Enhancing Feedback Mechanisms in English Language Assessments Through Deep Learning Model

In the realm of English language assessments, optimizing feedback mechanisms is crucial for enhancing learning outcomes and learner engagement. This paper therefore presents a new approach of increasing feedback enhancement by incorporating a Double Integral Transformer (DIT) model within the assessment process. The DIT model is an improved version of the regular Transformer architecture which employs two, or rather dual integral attention modules, to handle both the local and global contextual semantics of the language use more efficiently. With help of this model, the feedback process can be more accurate, accurate and individual for each learner. The Double Integral Transformer evaluates linguistic data by employing double attention layer that combines data at the syntactic and semantic level to enhance the quality of feedback given accurately. It improves the recognition of learners' strengths and weaknesses concerning the language in question and provides customized feedback that is apparently more accurate. The enhanced feeback mechanism reduces problems that are associated current assessment practices: for example, general feedback and lack of context awareness. In this study, the author presents the empirical support for the effectiveness of the DIT model to enhance the quality of the feedback is a major contribution of this study as it results in better learner performance and satisfaction. In this respect, the study advances the area's knowledge by demonstrating how novel types of Transformers can be integrated into educational technology systems while providing insights for possible changes to language assessments procedures.