The Quality Evaluation of English Talent Training in Universities Based on K-means Algorithm

• Published in: 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) pp. 1 - 7
• Main Author: Li, Yimei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.02.2023
• Subjects: Big Data; Buildings; Communication systems; Education; Integrated circuits; k -means algorithm; Quality assessment; talent training; Training; university English
• DOI: 10.1109/ICICACS57338.2023.10100228

English teaching has an important position in the teaching of all subjects and disciplines in universities. The urgency and importance of cultivating high quality, multidisciplinary and international talents have been mentioned many times in the relevant documents of the Ministry of Education. With the deepening integration of the global economy, people with language, English and vocational skills have a great advantage in acquiring advanced knowledge, understanding advanced foreign ideas and skills, and connecting with the international community. It is evident that in the new era, English teaching in colleges and universities will play an increasingly important role in the cultivation of quality and qualified personnel, and the quality of English teaching in colleges and universities should be monitored and guided by a sound teaching quality assessment system. This paper discusses the current situation and specific problems in assessing the quality of English teaching in colleges and universities and, on the basis of careful comparison and analysis, discusses the principles that should be followed in assessing the quality of English teaching in colleges and universities, namely: advanced teaching management concepts as a guide and emphasis on differentiated teaching assessment. The report also analyses countermeasures to improve the existing assessment of English teaching quality in colleges and universities, which should emphasize the building of faculty, promote the reform of assessment methods, enrich assessment themes and strengthen the support of information technology such as big data. Institutions should also coordinate their activities, provide reasonable protection in terms of systems and funding, and pay attention to communication with employers to enhance the use of social forces to assess the quality of English teaching in higher education. To a certain extent, this study enriches the theoretical research on the assessment of the quality of English teaching in universities and can be used as a reference for strengthening and improving the assessment of the quality of English teaching in universities at this stage.