Treffer: A Study of Optimization Algorithms for English Vocabulary Memory and Review Plans: Integrating the Principles of Forgetting Curve and Memory Reinforcement

Mastering English vocabulary is a crucial steppingstone towards achieving fluency in the language. Building a strong English vocabulary foundation is essential for spoken and written fluency. This inefficiency can lead to wasted time reviewing known words or forgetting recently learned ones. This work proposes a novel deep learning-based system for personalized English vocabulary learning: the Contextual Awareness Controlled Generative Adversarial Network with Forget Curve & Memory Reinforcement (CACGAN-FCMR-PEVL) model. CACGAN-FCMR-PEVL leverages deep learning to create a personalized optimization algorithm, analyzing user data and incorporating forgetting curve principles. It utilizes a self-attention mechanism and a conditional generative adversarial network (CGAN) to generate personalized vocabulary maps and review schedules, optimizing memory retention through memory reinforcement techniques. Hyperparameter tuning with the Binary Waterwheel Plant Optimization Algorithm (BWpOA) further enhances the model's effectiveness. When compared to other existing methods, the proposed CACGAN-BWpOA-FCMR-PEVL model shows 53.55%, 31.703% and 32.403% higher Vocabulary Recall and 49.46%, 58.06% and 30.98% higher Word Similarity, indicating superior learning and retention. It also attains 47.42%, 56.701% and 73.21% higher Review Interval Prediction Accuracy (RIPA), which suggests effective personalization based on forgetting curves. These findings suggest that CACGAN-BWpOA-FCMR-PEVL effectively personalizes vocabulary learning, leading to improved memory retention.