Abstract
The increasing integration of learning analytics (LA) in education creates new opportunities to improve student engagement and academic achievement, particularly in English language teaching (ELT). However, several critical limitations affect the deployment of these technologies, including privacy concerns and the need for advanced predictions about student behavior alongside adaptations to standard educational frameworks. This investigation examines the role of LA in ELT, focusing on how data-driven strategies powered by artificial intelligence (AI), such as deep learning (DL), can enhance student engagement and academic success. The dataset, sourced from a publicly available Kaggle repository, comprises anonymized real-world student interaction metrics, including demographics, engagement patterns, interactions with the learning system, and academic performance. To efficiently preprocess the dataset, numerical features are normalized using Z-score normalization, and features are extracted using term frequency-inverse document frequency (TF-IDF), which assigns weights to each term based on its frequency in a document and rarity across the dataset. DL models, including the weighted white shark optimized deep residual network (WWSO-DResNet), were employed to examine student engagement patterns, predict learning outcomes, and provide personalized learning paths. The findings indicate that students who received personalized learning interventions based on WWSO-DResNet insights performed significantly better in terms of engagement and academic achievement. The performance of the proposed WWSO-DResNet approach was evaluated using metrics such as MAE (0.09), RMSE (0.21), MSE (0.18), and MAPE (0.25). The WWSO-DResNet method proved effective in identifying at-risk students early, enabling preventive interventions. In conclusion, AI-powered LA holds promise for transforming ELT by fostering personalized learning experiences.
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