The role of cloud-based career planning education platform in boosting students’ career growth
Published Online: Mar 17, 2025
Received: Oct 12, 2024
Accepted: Jan 30, 2025
DOI: https://doi.org/10.2478/amns-2025-0296
Keywords
© 2025 Junru Li, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Along with the continuous development and progress of social economy and science and technology, the quality requirements of the society for practitioners have been gradually improved, and the competition in the talent market has continued to heat up, resulting in the mobility and change of careers becoming more and more significant. In order to adapt to the social and economic changes under the new situation, the majority of colleges and universities, which are responsible for cultivating hundreds of millions of high-quality workers, should strengthen the guidance of students’ career planning while comprehensively improving the comprehensive quality of students. Therefore, how to effectively carry out students’ career planning education has become an important issue facing higher education [1-3]. Through the field survey and research on the current situation of career planning education in colleges and universities and the field survey of frontline teachers and students, combined with the reading of literature, it is found that the traditional teaching mode of classroom-based teaching adopted in colleges and universities is difficult to meet the needs of career planning education due to the limited teaching resources, narrowing of teaching content, single form of teaching and the neglect of students’ independent learning of the main body of the classroom teaching, and the lack of practice and interaction. It is difficult to meet the teaching demand and students’ learning needs in various aspects of career planning education due to the characteristics of rich teaching content, diverse teaching forms, autonomy, practicability and interactivity, etc., which has become a bottleneck restricting the effective development of career planning education for current students [4-6].
With the deepening development of education informatization, network education platform is increasingly widely used due to its openness, autonomy, interactivity, convenience, sharing and other characteristics. Compared with the traditional teaching mode of students’ career planning, which is mainly based on classroom teaching, network education emphasizes autonomy and focuses on the study of students’ personality characteristics, which can be used to achieve students’ personalized development by providing targeted learning guidance for students with different characteristics [7-9]. In terms of learning context creation, network education can make full use of the openness and interactivity of its platform and the richness and sharing of network resources to flexibly create learning contexts of independent inquiry and collaborative communication, actively guide students to carry out meaningful practice and exploration, so that students’ sense of innovation, practical ability and teamwork spirit can be cultivated, and then improve their comprehensive vocational literacy [10-11]. As a useful and necessary reform and supplement to the traditional teaching mode, the application of network education can better solve the current bottlenecks that restrict the effective development of students’ career planning education. Therefore, it is very necessary to use the campus network resources owned by the majority of colleges and universities to build the required career planning education platform, extend career planning education to network education, give full play to the advantages of network education in career planning education, make up for the shortcomings of traditional teaching, and promote the effective development of students’ career planning education [12-15].
The article firstly constructs a career planning education platform based on cloud computing, and carries out the overall design of the platform system from the IaaS layer, PaaS layer and SaaS layer and three levels respectively, and then carries out the construction of digital resources for career planning education, and further constructs a “training cloud” for career planning education, and introduces the dynamic load balancing algorithm in the It also introduces the receiver-driven, sender-driven and bidirectional-driven algorithms in dynamic load balancing. In order to understand the effect of the career planning education platform on students, an empirical test was conducted with students of a school as the research object, and after analyzing the differences in the basic conditions of the respondents, the influence of the career planning education platform on the students’ employability, career planning ability and entrepreneurial ability was further analyzed by regression analysis.
Effectively delivering career planning education to students is a fundamental requirement to meet the urgent needs of today’s social and economic changes and to fulfill the essential task of college education. Literature [16] proposes a career planning and employment strategy path based on deep learning, and verifies the effectiveness and feasibility of the proposed strategy through empirical analysis, which helps students to solve the main problems faced by career planning education, and then improves their competitiveness in employment, comprehensive quality, and the ability to explore career planning and employment strategies. Literature [17] used Indonesian students as research subjects and explored their perceptions of the role of LinkedIn, a career social media platform, in their career planning process through interviews, which showed that the respondents believed that LinkedIn could help them with self-assessment, career exploration, and job searching, facilitating them to look for a job based on their areas of interest. Literature [18] tailored a career-focused online career planning module based on Savickas’ (2005) career construct theory, students’ skills and interests, and an intervention experiment verified the reliability of the customized online module, which can effectively address students’ indecisiveness and negative thinking about their career paths, and improve college students’ career adaptability. Literature [19] emphasized the importance of mobile learning and designed an online learning platform for air environment testing and career planning based on 5G network, which was verified by experiments that the platform can cultivate students’ career planning ability, stimulate students’ motivation to learn, and play a certain role in boosting students’ career growth. Literature [20] takes students who are skilled in vocational skills and vocational workers in career centers as research objects, and explores their views on displaying their skills on online platforms through interview analysis, and the results prove that planning and designing an online vocational skills display platform is very necessary, which can provide information related to vocational skills to both knowledgeable and uneducated people, and create income for those who have skills. Literature [21] experimentally verified the superior performance of an online learning platform for predicting employment opportunities, which predicts and presents the user’s learning path compared to traditional learning methods, and thus satisfies the students’ desire for knowledge and provides them with online information anytime, anywhere. Literature [22] used Java, Python and other technologies to design a college student development planning platform based on intelligent Q&A mechanism, which can provide college students with planning consulting to help students clarify their future plans and give full play to their talents and values.
Cloud computing is the development of parallel computing, distributed computing and grid computing. Cloud computing, as a service-oriented technology, can be divided into three layers, SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service).The role of the SaaS layer is to provide an application as a service to the customer, the PaaS layer’s The role of SaaS layer is to provide applications as a service to customers, the role of PaaS layer is to provide a development platform as a service to users, and the role of IaaS layer is to provide virtual machines or other resources as a service to users.
According to the construction requirements and cloud computing service system, the overall architecture of cloud computing-based career planning education platform is shown in Figure 1.
Overall architecture diagram
The IaaS layer provides basic resources through virtualization technology, including virtual storage, virtual networks, and virtual computing.
The PaaS layer offers a variety of solutions, and all development is done in this layer, which includes cloud service management platform, distributed database, big data analysis system, and business development platform.
The SaaS layer provides the vocational planning education digital resources from the cloud to users, including portals, mobile applications [23].
The digital resource platform for career planning education based on cloud computing includes four functional modules: system management, portal, big data analysis system, and practical training cloud, as shown in Figure 2.
Functional module diagram
It includes three sub-modules: user management, resource management, and security management. User management career planning education platform is a system with access rights control, the system is set up with four types of users such as administrators, teachers, students, visitors, etc., who enjoy different rights according to the different types of users respectively. Resource management includes functions such as making, publishing, revising, deleting, categorizing, and analyzing digital resources. Security management monitors the network and system operation status to ensure the safe and stable operation of the system.
Unified authentication login, supporting PC and mobile applications. Online learning includes video teaching, courseware material download, online Q&A, and other functions. Simulated real training effectively alleviates the problems of tight training venues and equipment in institutions. The digital library integrates digital resources from the library to facilitate online reading, searching, renewing, downloading, and other functions.
Provide personalized services by analyzing user preferences using big data. Search term analysis examines the keywords used by learners when searching and identifies their concerns. Browsing behavior analysis is used to monitor learners’ access to website behavior records, analyze their access content and access trajectory, and provide them with content they are interested in. User demand analysis analyzes the content of learner inquiries, identifies the key content that learners are concerned about, and organizes common questions and related answers based on the analysis results to provide retrieval.
Practical training cloud provides users with practical training platforms, practical training resources, and services for practical training processes, so that practical training environments, operating platforms, simulation scenarios, and result evaluations can be supplied on-demand and self-service when trainees carry out practical training activities. Thus, resources can be shared and users can collect relevant information more accurately.
The construction of digital resources for vocational planning education should be comprehensively planned, the professional curriculum should be systematically designed, and the fragmented resources with reasonable internal logic system should be systematically reconstructed by focusing on the learner as the center and on the technological application of the enterprise, targeting the posts corresponding to the profession and according to the skill requirements of each post, and according to the different levels of the material, the cumulative parts, the modules and the courses. In the planning of resource content, it is necessary to consider that the basic resources cover the basic knowledge points of the specialty and the basic skill points of the post, expand the resources to reflect the cutting-edge technology and the latest achievements of the industry development, and gather the high-quality resources with different geographical characteristics and technical features of the country in the field of this specialty. The library’s resources strive to be rich and diverse in terms of quantity and type, and can greatly exceed the scope of resources required by the library to provide courses, in order to achieve resource redundancy [24]. The composition of resources should include professional introduction, talent training programs, teaching environment, network courses, training programs, and assessment systems.
1) Network courses and electronic lesson plan library. Including professional syllabus, teaching courseware for network courses, e-teaching plan, teaching design, project teaching, and case teaching. Including matching professional talent training programs, curriculum standards, production process simulation software, etc. It should also include the module design for students’ independent learning, with the main contents of student information, question bank, internship training, level test, and assessment.The resources should be able to reflect the complete job technical skills requirements and come from actual production projects and key production technology links.
2) Text materials and electronic material library. Including all types of professional text, image graphics, audio, and video, as well as professional teaching animation classes.Through research and development, integration, and other means, the industry standard, teaching cases, examination questions, practical training projects, competition programs, and other common theme materials are gathered in a library.With the development of the industry and technological progress, it is important to constantly enrich and update the content related to text materials and electronic material libraries.
3) Virtual laboratory. The content includes project training, guidance on student graduation design, cooperation between schools and enterprises, comprehensive training room management, and a practical training assessment program for teachers and students. It also includes audio and video information on business processes, product structures, production scenes, etc. of enterprise production practices, as well as enterprise virtual scenes, virtual equipment, and virtual training programs (virtual training platform, simulated workflow platform).
4) Professional Resource Library. Including professional talent training programs, professional construction programs, teacher training, “dual-certification” education and order classes, job competency analysis, off-campus training bases, school conditions, etc. [25]. The professional resource base consists of three parts: management, promotion, and exchange, which form an open and efficient sharing mode for knowledge pushing, collaboration, and independent learning.
5) Industry resource base. It consists of teaching and research information on vocational planning education, as well as industry information. Teaching and research information includes teaching and research information such as policies and regulations on vocational planning education, educational quality standards, academic papers on vocational planning education, experts and talents in vocational planning education, and experience in running vocational planning education. Industry information includes technical data of the industry, enterprise and industry information, enterprise personnel training courses and training organizations, occupational standards and job responsibilities, career planning and certificate training, employment information and employment guidance. In addition, it collects, organizes and shares domestic and international information on product technology upgrading, enterprise management knowledge, vocational positions in the industry, technological innovations and technological advances in the industry, so as to provide the latest information for the development of the industry.
Generally speaking, tasks on each node in a distributed system are dynamically generated and the load size of each node changes dynamically, so a dynamic load balancing strategy should mainly be used. In the process of completing the task periodically check the situation of task completion and interact these situations with other nodes, on this basis, according to certain principles to determine whether to migrate the task as well as the source and destination nodes of migration and the amount of migration. Currently there are some problems in the dynamic load balancing algorithm, such as. Load evaluation for each node: the factors that affect the load are complex, such as the length of the CPU queue, the available resource situation, and the requirement to deliver tasks. Jitter problem of task migration: due to the existence of delays in information transfer, communication obstruction, etc., a task load node may be migrated back and forth between the nodes without being executed, thus generating jitter and reducing the system frequency.
To address these problems, the most representative algorithms in the dynamic load balancing strategy are the receiver-driven algorithm, sender-driven algorithm, and bidirectional driving algorithm.
The main idea of this algorithm is. Divide all the nodes into light loaded nodes and heavy loaded nodes according to the threshold
At startup, all the nodes start executing tasks and after some time, the node starts checking whether it is a lightly loaded node or not, if it is, it tries to find out the heavily loaded node in the related domain and requests the tasks on that node. Specifically, let the load of this light loaded node be
To achieve an even distribution of tasks, the amount of load that should be passed to a heavily loaded node in the domain of interest should be found
The node can then send acceptance task requests to each related node according to
Shows the flow of the recipient-driven algorithm
The main benefit of receiver-driven is that load information is not exchanged with each other. For massively parallel computing problems, when every node is busy, little additional scheduling overhead is required. Much of the load balancing work is done by idle nodes without adding much extra burden to the busy nodes.
The main disadvantages of receiver-driven are that the number of tasks is relatively small at the beginning and end phases, and many task requests delay the execution of busy nodes.
The main idea of this algorithm is to divide the lightly loaded nodes and heavily loaded nodes by a threshold
At startup, all nodes begin to perform tasks. After some time, the node starts checking whether it is a heavy loaded node or not and if it is, it tries to distribute the tasks evenly in the related domain. Specifically, let the load of this heavy loaded node be
To achieve an even distribution of tasks, the amount of load passed by the heavy loaded nodes to the nodes in each relevant domain should be found
The node can then send tasks to each related node as per
Send-driven algorithm flow
The main idea of this algorithm is that both the sender and the receiver can transfer the task. Therefore, this algorithm has the advantages of both sender-driven and receiver-driven algorithms.When the system load is low, the sender-driven algorithm can easily find the nodes that are lightly loaded.When the system load is high, the receiver-driven algorithm can easily find the nodes that are heavily loaded.However, the two-way active algorithm also has some shortcomings, such as the use of sender-driven tendencies to cause system instability when the system load is high. A better solution is to use an adaptive algorithm with a reasonable threshold value and use receiver-driven when the system is under high load and sender-driven when the system is under low load [26].
A complete practical training environment is the basic prerequisite for vocational planning education that focuses on skill development. However, there is a lack of practical programs, insufficient training resources, and uneven distribution of resources in vocational planning education, which is mainly reflected in regional differences. The core technology of “cloud computing” can help solve the problem of regional differences and build a “practical training cloud”.
Career planning education “practical training cloud” can provide trainees with practical training platform, practical training resources, practical training process services. The practical training environment, operation platform, simulation scenarios, and result evaluation can be provided on-demand and self-service when trainees carry out practical training activities. The “practical training cloud” has unlimited processing capacity and storage energy, strong interactivity and derivativeness, avoids duplication of the same resources in the vocational planning education system, and saves education funds.
This kind of Internet-based resource sharing not only solves the regional differences in practical training teaching within the school, but also allows enterprises to participate in it for remote practical training guidance, breaks through the bottleneck of school-enterprise cooperation, broadens the mode of school-enterprise cooperation and the space for cooperation, and realizes the seamless connection between the school in terms of cultivating talents and the enterprise in terms of talent reserve.
In addition to training the skills of school students, the vocational planning education “practical training cloud” can also provide training for working people, so that working people can also use this platform to learn new technologies and conduct professional vocational training. The architecture of the platform theoretically draws on the idea of “distributed dynamic load balancing algorithm” to analyze and design the task management mechanism, task queuing mechanism, task execution mechanism and execution structure. The architecture of “Practical Training Cloud” is shown in Figure 5.
Architecture of the Training cloud
In this paper, using the students of a school as an empirical study, the basic profile of the respondents, i.e., whether there are significant differences in the dimensions across gender, grade, nature of the school they are attending, and their place of origin, will be examined.
The variance analysis of different genders in each dimension is shown in Table 1 (M stands for mean; SD stands for standard deviation; * stands for significance, a * stands for significance less than 0.05, ** stands for significance less than 0.01, the same below). From the table, it can be seen that the F-values corresponding to different genders in professional and methodological competence are 5.025 and 5.58, respectively, and the significance corresponding to them is less than 0.05, so there is a significant difference between different genders in professional and methodological competence. The F value corresponding to social competence is 1.985 and the significance corresponding to it is more than 0.05, which means that there is no significant difference between different genders in social competence. The average score for professional competence is 3.03 for boys and 4.17 for girls, indicating that girls have higher professional competence than boys.The average methodological competence score for boys is 3.87 and for girls is 3.67, indicating that boys have greater methodological competence than girls.
Different gender analysis of different dimensions
| Man(M±SD) | Female(M±SD) | F value | |
|---|---|---|---|
| Professional ability | 3.03±0.53 | 4.17±0.88 | 5.025** |
| Method ability | 3.87±0.79 | 3.67±0.71 | 5.58** |
| Social ability | 4.31±0.45 | 4.81±0.43 | 1.985 |
The variance analysis of different grades in each dimension is shown in Table 2. The F-value of the table shows that there is a significant difference between different grades in terms of professional competence, methodological competence, and social competence. The mean values of the three dimensions of the first year students are 3.44, 3.18 and 4.13, the mean values of the three dimensions of the second year students are 4.26, 3.38 and 3.91, and the mean values of the three dimensions of the third year students are 3.55, 4.37 and 3.9, which can be clearly seen that the third year scores the highest in the dimensions, followed by the second year, and the first year is the lowest. It indicates that vocational competence is increasing progressively from the first to the third grade.
Differences in different grades in different grades
| First grade(M±SD) | Second grade(M±SD) | Third grade(M±SD) | F value | |
|---|---|---|---|---|
| Professional ability | 3.44±0.32 | 4.26±0.4 | 3.55±0.65 | 17.621 |
| Method ability | 3.18±1.12 | 3.38±0.99 | 4.37±0.47 | 10.623 |
| Social ability | 4.13±0.94 | 3.91±0.06 | 3.9±0.06 | 9.586 |
The analysis of variance of different school natures in each dimension is shown in Table 3. The F-value of the table shows that there is a significant difference between different school types in terms of professional competence, methodological competence, and social competence. The mean scores of public schools in each dimension are 3.68, 4.19, and 4.65, and the mean scores of private schools in each dimension are 3.76, 3.25, and 4.0. Obviously, the scores of public schools are higher than those of private schools in each dimension, which indicates that the professional competence, methodological competence, and social competence of students in public schools are higher than that of students in private schools.
Differences in the properties of different schools in each dimension
| Public office(M±SD) | Private sector(M±SD) | F value | |
|---|---|---|---|
| Professional ability | 3.68±0.83 | 3.76±0.35 | 6.018** |
| Method ability | 4.19±0.83 | 3.25±1.01 | 6.31** |
| Social ability | 4.65±0.48 | 4±0.45 | 7.048** |
The variance analysis of different places of origin in each dimension is shown in Table 4. The F-value in the table shows that there is a significant difference between different places of origin in terms of professional competence, but there is no significant difference in terms of methodological competence or social competence. The mean score of professional competence of students whose place of origin is urban (municipal and above) is 3.48, that of students from county is 3.18, and that of students from rural area is 2.51. It is clear that students from urban (municipal and above) have the strongest professional competence, students from county is the next strongest, and those from rural area are the weakest.
The difference analysis of different sources in each dimension
| City(M±SD) | County seat(M±SD) | Countryside(M±SD) | F value | |
|---|---|---|---|---|
| Professional ability | 3.48±0.29 | 3.18±0.27 | 2.51±0.16 | 8.551** |
| Method ability | 3.78±0.68 | 3.59±0.26 | 3.75±0.68 | 1.583 |
| Social ability | 4.73±0.14 | 4.5±0.25 | 3.35±0.36 | 2.167 |
According to the scoring rules of the questionnaire, each option was assigned a rank from 1-5, and the mean of the scores for each dimension of vocational competence was calculated, with 3 as the threshold, and the closer the score is to 5 indicating a higher level of vocational competence. The descriptive statistics (N=450) are shown in Table 5. The table shows that the scores of all dimensions are above 3.5 and the overall mean value of vocational competence is 3.9, which indicates that the students’ vocational competence is strong. When the secondary indicators were analyzed, it was found that overall social competence was good, but there were some problems with professional and methodological competence.
Descriptive statistics
| Variable | Dimension | Mean | Standard deviation |
|---|---|---|---|
| Professional ability | Professional knowledge | 3.64 | 0.88 |
| Basic skill | 3.93 | 0.77 | |
| Practical use | 3.55 | 0.76 | |
| Method ability | Reflection and development ability | 4.15 | 0.75 |
| Career planning ability | 3.56 | 0.72 | |
| Social ability | Ethics and philosophy | 4.47 | 0.79 |
| Communication skills | 3.99 | 0.77 |
By testing the influence relationship between the independent and dependent variables through regression equations, it is possible to calculate the average number of units that the dependent variable changes when the independent variable changes by one unit.The regression coefficient refers to the degree of influence that the independent variable has on the dependent variable, i.e. the slope of the regression straight line. Commonly used indicators are R2, t-test, R2 is called the coefficient of determination, which refers to the proportion of the total variation of the dependent variable that can be explained by the independent variable through the regression relationship.The value of R2 is between 0 and 1, and the closer the value is to 1, it means that the model fit is better. In this section, the multiple linear regression model is used to study the relationship between the use of career planning education platforms and students’ career growth.
This paper uses a multiple step-by-step regression method to analyze the relationship between course teaching situation and employability harvest and derive the regression model. The step-by-step multiple regression can eliminate the independent variables that do not have a significant effect on the dependent variable step by step, and finally leave the independent variables that have a significant effect on the dependent variable, so as to construct the best model. The regression of platform usage on employability harvesting is shown in Table 6. From the table, R2 is 0.413, indicating that the regression equation is able to explain 41.3% of the original variables, the tolerance is close to 1, and the VIF is less than 10, so there is no covariance between the independent variables, and the equation fits well. From the above regression model, it can be seen that eight variables: the degree of knowledge of emerging careers in the industry, the degree of knowledge of personal qualities in the industry, campus recruitment information, mass media employment information, family and friend social relations, interview skills, social security knowledge, teacher acceptance and teaching method acceptance entered the model (P<0.05), of which mass media (e.g., the Internet, television, magazines, etc.) had a negative effect on employability gains with a regression coefficient of -0.138, while all other variables had a positive effect.
The platform usage is a return to the ability of employment
| variable | Nonnormalized coefficient | Normalization factor | t | Sig. | Common linear statistics | R2 | ||
|---|---|---|---|---|---|---|---|---|
| B | Standard error | Trial version | tolerance | VIF | ||||
| constant | 0.353 | 0.073 | 1.343 | 0.069 | 0.413 | |||
| Learn about emerging occupations | 0.117 | 0.062 | 0.166 | 2.759 | 0.036 | 0.583 | 1.31 | |
| Know the industry about personal quality | 0.1 | 0.028 | 0.141 | 2.18 | 0.014 | 0.891 | 1.18 | |
| Campus recruitment information | 0.089 | 0.049 | 0.2 | 1.883 | 0.008 | 0.763 | 1.09 | |
| Mass media information | -0.138 | 0.044 | -0.213 | -0.124 | 0.012 | 0.74 | 1.524 | |
| Family social relations | 0.143 | 0.075 | 0.157 | 2.39 | 0.009 | 0.817 | 1.313 | |
| Interview skills | 0.113 | 0.054 | 0.134 | 1.227 | -0.01 | 0.894 | 1.706 | |
| Awareness of social security | 0.103 | 0.048 | 0.105 | 0.378 | 0.003 | 0.688 | 1.112 | |
| Method acceptance of teaching method | 0.142 | 0.078 | 0.158 | 4.692 | -0.015 | 0.746 | 1.348 | |
| Teacher acceptance | 0.1 | 0.045 | 0.079 | 0.692 | 0.006 | 0.596 | 1.757 | |
In this paper, we do a stepwise multiple regression of course teaching situation on the gain of career planning ability, and the regression of platform usage on the gain of career planning ability is shown in Table 7. From the table, R2 is 0.453, indicating that the regression equation can explain 45.3% of the original variables, the tolerance is close to 1, and the VIF is less than 10, so there is no covariance between the independent variables, and the equation fits well. From the regression model, it can be seen that the degree of understanding of the industry on individual professional skills, the degree of understanding of preferential policies related to the employment of talents (such as support for the Western Program, the Three Support Program, the Selected Candidates, etc.), the campus recruitment information, the employment information of the mass media, the interviewing skills, the degree of understanding of social security, the degree of teacher acceptance and the degree of acceptance of the teaching method entered into the equation (P<0.05), of which the degree of acceptance of the mass media ( such as the Internet, television, magazines, etc.) had a negative effect on the harvest of career planning skills with a regression coefficient of -0.118, while all other variables had a positive effect.
The platform usage is a return to the ability of career planning
| variable | Nonnormalized coefficient | Normalization factor | t | Sig. | Common linear statistics | R2 | ||
|---|---|---|---|---|---|---|---|---|
| B | Standard error | Trial version | tolerance | VIF | ||||
| constant | 0.09 | 0.011 | 1.848 | 0.563 | 0.453 | |||
| Know the industry about personal quality | 0.321 | 0.113 | 0.149 | 0.77 | 0.029 | 0.564 | 1.51 | |
| Preferential policies for talent employment | 0.102 | 0.04 | 0.041 | 0.272 | 0.048 | 0.905 | 1.742 | |
| Campus recruitment information | 0.233 | 0.045 | 0.128 | 1.146 | 0.014 | 0.775 | 1.993 | |
| Mass media information | -0.118 | 0.037 | -0.052 | -0.34 | 0.008 | 0.743 | 1.621 | |
| Interview skills | 0.153 | 0.038 | 0.127 | 0.145 | 0.021 | 0.828 | 1.27 | |
| Awareness of social security | 0.117 | 0.025 | 0.102 | 0.79 | 0.005 | 0.908 | 1.412 | |
| Teaching method | 0.184 | 0.052 | 0.15 | 1.241 | 0.007 | 0.678 | 1.307 | |
| Teacher acceptance | 0.077 | 0.018 | 0.094 | 2.208 | 0 | 0.742 | 1.433 | |
In this paper, we do stepwise multiple regression of the use of career planning education platform on the harvest of entrepreneurial ability, and the regression of the use of platform on the harvest of entrepreneurial ability is shown in Table 8. From the table, R2 is 0.372, indicating that the regression equation can explain 37.2% of the original variables, the tolerance is close to 1, and the VIF is less than 10, so there is no covariance between the independent variables, and the equation fits well. From the regression model, it can be seen that the degree of knowledge of emerging careers related to the industry, the degree of clarity of career development goals, the various procedures before starting a business and the entrepreneurial process, the policies and regulations related to entrepreneurship, the degree of acceptance of teachers, the degree of acceptance of the teaching method entered into the equation (P<0.05), in which the degree of clarity of career development goals has a negative effect on the harvest of entrepreneurial ability, the regression coefficient is -0.108 , and all other variables had a positive effect.
Platform usage is a return to the ability of entrepreneurship
| variable | Nonnormalized coefficient | Normalization factor | t | Sig. | Common linear statistics | R2 | ||
|---|---|---|---|---|---|---|---|---|
| B | Standard error | Trial version | tolerance | VIF | ||||
| constant | 0.528 | 0.211 | 3.231 | 0.153 | 0.372 | |||
| Learn about emerging occupations | 0.15 | 0.086 | 0.602 | 2.748 | 0.017 | 0.936 | 1.228 | |
| Career goals are clear | -0.108 | 0.058 | -0.513 | -1.247 | 0.035 | 0.903 | 1.313 | |
| Pre-entrepreneurship preparation | 0.159 | 0.039 | 0.26 | 1.892 | -0.001 | 0.664 | 1.07 | |
| Entrepreneurial policy | 0.125 | 0.044 | 0.332 | 0.665 | 0.001 | 0.626 | 1.329 | |
| Teaching method | 0.152 | 0.054 | 0.202 | 1.191 | 0.006 | 0.583 | 1.552 | |
| Teacher acceptance | 0.073 | 0.016 | 0.195 | 2.7 | 0.016 | 0.806 | 1.27 | |
In conclusion, the use of the cloud-based career planning education platform has different degrees of positive impact on the three dimensions of employability, career planning ability, and entrepreneurial ability on students’ career growth, indicating that the higher the acceptance of the career planning education platform, the greater the gains of students. The achievement of the goals of career guidance teaching needs to be combined with the positive interaction between teachers and students to accomplish the tasks of career guidance teaching in order to make students gain effectively in all aspects of employability, career planning ability, and entrepreneurial ability. Therefore, the use of cloud-based career planning education platform in career guidance planning will improve the degree of gains in each dimension of students’ career guidance courses to different degrees.
With the continuous development of education technology, the advantages of network education are becoming more and more prominent, and its role in students’ career planning is becoming more and more important. In this context, the article proposes a career planning education platform based on cloud computing to explore its role in boosting students’ career growth.
In the regression analysis of the platform usage on the gain of career planning ability, the regression coefficient of mass media on the gain of career planning ability is -0.118, and all other variables are positively influenced, so the cloud computing-based career planning education platform proposed in this article has a good effect on students’ career planning ability. Therefore, the teacher and teaching method acceptance has a positive effect on the three dimensions of employability, career planning ability, and entrepreneurship ability on the students’ harvest, and the variables of the use of the cloud-based career planning education platform designed in this paper have different degrees of positive and negative effects on the dimensions of the students’ harvest.