Research on the Analysis of students’ English Learning Behavior and Personalized Recommendation Algorithm based on Machine learning

Abstract

The goal of this study is to create a personalized recommendation system for English learning resources by analysing students’ English learning behaviors using a Generalized Regression Neural Network (GRNN). For efficient language acquisition, tailored educational support is essential due to the diversity of students’ linguistic backgrounds and learning demands. Performance ratings, personal preferences, and the amount of time students spent on various content categories were among the data gathered for this study on how students interacted with English language learning materials. Our initial analysis of the patterns and discrepancies in the learning behaviors of the pupils involved the use of the GRNN model. Strong insights into the correlations between various aspects and learning results were obtained by the neural network, which was especially well-suited for this investigation due to its affinity for handling non-linear interactions and its low need for preprocessing data. These observations led us to create an individual recommendation engine that recommends educational resources and activities based on each user’s learning preferences and skill level. Using a varied set of students, a controlled study was conducted to assess the efficacy of the individual suggestions. Comparing the preliminary findings to conventional, non-personalized methods of learning, efficiency in learning and student engagement have significantly improved. In addition to showcasing GRNN’s potential for educational applications, this work offers an adaptable framework for adaptive learning systems across a range of academic fields.