An Empirical Study on the Precise Employment Situation-Oriented Analysis of Digital-Driven Talents with Big Data Analysis

Lin, Li; Tsai, Sang‐Bing

doi:10.1155/2022/8758898

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Professional big data analysis requires certain professional knowledge of computer technology, Internet technology, statistics, digital technology, etc. It is not only simple data collection, processing, and analysis but also deep excavation and analysis of college students' related information, enterprise recruitment information, and employment policy information by using big data technology [13]. Applying big data information to college students' employment guidance work and providing students with employment guidance services are an inevitable demand for college students' employment guidance work in the era of big data and also a major challenge for college students' employment guidance work.…”

Section: Introductionmentioning

confidence: 99%

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

Huang

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The increasingly large employment group in colleges and universities has brought huge employment pressure to the society. Therefore, improving the level of professional ability of college students, providing students with personalized career development direction planning, and helping students to establish a correct concept of career selection are feasible ways to solve the structural contradiction of college students’ employment. The arrival of big data era provides new development opportunities for employment work in colleges and universities. Big data drives the high-quality development of precise employment in colleges and universities, which is inevitably required to reach the precise requirements of work development, information mastering, platform docking, information pushing, and help work. The traditional employment guidance work is backward in means and poor in timeliness, which leads to poor intervention effect on students’ employment expectation. With the deepening of digital campus construction and the development of big data technology, massive educational data has been accumulated but not reasonably utilized. Therefore, in this paper, we take the massive student data accumulated in digital campus as the research object, based on the research method of convolutional neural network, to explore the hidden personalized information of students behind it, predict their future career development direction, and provide scientific and technological support for the work of university education and teaching. Thus, the architecture classical DenseNet was improvised to avoid gradient disappearance and guarantee the classification accuracy, thereby targeting to reduce the number of connections. The study also proposed a 2-DenseNet model, wherein the attention module was embedded into 2-DenseNet followed by the conduction of training and validation on the university employment dataset. The experimental results show that the method proposed in this paper could predict the appropriate career development direction timely and effectively based on multiple perspectives of students’ comprehensive quality. This would enable the students to plan and adjust their employment expectations leading to disseminating of efficient employment guidance in colleges and universities.

show abstract

Section: Introductionmentioning

confidence: 99%

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

Huang

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Understanding university graduate’s independent entrepreneurship by 3D designing cultural and creative products

Xing,

Abidin,

Mokhtar

et al. 2023

EHSS

View full text Add to dashboard Cite

In the context of the COVID-19 pandemic, the global economy is sluggish and the employment situation is grim. It is difficult for fresh graduates to find a job with a stable income. Therefore, college students' self-employment has become one of the main forms of college graduates. Among them, designing a meaningful cultural and creative product for sale is one of the ways for art graduates to achieve employment. However, college students' entrepreneurship must face fierce competition from existing cultural and creative products in the market, which often leads to a high failure rate of college students' entrepreneurship and a weaker ability to resist market risks. Based on the above problems, this paper is triggered to help college students quickly adapt to the business competition environment. The research methods of this subject mainly include the following two: (1) With the help of teachers, college graduates majoring in art and design can design cultural and creative products with practical, beautiful, and cultural characteristics, so that these novel cultural and creative products have price competitiveness and product design competitiveness in existing cultural and creative products. (2) Helping college students to visit enterprises broadening employment channels, and expanding sales forms through college leaders are also effective ways to improve students' self-employment. From these two aspects, the main research content of this paper is to promote the entrepreneurship and employment of college students in the context of COVID-19.

show abstract

An Empirical Study on the Precise Employment Situation-Oriented Analysis of Digital-Driven Talents with Big Data Analysis

Cited by 2 publications

References 21 publications

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

A Multiperspective Intervention Path Study of Employment Expectancy in the Era of Big Data

Understanding university graduate’s independent entrepreneurship by 3D designing cultural and creative products

Contact Info

Product

Resources

About