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Evaluating critical thinking of English learners using modern technologies and GTMA

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Abstract

The paradigms of teaching and learning are undergoing a rapid transition in the present era of information and communication technology, and this is also true for the study of the English language. Through the use of approaches like flipped learning, blended learning, and a content-based approach, the learners can be given a very suitable environment. However, the most likely option for an efficient learning process is the growth and promotion of critical thinking (CT) in pupils. It encourages kids to reason clearly and make intelligent decisions. The goal of the current study is to analyze and provide an overview of the numerous methods and skills that can help language learners develop their critical thinking. It was determined that the attitudes and knowledge of the teachers play a significant role in the development of the class’s thinking mindset. The improvement of the candidates’ problem solving and thinking skills is greatly helped by improved teaching methodologies and assessment methods. The proposed study has identified a number of features after thoroughly examining the existing research on the development of CT in English language learning, and significant features were selected from them. With the use of the chosen features and the application of the graph theory matrix approach (GTMA), the alternatives were ranked in order of preference. The current investigation has taken this into account and provided a thorough summary of the information currently available on the research topic. The research shows a variety of ways to analyze text so that students can come up with new ideas about the topic.

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Funding

1. Foreign Language Research Joint Project of Social Science Foundation of Hunan Province (No. 2021WLH35). 2. Teaching Reform Project in colleges and universities of Hunan province (No. HNJG-2020-1208).

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Correspondence to Fengying Peng.

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Peng, F. Evaluating critical thinking of English learners using modern technologies and GTMA. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08127-7

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