mainly include theoretical knowledge of career planning, commonly used career assessment tools, career planning cases, resume making cases, job interview cases and so on. Professional resources are mainly videos of professional courses offered by the school, professional development videos and teaching materials of various majors, which students can choose to watch according to their own majors. The main function of this module is to promote students to carry out e-learning independently, expand the width and breadth of learning, and enhance students’ ability to integrate resources and self-learning.
The function of interactive space points to daily learning and communication, which includes online counseling, public discussion and style display, and it builds a communication platform for students, teachers and students, as well as students and outstanding career planning experts outside the university.
Task space is the main module for students to complete the tasks of the career planning course, which includes both compulsory tasks and optional tasks and various offline experiential activities of various majors. Compulsory tasks are combined with the career planning course, and each knowledge unit has a clear learning task to match it.
Personal space is a platform for students to understand themselves and their careers, as well as to record the development of their personal career planning and to realize self-evaluation and teacher evaluation. Among them, the target career module provides students with a clear understanding of the career orientation of the selected target career, the required vocational abilities and vocational qualities, and provides a scientific basis for students to compare themselves and find out their deficiencies.
Cloud computing [25] as a form of innovation in the field of information technology, since the birth of its concept has always been the world’s attention, these years is seen as a new generation of information technology innovation and business form of innovation of the core, with a wide range of market prospects. Now many mainstream IT companies have entered the field of cloud computing, each company based on their own traditional skills and market strategy from all directions to cloud computing. As a standardized IT capability, software, application channels or infrastructure can be provided to users over the Internet on an on-demand, self-service and billing basis. Cloud computing is not a new class of skills, but a variety of existing skills convergence and development of products, including grid computing, shared computing, virtualization skills, Web services and service-oriented architecture. The impact of the integration and development of the above popular skills on China’s IT industry, especially the software service industry should not be ignored.
On the one hand, “cloud” refers to the Internet, that is to say, the cloud service is not in the local computer, the user needs to access the broadband network through the “cloud” to use cloud services, and it can be anytime, anywhere access to the Internet of various terminals. On the other hand, “cloud” refers to the computing pool, that is to say, cloud computing is not realized by one or two computers, but by a large number of large-scale computer groups to form a resource pool, so it has the characteristics of large-scale clustering, good scalability and low cost.
According to the level where the resources provided by the cloud computing service are located, it can be divided into IaaS Infrastructure as a Service, PaaS Platform as a Service, and SaaS Software as a Service. Depending on the architecture of the underlying implementation, cloud computing can be further categorized into: dedicated cloud, public cloud, and hybrid cloud. Public clouds allow many different customers to share cloud services, but users are unaware of each other’s usage. Dedicated clouds are for internal use within an organization and are owned by a single customer, who can decide which of those users can use the services provided. Hybrid clouds are a combination of the first two, where the customer owns cloud resources that are partly dedicated and partly shared with others.
Data mining [26] is the non-trivial process of extracting valid, novel, and potentially useful knowledge from large, noisy, incomplete, fuzzy, and random real-world application data. The process of data mining is shown in Fig. 2.
Data mining process
Data Selection: Determine the operation object corresponding to data mining, i.e. target data.
Preprocessing: this is a very important step in data mining, an important part of the whole process, the effect of pre-processing directly determines the content of the mining knowledge, the efficiency and accuracy of mining.
Conversion: Dimensionality reduction, convert the high dimensional data that is not conducive to data mining into low dimensional data that is more suitable for mining, to achieve the process of dimensionality reduction, to ensure the feasibility of mining and the efficiency of mining.
Data Mining: Determine the purpose and task of mining, and determine what kind of mining algorithm is more appropriate to use, such as classification, clustering, association rules and other algorithms. According to the characteristics of the provided application data and the requirements of mining, choose the appropriate data mining algorithms to find out the knowledge that users need.
Interpretation and evaluation: the knowledge found in the data mining stage is not all useful, and is screened after evaluation by the user and the actual application, and redundant or irrelevant patterns are deleted, so that the knowledge obtained by the user is more understandable and practical.
The current data mining algorithms mainly include neural network method, decision tree method, genetic algorithm, rough set method, association rule method and other classical algorithms. The specific ones are as follows:
Association rules: association rules are one of the most typical of the many types of algorithms for data mining, and the advantages are more obvious when comparing with other algorithms in the association mining of things database. The essence of association rule mining is to discover the association relationship hidden in a large amount of data. If the values of data items are repeated, then there is likely to be some kind of intrinsic connection between these data items, and by using association rule mining, the association rules of these data items can be established.
Decision Tree Approach: Using the mutual information knowledge in information theory, some field contents with maximum information are mined. Through these field contents mined in the database to establish a node of the decision tree, each field will have different values, and then according to the different values to establish the branches of the tree. In the subset of each branch, the lower nodes of the tree need to be constantly repeated to establish, complete the establishment of each branch, in the process to build a decision tree.
Neural Network Approach: is a mathematical model of information processing that applies a structure similar to the synaptic connections of the brain.
Association rule [27] mining is a process of one’s own continuous learning, through the use of association rule mining, one can find out what one does not know in advance, or even dig out what is unimaginable yet realistic.
Support of itemsets: assuming that
Support of the rule: The support of rule
Support indicates the probability that
Minimum support: a user-defined value that can be used to determine the frequency of itemsets, denoted as min_sup.
Confidence: For association rules of the form
Minimum Confidence: In order to be able to judge whether a rule is reliable or not, a user-defined value is used to represent the minimum reliability, denoted as min_
Expected confidence: In all transaction sets, if there are
In association rule mining, support, confidence, and expected confidence together constitute a measure for evaluating the mining content. Among them, support and confidence reflect the usefulness and certainty of the discovered rules respectively, which can describe the nature of association rules more intuitively. That is to say, in the results of association rule mining, there must be the confidence and support of the rule, if a rule does not take into account the limitations of confidence and support, then the rule is meaningless. For this reason, the minimum support threshold
If the support and confidence corresponding to the result
Then the association rule
If the probability of occurrence of itemset
The ultimate goal of mining association rules is to find frequent itemsets in transactional data. In transactional databases, minimum support min_sup and minimum confidence min_ Find all frequent itemsets: the itemsets must appear in the transaction data no less than the pre-given minimum support min_sup. Generate strong association rules from frequent itemsets: the support of frequent itemsets must be not less than the minimum support and the minimum confidence. Therefore, in the process of generating association rules, we should make full use of frequent itemsets to generate strong association rules. The generation of strong association rules is based on the threshold set by the user, and the rules that satisfy both the minimum confidence and the minimum support will be retained, and the retained results are the mined strong association rules.
In association rules, the number of dimensions of the data may be different, and the association rules can be classified into uni-dimensional and multi-dimensional association rules. The process of mining student-related data involves only one dimension of the data, which is called a unidimensional association rule. When expressing the content of data, association rules involving two or more dimensions are called multidimensional association rules. In this paper, multidimensional association rules are used to mine students’ interests and relevant vocational skills they have acquired.
This university student career planning platform provides a convenient and fast communication channel for students, teachers and school administrators, and can guarantee the authenticity of student information, avoiding false information to cause trouble to students’ career planning, and giving full play to the maximum advantages of this university student system to provide fast services for graduates. The overall framework of the system is shown in Figure 3.
System overall framework
This chapter begins with a random collection of 65,589 student profiles from the career planning service platform, of which 13,452 students filled in their interests and vocational skills. However, the career planning service platform is very open to the form of data filled in by students. When students fill in their interests, the career planning service platform does not provide students with a set of interest labels for them to choose from, but allows them to enter their interests freely.
In order to facilitate the subsequent research work, the data entered by the students are firstly standardised. Therefore, this chapter firstly designs algorithms to intelligently separate the interest strings filled in by students of the career planning service platform, and merge the separated interest words with synonyms, including different forms of the same word, singular and plural, present tense, etc., and finally merge the semantic synonyms manually. In this paper, from the 13,452 student CVs collected that have been filled in with interest words, we identify 25,851 interest words, and each interest word represents one interest hobby respectively. Then a collection of 3445 interests was obtained by identifying synonyms, and the most general interest in the collection was used as the representative interest item of the collection.
Then, this paper transforms the interests in all student data into canonical expressions, i.e., when a student fills in an interest word that belongs to a certain synonym set, the original interest is replaced with the representative interest of the corresponding set. In this paper, we processed the students’ vocational skills data in the same way, and finally counted the frequency and occurrence ratio of interests and vocational skills, where the occurrence ratio refers to the number of occurrences of interests/total number of people in the dataset, and some of the experimental results are shown in Table 1 and Table 2.
Interest in word frequency sample
| Interest term | Frequency | Emergence ratio |
|---|---|---|
| Travel | 2385 | 5.73% |
| Read | 1782 | 4.29% |
| Music | 1409 | 3.37% |
| Golf | 1255 | 3.08% |
| Ski | 1211 | 2.89% |
| Sport | 1114 | 2.66% |
| Photography | 1110 | 2.66% |
| Movie | 978 | 2.34% |
| Cycling | 893 | 2.15% |
| Running | 848 | 2.00% |
Professional skill word frequency sample
| Interest term | Frequency | Emergence ratio |
|---|---|---|
| Management | 4437 | 1.61% |
| Leadership | 3963 | 1.45% |
| Strategic planning | 3350 | 1.24% |
| Project management | 3179 | 1.15% |
| Strategy | 2562 | 0.97% |
| Finance | 2313 | 0.86% |
| Sales | 2067 | 0.77% |
| Microsoft office | 1991 | 0.76% |
| Marketing | 1914 | 0.73% |
| Training | 1846 | 0.73% |
Undoubtedly, the higher the frequency of interest hobbies in student data, the more popular they are and the higher the value of the analysis. However, from the data in the table, it can be found that the distribution of interests and hobbies is relatively dispersed, and only a relatively small number of interests and hobbies have a relatively high frequency of occurrence, therefore, without loss of generality, this paper categorises interests with a rate of occurrence of interest words greater than 1% as high-frequency interests and hobbies (which means that there is at least one person who has this interest in every 100 people), and as the distribution of the data on vocational skills is even more dispersed, this paper will this threshold relaxed to 0.1%, so 212 high-frequency interests and 4,760 high-frequency skills were finally filtered out. In addition, this paper counts the student coverage of high-frequency interests and vocational skills, and the results are shown in Figures 4 and 5, respectively.
Some high-frequency interest interests user coverage
User coverage of some high-frequency professional skills
From the figures, it can be seen that the coverage rate of high-frequency interests and vocational skills among students is relatively similar. Among them, the top 50 high-frequency hobbies covered 88.89% of students and the top 100 hobbies covered 92.86% of students. In terms of vocational skills, the top 50 vocational skills covered 93.56% of students, and the top 100 vocational skills covered 96.11% of students, and the above data illustrate that the high-frequency hobbies and vocational skills selected in this paper are representative. Finally this paper obtained standardised data for each student profile.
Based on the collected data, this chapter first applies the Apriori algorithm [28] to mine frequent itemsets, which have both interest and skill items, followed by mining multidimensional association rules using two-dimensional conditional constraints, such that the forerunner of the association rules consists of hobbies and interests, and the successor consists of vocational skills. The number of association rules mined according to different support and confidence thresholds is shown in Table 3.
The number of association rules under different threshars
| Minimum confidence threshold | Minimum support threshold | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2% | 0.4% | 0.6% | 0.8% | 1.0% | 1.2% | 1.4% | 1.6% | 1.8% | 2.0% | |
| 10% | 11469 | 4323 | 2831 | 1907 | 1309 | 885 | 601 | 453 | 330 | 254 |
| 20% | 3973 | 1281 | 694 | 505 | 398 | 341 | 274 | 222 | 180 | 143 |
| 30% | 1748 | 557 | 302 | 199 | 143 | 120 | 114 | 101 | 89 | 72 |
| 40% | 710 | 196 | 101 | 56 | 39 | 32 | 24 | 27 | 25 | 22 |
| 50% | 220 | 61 | 24 | 11 | 12 | 9 | 9 | 10 | 4 | 9 |
| 60% | 78 | 21 | 6 | 3 | 3 | 1 | 2 | 4 | 4 | 3 |
| 70% | 17 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 80% | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 90% | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Based on different thresholds a certain number of association rules for hobbies and vocational skills can be mined. In this paper, the structure of association rules is counted, and it is found that these association rules can be categorized into the following three types:
Single-to-single (S2S) association rules, which include a precursor and a successor, e.g., “language→strategy”. Double-to-single (D2S) association rules, including two forerunners and one successor, e.g. “basketball/travel→team building”. Double-to-double (D2D) association rules, including two forerunners and two successors, e.g. “design/sales→training/coaching”. Without loss of generality, this paper focuses only on these three association rules. When the minimum confidence threshold is set to 10%, based on different minimum support thresholds, three main types of association rules can be mined. Table 4 shows the number of each type of association rules with different support thresholds.
The association rules for various classes under different thresholds
| Minimum support threshold | 0.2% | 0.4% | 0.6% | 0.8% | 1.0% | 1.2% | 1.4% | 1.6% | 1.8% | 2.0% |
|---|---|---|---|---|---|---|---|---|---|---|
| S2S rule | 2406 | 1479 | 1078 | 812 | 619 | 474 | 352 | 271 | 203 | 166 |
| D2S rule | 3225 | 667 | 241 | 121 | 63 | 30 | 17 | 13 | 12 | 6 |
| D2D rule | 2543 | 520 | 338 | 167 | 88 | 45 | 24 | 15 | 11 | 2 |
According to the experimental needs, this paper sets the minimum support degree of association rules to 0.2% and the minimum confidence degree to 40%, so as to mine a large number of S2S interest-skill association rules, which are sorted in descending order according to their confidence degrees, and some results are shown in Table 5. From Table 5, it can be found that there is a certain correlation relationship between some hobbies and vocational skills. For example, the confidence level of “basketball→leadership” reaches 62.94%, and its support degree is as high as 3.93%, and the enhancement degree reaches 184.02%, which indicates that the two are highly correlated, and the intrinsic relationship between them has great research value. In this paper, we use the same method and set different support and confidence thresholds to screen D2S and D2D association rules, and Tables 6 and 7 show some experimental results of D2S and D2D association rules, respectively. By building an association analysis model, this paper mines out a large number of association rules between hobbies and vocational skills, revealing that there are some intrinsic connections between hobbies and vocational skills of students in social networks, i.e., students with specific hobbies tend to have some specific vocational choice tendencies.
Given the S2S association rule sample
| Front piece | Afterpiece | Support | Confidence | Degree of ascension |
|---|---|---|---|---|
| Building | Team building | 0.22% | 68.43% | 432.61% |
| Basketball | Leadership | 3.93% | 62.94% | 184.02% |
| Baseball | Leadership | 4.40% | 57.84% | 169.18% |
| Rugby | Management | 0.88% | 55.69% | 145.35% |
| Performance | Leadership | 0.31% | 54.77% | 160.23% |
| Communication | Strategic planning | 0.62% | 52.99% | 183.02% |
| Hike | Management | 0.20% | 52.73% | 137.66% |
| Risk | Java | 0.21% | 52.23% | 1083.28% |
| Psychology | Management | 0.48% | 50.94% | 133.08% |
| Fitness | Management | 2.20% | 45.50% | 118.83% |
Given a threshold value of D2S correlation rule sample
| Front piece | After piece | Support | Confidence | Degree of ascension |
|---|---|---|---|---|
| Media communication | Social media | 0.24% | 87.89% | 693.04% |
| Media communication | Public relations | 0.22% | 78.80% | 685.33% |
| Basketball writing | Leadership | 0.32% | 77.26% | 225.99% |
| Baseball fitness | Leadership | 0.28% | 70.45% | 206.00% |
| Photography basketball | Management | 0.35% | 68.86% | 179.78% |
| Media internet | Social media | 0.56% | 68.04% | 536.60% |
| Tennis investment | Finance | 0.23% | 65.05% | 325.19% |
| Read basketball | Management | 0.51% | 55.46% | 144.75% |
| Media internet | Strategic planning | 0.43% | 52.59% | 181.63% |
| Media internet | Marketing | 0.40% | 50.52% | 307.88% |
The D2D association rule sample is shown in the given threshold
| Front piece | Afterpiece | Support | Confidence | Degree of ascension |
|---|---|---|---|---|
| Media communication | Public relations social media | 0.27% | 75.81% | 1547.74% |
| Basketball; writing | Management; leadership | 0.23% | 54.51% | 234.51% |
| Photography; basketball | Management; leadership | 0.29% | 54.12% | 232.55% |
| Basketball; language | Coaching; leadership | 0.25% | 47.47% | 553.72% |
| Technology; baseball | Management; leadership | 0.40% | 45.69% | 196.38% |
| Baseball; language | Leadership; workshop facilitation | 0.21% | 40.00% | 1039.13% |
| Media; movie | Public relations; social media | 0.17% | 39.70% | 810.67% |
| Media; writing | Social media; blogging | 0.21% | 36.47% | 1420.93% |
| Movie; writing | Skill-editing; social media | 0.16% | 31.65% | 1240.15% |
| Travel internet | Leadership; strategic planning | 0.61% | 30.07% | 175.82% |
The development of the career planning platform has laid a solid foundation for the school system to carry out life planning education. On this basis, the author takes the theory of “job matching” as the guiding theory and the individualized development of students as the guide, and constructs a “1+3+2” career planning operation mode of “one platform, three stages, and two ceremonies”. Among them, “one platform” refers to the career planning platform, the “three stages” are the freshman stage, the lower grade stage and the senior stage, and the “two ceremonies” are the “dream mailbox” launching ceremony and the “coming-of-age ceremony” ceremony. Figure 6 shows the operation of the platform.
“1+ 3+ 2”career planning mode
Foundation Stage: First Month of New Student Enrollment When students enter the school, besides adapting to new teachers, new classmates and new environment, adapting to a new identity is the key to their entry into the school. For this reason, in the first month of enrollment, the school should give them a correct understanding of the orientation, so that they understand that vocational education also has a broad space for development. The stage of sharpening ability: the first semester of freshman and sophomore years This stage is the basic stage of career planning for college students. In this semester, offline career planning classroom teaching is the main focus, and students learn with the quality resources of the career planning platform to complete a comprehensive analysis of self, career and environment, so as to further realize the focus of their careers. Enhancement stage: second half of sophomore year to graduation Entering the senior stage, students have gradually established the awareness of career planning through the guidance of teachers and the application of the career planning platform, which points to more independent learning of individual students in addition to obtaining career certificates. In order to enhance learning efficiency, the career planning platform will match students with the same or similar target careers online to form an 8-member study group, which often has a common development direction. This function is very widely used in practice, and some students who are not in the same class become peers on the road to growth.
This chapter mainly used a random sampling method to randomly select students majoring in Chinese international education from four schools that put into use the career planning platform as individual samples.
The questionnaire of this study involved a total of 120 research subjects. The research subjects were from different study stages of freshmen, sophomores and juniors, 40 from each stage.
After the pre-test and post-test modifications finally a questionnaire was formed that met the survey requirements in terms of reliability and validity. The questionnaire is divided into two main parts: the first part makes a short statement to clarify the purpose of the survey and related matters to the respondents. The second part is the main content of the questionnaire, the data collected covers the basic information of the respondents and different research dimensions and other aspects of the survey. The main content of the questionnaire is divided into two main sections: the first section mainly collects the basic information of the survey respondents. The second part is to use the career planning platform from five research dimensions, which are career awareness dimension, career emotion dimension, career will dimension, career expectation dimension, and career behavior and tendency. The platform is used to investigate the career identity of college students at different study stages in different dimensions, to understand the current status of career identity and the trajectory of identity development of the research subjects, and to explore the factors affecting their career identity.
The results of students’ occupational identity descriptive statistics are shown in Table 8. In this study, according to the five-degree Likert scale, based on the analysis of the data, it is concluded that the total mean value of Chinese international education students’ occupational identity is 3.756, which is higher than the theoretical mean value of 3, indicating that the overall level of occupational identity of Chinese international education students is high. By sorting the mean values, it is concluded that students’ occupational identity in each dimension is measured in the following order: occupational emotion dimension > occupational awareness dimension > occupational will dimension > occupational expectation dimension > occupational behavior and tendency dimension, with occupational emotion dimension having a high mean value of 4.012 and occupational awareness dimension having the second highest mean value of 3.946, and the means of these two dimensions are higher than the theoretical mean value of 3, which indicates that students in the stage of school cultivation in Chinese international education have a higher overall level of occupational identity. This indicates that students of Chinese language education in the school training stage have strong emotional factors for the international Chinese language education career, and at the same time, they have a better understanding of the international Chinese language teaching career through school training and self-knowledge of the career.
Different learning stages of the students’ professional identity
| N | Minimum value | Maximum value | Mean | Standard deviation | |
|---|---|---|---|---|---|
| Occupational awareness | 120 | 1.711 | 5.000 | 3.946 | 0.649 |
| Professional emotion | 120 | 2.010 | 5.000 | 4.012 | 0.645 |
| Professional will | 120 | 2.201 | 5.000 | 3.654 | 0.642 |
| Career expectation | 120 | 1.668 | 5.000 | 3.615 | 0.677 |
| Occupational behavior and disposition | 120 | 1.288 | 5.000 | 3.589 | 0.735 |
| Professional identity | 120 | 1.931 | 5.000 | 3.756 | 0.594 |
The standard deviation of students’ professional identity is 0.594, indicating that there is not much difference in the level of pre-service teachers’ professional identity. Among the five dimensions, the standard deviation of 0.735 for the dimension of occupational behavior and tendency is larger, which also indicates that there are larger differences in the occupational behavior and tendency of Chinese language international education students.
In order to reflect the differences in the vocational identity of Chinese international education students at different study stages, this study conducted a one-way ANOVA on the vocational identity of Chinese international education students at different study stages, as shown in Table 9.
Analysis of differences in working industry identity
| Grade | Case number | Mean value | Standard deviation | F | P | |
|---|---|---|---|---|---|---|
| Occupational awareness | Freshman year | 40 | 4.147 | 0.59 | 5.387 | 0.007 |
| Sophomore | 40 | 3.97 | 0.618 | |||
| Junior | 40 | 3.704 | 0.676 | |||
| Professional emotion | Freshman year | 40 | 4.274 | 0.555 | 7.556 | 0.001 |
| Sophomore | 40 | 3.956 | 0.634 | |||
| Junior | 40 | 3.761 | 0.638 | |||
| Professional will | Freshman year | 40 | 3.919 | 0.54 | 7.551 | 0.001 |
| Sophomore | 40 | 3.634 | 0.706 | |||
| Junior | 40 | 3.398 | 0.574 | |||
| Career expectation | Freshman year | 40 | 3.95 | 0.559 | 10.254 | 0.000 |
| Sophomore | 40 | 3.555 | 0.647 | |||
| Junior | 40 | 3.339 | 0.678 | |||
| Occupational behavior and disposition | Freshman year | 40 | 3.851 | 0.637 | 7.659 | 0.001 |
| Sophomore | 40 | 3.644 | 0.731 | |||
| Junior | 40 | 3.246 | 0.732 | |||
| Professional identity | Freshman year | 40 | 4.022 | 0.471 | 9.939 | 0.000 |
| Sophomore | 40 | 3.754 | 0.591 | |||
| Junior | 40 | 3.484 | 0.577 |
The results of the data show that there are extremely significant differences in the vocational identity of Chinese international education students at different learning stages (p<0.01), and there are significant differences in the five dimensions of vocational awareness, vocational emotion, vocational will, vocational expectations, and vocational behaviors and dispositions (p<0.05). Due to the differences in various influencing factors such as learning tasks and learning experiences as well as the differences in thinking changes of Chinese language international education students at different learning stages, it also leads to the differences in their vocational identity, which is a process of continuous change and development. The existence of differences in the vocational identity of students of Chinese language education at different learning stages provides a prerequisite for the study of vocational identity of students of Chinese language education.
According to the survey and analysis of the occupational identity of Chinese language international education students, it is concluded that the occupational identity of Chinese language international education students at different study stages shows a gradual downward trend, as shown in Figure 7. The level of occupational identity shows a decreasing trend, from high to low: freshman > sophomore > junior. The factors influencing the vocational identity of Chinese language international education students at different study stages mainly include four aspects: the state and society, school, family and individual.
Investigation and analysis of occupational identity
By analyzing the correlation between the vocational identity of Chinese language international education students at different stages of study, it was concluded that all five dimensions were strongly correlated with the vocational identity of Chinese language international education students. All five dimensions significantly affect the vocational identity of Chinese international education students. The details are shown in Tables 10-12.
College students’ professional identity and various dimensional analysis
| Occupational awareness | Professional emotion | Professional will | Career expectation | Occupational behavior and disposition | Professional identity | |
|---|---|---|---|---|---|---|
| Occupational awareness | 1 | ** | * | |||
| Professional emotion | 0.845** | 1 | ** | |||
| Professional will | 0.475** | 0.625** | 1 | ** | * | |
| Career expectation | 0.642** | 0.704** | 0.722** | 1 | ||
| Occupational behavior and disposition | 0.402** | 0.415** | 0.538** | 0.625** | 1 | |
| Professional identity | 0.816** | 0.854** | 0.785** | 0.894** | 0.762** | 1 |
College students’ professional identity and various dimensional analysis
| Occupational awareness | Professional emotion | Professional will | Career expectation | Occupational behavior and disposition | Professional identity | |
|---|---|---|---|---|---|---|
| Occupational awareness | 1 | |||||
| Professional emotion | 0.807** | 1 | * | |||
| Professional will | 0.743** | 0.695** | 1 | |||
| Career expectation | 0.712** | 0.742** | 0.759** | 1 | ||
| Occupational behavior and disposition | 0.715** | 0.7056** | 0.705** | 0.797** | 1 | |
| Professional identity | 0.892** | 0.885** | 0.865** | 0.903** | 0.909** | 1 |
College students’ professional identity and various dimensional analysis
| Occupational awareness | Professional emotion | Professional will | Career expectation | Occupational behavior and disposition | Professional identity | |
|---|---|---|---|---|---|---|
| Occupational awareness | 1 | |||||
| Professional emotion | 0.835** | 1 | ||||
| Professional will | 0.723** | 0.654** | 1 | |||
| Career expectation | 0.706** | 0.633** | 0.618** | 1 | * | |
| Occupational behavior and disposition | 0.678** | 0.636** | 0.738** | 0.639** | 1 | |
| Professional identity | 0.911** | 0.858** | 0.844** | 0.835** | 0.877** | 1 |
In order to verify the relationship between Chinese international education students’ occupational identity and the five dimensions in the freshman study stage, this study used correlation analysis, and the results of the analysis are shown in Table 10. ** At the 0.01 level (two-tailed), the correlation is significant.
The results show that vocational identity is significantly and positively correlated with each dimension, and the correlation coefficients of the five dimensions are as follows: vocational expectation>vocational emotion>vocational awareness>vocational will>vocational behaviors and tendencies, and vocational expectation has the strongest correlation with vocational identity, with a correlation coefficient of 0.894, and vocational behaviors and tendencies have the weakest correlation with vocational identity. The correlation coefficients between the five dimensions and the overall vocational identity are all greater than 0.75, indicating that each dimension is strongly correlated with the vocational identity of Chinese international education students in the first-year study stage, and therefore, all five dimensions significantly affect the vocational identity of Chinese international education students in the first-year study stage.
In order to verify the relationship between vocational identity and the five dimensions of Chinese international education students in the sophomore study stage, this study used correlation analysis, and the results of the analysis are shown in Table 11.
The results show that vocational identity is significantly positively correlated with each dimension, and the correlation of the five dimensions is as follows: vocational period behavior and tendency>vocational expectation>vocational awareness>vocational emotion>vocational will, vocational behavior and tendency has the strongest correlation with vocational identity, and vocational will has the weakest correlation with vocational identity, with the correlation coefficients of 0.909, 0.865, respectively. The correlation coefficients of the five dimensions with the overall vocational identity are all greater than 0.85, indicating that each dimension is strongly correlated with the vocational identity of the sophomore Chinese international education students, and therefore, all five dimensions significantly affect the vocational identity of the sophomore Chinese international education students.
In order to verify the relationship between career identity and the five dimensions of Chinese international education students in the junior study stage, this study adopts correlation analysis, and the results of the analysis are shown in Table 12.
The results show that vocational identity is significantly positively correlated with each dimension, and the correlation coefficients of the five dimensions are as follows: vocational period awareness>vocational behavior and tendency>vocational emotion>vocational will>vocational expectation, vocational awareness has the strongest correlation with vocational identity, and vocational expectation has the weakest correlation with vocational identity, with the correlation coefficients of 0.911 and 0.835, respectively. The correlation coefficients of the five dimensions with the overall vocational identity are all greater than 0.80, indicating that each dimension is strongly correlated with the vocational identity of the Chinese international education students in the third-year study stage, and therefore, all five dimensions significantly influence the vocational identity of the Chinese international education students in the third-year study stage.
This paper completes the design and realization of the career planning service platform for college students based on cloud computing.
In this paper, we first collect the relevant data of students on the career planning service platform, standardize the relevant data of students’ interests and vocational skills, and obtain 212 high-frequency interests and 4760 high-frequency skills of students. Subsequently, the multidimensional association rules between interests and skills were mined, and it was found that there were many association rules between students’ interests and vocational skills, which were mainly classified into three categories: single-to-single association rules, double-to-single association rules, and double-to-double association rules. These association rules can be used to determine the students’ career adaptability, which provides some help for students’ personal career development and career planning.
Subsequently, by analyzing the practical application of career planning service platform in colleges and universities, the level of students’ career identity is different in different study stages, and the level of students’ career identity shows a decreasing trend from the first year to the third year of college. The correlation between students’ career expectations and career identity also shows a decreasing trend from the first to the third year of study, which is not conducive to students’ employment and career choice near graduation. The career planning service platform provides a good help for the dilemma faced by the junior students, which can target to find out the problems of students in career planning and provide a channel for teachers to take further career guidance for students.