Effectiveness Assessment and Optimization of Cross-Language Comparative Learning Algorithms in English Learning

Abstract

This study looks into the usefulness of cross-language comparison learning algorithms for enhancing English language acquisition among adult learners from various linguistic origins. In this study, two different algorithms, Algorithm A and Algorithm B, were systematically assessed to determine their impact on two critical components of language learning: listening comprehension and spoken fluency. A group of people including 100 adult learners participated in the study, taking exams customized to measure their proficiency in listening comprehension and speaking fluency. The evaluation indicated significant disparities in the efficacy of the two algorithms. Algorithm A outperformed Algorithm B, with higher mean scores in both comprehension and fluency evaluations. The results highlight the potential of optimized cross-language comparative learning algorithms to improve language learning outcomes, particularly in the context of English language acquisition. These algorithms show promise in meeting the different requirements and preferences of English language learners by leveraging computational approaches and multilingual data to effectively scaffold language learning processes. Furthermore, the study emphasizes the need for additional research to improve algorithmic designs and assess the long-term competence outcomes related to the usage of cross-language comparative learning algorithms. Embracing new technologies provides promising prospects to improve the effectiveness of English language instruction, encourage linguistic variety, and prepare students to succeed in an interconnected global society.