The Cultivation Quality Evaluation of International Talents Based on Deep Learning

Jia, Xin; Li, Mo; Zhao, Jianmin

doi:10.3991/ijet.v17i09.31375

Cited by 2 publications

(2 citation statements)

References 17 publications

(17 reference statements)

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“…Teaching management decision-making mainly refers to the role of big data evaluation technology in talent selection, identification, and management. Jia et al (2022) used Empirical Modal Algorithm (EMD) to analyze the quality changes in the process of international talent cultivation, and obtained corresponding predicted quality values based on the trends in the quality of international talent cultivation at different time scales. Guleria and Sood (2023) utilized a functional education framework that integrates machine learning (ML) and interpretable AI (XAI) to analyze the educational factors that affect students' employment, in order to help students make career choices and decisions.…”

Section: Practical Characteristics Of Big Data Driven Education Evalu...mentioning

confidence: 99%

A Systematic Review of Big Data Driven Education Evaluation

Lin,

Zhou,

Wang

et al. 2024

Sage Open

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The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.

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Section: Practical Characteristics Of Big Data Driven Education Evalu...mentioning

confidence: 99%

A Systematic Review of Big Data Driven Education Evaluation

Lin,

Zhou,

Wang

et al. 2024

Sage Open

View full text Add to dashboard Cite

show abstract

“…To introduce high-level internationalized talents, it is important to evaluate the training quality of high-level internationalized talents, which is the foundation of the other works. Jia et al [22] studied the quality evaluation of internationalized talent training based on machine learning. Firstly, a framework was developed for the concept of internationalized talent training, and an evaluation index system was created for internationalized talent training with four basic elements, highlighting the importance of professional business English training and testing.…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning

Yang¹,

Liu²,

Liu³

et al. 2022

Int. J. Emerg. Technol. Learn.

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Internationalized enterprises conduct business and operation in more than one country. Their complex work processes raise a high demand for the work efficiency of employees. As a result, the enterprises in need of internationalization attach great importance to individual ability and quality when recruiting talents, and need to train talents based on orders. To improve the degree of specialization and employment quality of graduates, it is necessary to effectively evaluate the order-based talent training in internationalized enterprises, which helps to rationalize the training scheme and realize scientific education. However, there are very few studies that quantify the order-based talent training in internationalized enterprises. To fill the gap, this paper evaluates the quality of order-based talent training in internationalized enterprises based on machine learning. Section 2 summarizes the flow of order-based talent training in internationalized enterprises, and establishes an evaluation index system for the training quality, referring to the requirements of internationalized enterprises on the skills, cultural qualities, and professional ethics of talents. The feature data of the evaluation indies were preprocessed through principal component analysis (PCA), which reduces the computing load and increases the computing speed for the order-based talent training quality in internationalized enterprises. Section 3 optimizes the backpropagation (BP) neural network for prediction, and further reduces the dimensionality of the multi-dimensional data on the evaluation indices through locality preservation projection (LPP). The proposed model was proved effective through experiments.

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The Cultivation Quality Evaluation of International Talents Based on Deep Learning

Cited by 2 publications

References 17 publications

A Systematic Review of Big Data Driven Education Evaluation

A Systematic Review of Big Data Driven Education Evaluation

Quality Evaluation of Order-Based Talent Training in Internationalized Enterprises Based on Machine Learning

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