Construction and Coordination Mechanism of College Students’ Employment and Labor Relations in the Internet+ Environment

Zuo, Baike; Juan, Gloria

doi:10.3991/ijet.v17i19.34517

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor [1,2]. Cultivating students to form a good labor habit is an important content for vocational colleges to fulfill their mission of enabling students to achieve all-round development in terms of morality, intelligence, physical fitness, aesthetic perception, and labor [3][4][5]. Higher vocational colleges need to put more efforts to education through practice based on the features of each discipline.…”

Section: Introductionmentioning

confidence: 99%

Student Behavior Identification During Practice and Training Based on Video Image

Chen¹,

Fan²,

Dai³

et al. 2023

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Enriching and developing the connotation and value of labor education theories can help students in higher vocational colleges form correct viewpoint and attitude towards labor. Higher vocational colleges should put more efforts to education through practice based on the features of each discipline. Accurately identifying students' behavior in complex practice and training scenarios is very important for teachers to know about their status during practice and training, however, existing research results are not applicable to complex practice and training scenarios since they have neither considered how to improve the accuracy of static image identification while ensuring the model is lightweight structured, nor considered the time series information of students' behavior during practice and training in the collected video images. For this reason, this paper took the property management major as the subject to study the identification of student behavior during practice and training based on video image. In the paper, the students' practice and training content was divided into three aspects, a task of asking students to cooperate with each other to deal with an equipment failure emergency was adopted for the research, and a research idea of helping teachers figure out students' status during practice and training via identifying their actions and intentions during the said activities was determined. Then, a few pre-processing operations were performed on the captured video images of student behavior during practice and training, including removing abnormal image frames, filtering, and aligning, etc. After that, based on the collected video image data, the dynamic convolution kernel was improved and optimized, and a lightweight convolution network model was built for identifying student behavior during practice and training. At last, experimental results verified the validity of the proposed identification model.

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Section: Introductionmentioning

confidence: 99%

Student Behavior Identification During Practice and Training Based on Video Image

Chen¹,

Fan²,

Dai³

et al. 2023

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An Analysis of Employment in Agricultural and Forestry Colleges Based on Logistic Modelling

Guo,

Song,

Wang

et al. 2024

2024 13th International Conference on Educational and Information Technology (ICEIT)

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A bilateral employment situation prediction model for college students using GCN and LSTM

Shen¹

2023

PeerJ Computer Science

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Due to the prevailing trend of globalization, the competition for social employment has escalated significantly. Moreover, the job market has become exceedingly competitive for students, warranting immediate attention. In light of this, a novel prognostic model employing big data technology is proposed to facilitate a bilateral employment scenario for graduates, aiding college students in promptly gauging the prevailing social employment landscape and providing precise employment guidance. Initially, the focus lies in meticulously analyzing pivotal aspects of college students’ employment by constructing a specialized employment platform. Subsequently, a classification model grounded in a graph convolution network (GCN) is built, leveraging big data technology to comprehensively comprehend graduates’ strengths and weaknesses in the employment milieu. Furthermore, based on the outcomes derived from the comprehensive classification of college students’ qualities, a college students’ employment trend prediction method employing long and short term memory (LSTM) is proposed. This method supplements the analysis of graduates’ employability and enables accurate forecasting of college students’ employment trends. Empirical evidence substantiates that my proposed methodology effectively evaluates graduates’ comprehensive qualities and successfully predicts their employment prospects. The achieved F-values, 82.45% and 69.89%, respectively, demonstrate the efficacy of anticipating the new paradigm in graduates’ dual-line employment.

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Construction and Coordination Mechanism of College Students’ Employment and Labor Relations in the Internet+ Environment

Cited by 3 publications

References 20 publications

Student Behavior Identification During Practice and Training Based on Video Image

Student Behavior Identification During Practice and Training Based on Video Image

An Analysis of Employment in Agricultural and Forestry Colleges Based on Logistic Modelling

A bilateral employment situation prediction model for college students using GCN and LSTM

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