The Construction of a Practical Teaching Platform Based on a Tag-based Talent Search Algorithm
Dawei Zhang, Tengfei Wang, Meifan Ma
International Journal of Computational Linguistics Research, Vol. 15, No. 4, 2024.
DOI: https://doi.org/10.6025/ijclr/2024/15/4/146-152
Link: https://www.dline.info/jcl/fulltext/v15n4/jclv15n4_3.pdf
Abstract:
This research aims to explore the construction of a practical teaching platform for
engineering applied talents based on the tag-based talent search algorithm. By building
a practical teaching platform for engineering students and using the tag-based talent
search algorithm, personalized assessment and matching of students can be achieved
to enhance their practical abilities and employability. This study analyzes the
requirements and design principles of the practical teaching platform, proposes a
platform architecture based on the tag-based talent search algorithm, and validates
it through practical case studies. The research finds that the practical teaching platform
based on the tag-based talent search algorithm is of great significance for enhancing
the practical skills of engineering students. This approach provides students with
more internship opportunities and enhances their competitiveness in the job market.
Moreover, the platform allows students to choose suitable internship projects based
on their interests, enabling the best career development. Additionally, the platform
serves as a high-quality talent supply channel for enterprises, promoting in-depth
cooperation between academia and industry. The results of this research have practical
significance for promoting innovation and improvement in cultivating applied talents
in engineering.