The Evaluation Algorithm of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

Introduction

The use of information processing technology and big data analysis technology for teaching evaluation and resource information scheduling has positive and important significance in improving the quantitative management and planning ability of teaching processes (Zhen, 2021). In recent years, with information technology as the core support and “digital” and “intelligence” as the theme of industry reform, education Big data has become China's national strategy for the first time (Miao, 2021). The Internet has created a more open, free, equal, and interconnected learning space. The original simple interaction of “one-to-one, one-to-many” in the process of teaching and learning has been transformed into a complex “many-to-many” interaction, which intensifies the teaching and learning process (Shang & Liang, 2022). Due to the uncertain, disordered, and multi-level nature of education, the relationship between teaching and learning presents complex system characteristics, and the original simple one-way, linear thinking mode and teaching rules are difficult to explain. Researchers applied the concepts and characteristics of complex systems to the field of education and explained the complexity of learning from two levels of collective behavioural complexity and individual behavioural complexity (Li, 2022). From five aspects—organization, system level, initial value sensitivity, nonlinearity, and emergence—the complexity of individual behaviour shows three characteristics: parallelism, conditional triggering, and adaptation and evolution (Debao et al., 2021). Using complex network analysis, machine learning, simulation, natural language processing, and other new methods to reveal new laws of teaching and learning has also become a research hotspot in the field of international education (Sreedhar et al., 2017).

However, at present, the educational concepts of higher education in China, such as learning-centred education, result-oriented education, innovation and entrepreneurship education, quality education, and quality culture, have not been fully implemented, and there are still many incompatibilities with the requirements of the national action program (Zhang, 2021). Therefore, the organic combination of higher education and information technology in the big data environment has the characteristics of real-time, continuity, dynamism, and comprehensiveness compared to the traditional organic combination of higher education and information technology (Duan, 2022).

In the ancient traditional taxonomy, the classification problem mainly comes from people's cognition of things. People mainly rely on experience and domain knowledge (Peng, 2022). The classification of things is mainly in the qualitative sense, and it is difficult to achieve the quantitative sense (Buslim et al., 2021). However, it is for the classification problems, and the ancient traditional taxonomy based only on experience and field knowledge is powerless (Ravuri & Vasundra, 2020). Mathematics is introduced into taxonomy as a tool, forming a numerical taxonomy with quantitative classification significance (Borlea et al., 2021). After that, with the further increase of the difficulty of classification problems, people began to gradually introduce the related techniques of multivariate analysis into numerical taxonomy, forming the widely used cluster analysis technology today (Liu et al., 2019).

This article studies the evaluation of English teaching ability based on big data analysis. This article proposes an English teaching ability estimation method based on big data fuzzy k-means clustering and information fusion, which achieves clustering and integration of English teaching ability indicator parameters, prepares corresponding teaching resource allocation plans, achieves quantitative planning of English teaching ability evaluation, and achieves accurate evaluation of English teaching ability.