Abstract
As big data technology continues to penetrate the field of education, the assessment of English education is at a pivotal moment of transformation. Traditional assessment methods, by overlooking the rich data of learning processes and individual differences, fail to meet the demands of modern educational evaluation. This study aims to explore the application of big data analysis in evaluating and enhancing the learning outcomes of English education. Initially, it introduces the revolutionary significance of big data analysis for transforming English education assessment methods, emphasizing the customization of personalized learning pathways and the data support for educational decisions. Subsequently, the paper analyzes the deficiencies in current research methodologies, such as the disconnect between analysis models and educational theories, and the lack of precision in data processing. Building on this, the paper proposes a novel assessment method based on the Graph Attention Network (GAT)-Transformer, which can more accurately analyze and evaluate English learning outcomes, particularly suitable for dealing with complex learner interaction data. Moreover, by integrating the inquiry community theory, this study constructs a framework for improving English education outcomes, based on the interaction of three core elements: teaching presence, social presence, and cognitive presence, and proposes specific enhancement strategies. This research not only theoretically enriches the application of big data in the assessment of English education but also provides practical guidance for educational practitioners.
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