Neural Networks and English Learning: Design and Implementation of Personalized Learning Paths

An instructional approach identified as a personalized learning path aims to adjust regulations to the particular interests’ necessities, talents, and skills of each pupil. Research on personalized path recommendation is critical for the development of sophisticated learning systems. The challenge emerges from the difficulty in developing personalized learning paths that properly fit every pupil’s unique characteristics and learning needs. This paper examines personalized educational routes and how deep learning (DL) algorithms can be used to construct them, with a particular emphasis on English language learning. This research proposed a novel fruit fly optimizer-tuned adjustable recurrent neural network (FFO-ARNN) to employ the students learning performance and effectively personalized English learning. For this study, gathering data on student’s demographics and English proficiency levels through assessments and educational records, in that Data on the linguistic competency of English linguistic Learners (ELLs) in grades 8–12 was collected. The data were pre-processed using tokenization for the obtained data. Term Frequency-Inverse Document Frequency (TF-IDF) is a method using extracts the feature from pre-processed data. These personalized learning approaches encourage self-directed learning and independence while increasing student engagement and academic achievement. The proposed method is implemented using Python software. In comparative analysis, the proposed method evaluates various performance metrics. The results show that the proposed method achieved superior performance in personalized learning paths in English learning. This study demonstrates how DL has the potential to revolutionize language instruction by providing customized solutions for every student.