Deep learning based personalized English listening learning path recommendation algorithm

A crucial aspect of learning a language and listening is competency in English. A information on users' vocabulary complexity, pronunciation, proficiency level,speed reading, topic relevance, objective in learning, data performance and preferences, To evaluate the DL -based personalized learning path recommendation algorithm for English listening instruction can make customized learning path recommendations. In order to enable individualized English listening instruction, this study presents the CNN-PR algorithm, which is based on CNN. The CNN-PR system uses deep learning and data analytics to deliver personalized listening recommendations based on every learner vocabulary complexity, topic relevance,skill level, and reading speed. We assess the algorithm's efficacy using a battery of tests and analyses, considering variables such as adaptability, learner satisfaction, and recommendation rating. The algorithm's capacity to select varied and pertinent listening resources improves student engagement and comprehension, as demonstrated by the results. On the other hand, we recognize difficulties such as algorithmic biases and the need for constant improvement. In the end, the CNN-PR algorithm shows promise as an adaptive learning strategy in language learning, advancing the development of tailored and successful language learning encounters. We used eight high-performance iterations and chose the best four. When compared to other current methods, the suggested algorithm performs under these features with predicted model accuracy, precision, and recall levels: vocabulary complexity accuracy of 97.32 %, proficiency level accuracy of 92.72 %, topic relevance accuracy of 91.62 %, speed reading accuracy of 95.34 %, and the highest CNN-PR accuracy of 97.32 % overall. The experiment's findings show that the study in this paper can, to some extent, recommend the most effective learning paths for the intended users, enhance the accuracy of the suggested resources, and enhance the users' learning experience and quality.