The Employability and Career Development of Finance and Trade College Graduates

College Students' Employability, Cognition, and Demands for ChatGPT in the AI Era Among Chinese Nursing Students: Web-Based Survey

BACKGROUND

With the rapid development of artificial intelligence (AI) and the widespread use of ChatGPT, nursing students' artificial intelligence quotient (AIQ), employability, cognition, and demand for ChatGPT are worthy of attention.

OBJECTIVE

We aimed to investigate Chinese nursing students' AIQ and employability status as well as their cognition and demand for the latest AI tool-ChatGPT. This study was conducted to guide future initiatives in nursing intelligence education and to improve the employability of nursing students.

METHODS

We used a cross-sectional survey to understand nursing college students' AIQ, employability, cognition, and demand for ChatGPT. Using correlation analysis and multiple hierarchical regression analysis, we explored the relevant factors in the employability of nursing college students.

RESULTS

In this study, out of 1788 students, 1453 (81.30%) had not used ChatGPT, and 1170 (65.40%) had never heard of ChatGPT before this survey. College students' employability scores were positively correlated with AIQ, self-regulation ability, and their home location and negatively correlated with school level. Additionally, men scored higher on college students' employability compared to women. Furthermore, 76.5% of the variance was explained by the multiple hierarchical regression model for predicting college students' employability scores.

CONCLUSIONS

Chinese nursing students have limited familiarity and experience with ChatGPT, while their AIQ remains intermediate. Thus, educators should pay more attention to cultivating nursing students' AIQ and self-regulation ability to enhance their employability. Employability, especially for female students, those from rural backgrounds, and students in key colleges, deserves more attention in future educational efforts.

Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction

The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.

Current college graduates' employability factors based on university graduates in Shaanxi Province, China

With the development of higher education, there are a large number of college graduates turn into the labor market, and college students' employability has become a popular topic. In order to explore the factors that affect employability's improvement and what factors employability includes, this article summarizes the previous research on employability, uses university graduates of Shaanxi Province as research examples, and investigates the employability factors of college graduates. With the help of SPSS software, data analysis is conducted on the 220 valid questionnaires. The study uses reliability and validity analysis to verify the quality of the questionnaire, takes the exploratory factor analysis to test the employability factors of college students, and employs multiple linear regression analysis to test the factors that influence employability's improvement. The results of the research show that individual traits, social experience, and workplace training have a significant impact on college students' employability; knowledge understanding and learning ability, self-management ability, emotional intelligence, generic skills, professional ability, and career planning capability are the important factors of the employability which college students should master. Our research results update the influencing factors of employability, so that contemporary college students have a new understanding of employability, and help them to improve their employability more pertinently.

Development and validation of Chinese college students' future employability scale

COVID-19 and the pandemic-induced lockdowns juxtaposed against the surge in the number of college graduates have made the dilemma of "fierce competition and difficult employment" more real. The employment of college students has become a topic of serious concern in society. This study aimed to develop a Future Employability Scale for Chinese college students and evaluate its reliability and validity. Based on the analysis of the literature, the study developed the initial measurement scale of the college students' future employability and calibrated the initial measurement and question volume based on experts' feedback. First, the students' group was measured, and data from 389 university students were collected and analyzed. Second, the data collection and verification factor analysis of 387 university students were collected and verified, and the internal consistency reliability, split-half reliability, and validity of the scale were evaluated. Further, 68 college students were selected to evaluate their test-retest reliability after an interval of one month. The Future Employability Scale of college students had 28 items covering four dimensions: knowledge skill, personality quality, interpersonal network, and career development. The reliability test found that the total scale of the Future Employability Scale and the internal consistency reliability, split-half reliability, and retest reliability of each dimension were good, and the validity test suggested that the scale had good content validity, structural validity, and calibration correlation validity. With a clear structure, good reliability, and validity, the Future Employability Scale is a good tool to measure the future employability of college students.