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With information technology becoming increasingly common, manual data analysis has become outdated. Nowadays, hierarchical progressive methods are widely applied in various fields of society, and more and more types of hierarchical progressive methods are being developed (L.Liu et al., 2021). The UK makes the most frequent use of hierarchical progressive methods, which are widely used in the production industry (Díaz et al., 2021). For example, production industries such as automobile production and food production. Layered methods are also used in other fields, such as finance. Whenever there is a large amount of data to be processed, there is always a hierarchical method (Cavalcanti et al., 2021). By implanting the hierarchical progressive method (Hou et al., 2019) on the computer side of this method (Cheng,Ma et al., 2022), the data from the main control end is sorted and processed (Cheng, Wei & Cheng et al., 2022). The data is stored internally and retransmitted to the user for use. Compared with manual data integration (Liu et al., 2020), this can not only directly replace manual labor (Cheng, Yang et al., 2022) but also minimize data processing time and remaining additional costs (Blazar et al., 2020). In addition, the manual process of data integration often leads to data omissions and erroneous data judgments, which can affect the judgment of researchers (Hendrix et al., 2003). The basic purpose of the layered method is to achieve the most efficient data processing capability by incorporating a layered system, thereby saving a lot of money and extending the service life.
In today’s fast-moving times, the field of education is developing at an increasingly rapid pace (Liu et al., 2022). During the development of educational reform, the direction and results of educational reform are often poorly applied within the actual classroom (Cai & Liu, 2022). As educational reform is confronted with uncontrollable factors for students, who are by nature individuals with individual opinions and independence of thought, the factors of change are too great to avoid misanalysis (Berson et al., 2022). Curriculum reform in English at the university level has, therefore, been a major direction for researchers to tackle in the process of educational reform (Gregoire et al., 2021).
As the educational ecosystem is a joint ecosystem that includes the classroom life environment, a powerful modeling system is needed instead of a human being to classify the data as a whole, as opposed to the individual environment within the classroom (Franz et al., 2022). From the perspective of the modeling system itself, when analyzing and processing the data collected concerning curriculum reform, data blocking problems often occur, leading to data processing failures (Ngwenya et al., 2020). From the perspective of the environment in which the model system operates, variable external factors such as temperature can also have varying degrees of impact on the overall system operation process (Hübner et al., 2021). Therefore, it is not just a matter of building the model using a hierarchical approach but also of ensuring that the system performs as well as possible in the face of large amounts of data (Njoroge & Gathungu, 2013). Therefore, it is important to consider all aspects of building a hierarchical and progressive modeling system in the education ecosystem to maximize the capabilities of the modeling system (Sung & Vong, 2021).