Advancing Adaptive Assessment in English Language Teaching: A Deep Learning-based Approach within Intelligent Tutoring Systems
A. Greeni, Preeti Chitkara, Punit Pathak, Myagmarsuren Orosoo, Manikandan Rengarajan, B Kiran Bala · 2024
The integration of deep learning techniques into Intelligent Tutoring Systems (ITS) is a unique strategy to advance adaptive assessment in English Language Teaching (ELT) presented in this research. Personalised learning experiences are hampered by the lack of flexibility of traditional ELT assessment systems. The efficacy of current methods is limited because they find it difficult to adjust evaluations to the skill levels and learning paths of specific students. On address this, provide an adaptive assessment system based on deep learning that modifies assessment problems in real time according on performance data. Deep learning algorithms can detect minute trends in language usage and understanding by examining enormous volumes of student data. This allows them to provide more precise and thorough feedback. Using deep neural networks trained on a massive corpus of language data, this study creates models that can evaluate several facets of language competency. According to preliminary findings, this method greatly increases the efficacy and flexibility of assessment in ELT. The system can offer tailored learning experiences and targeted interventions based on ongoing analysis of student performance, which improves learning outcomes and boosts student engagement. By providing knowledge about the possibility of data-driven techniques to revolutionize language learning and assessment processes, this research adds to the increasing body of literature on the convergence of education technology and deep learning.