Exploring Foreign Language Education using Personalized Learning Algorithms and Distributed Systems Based on Big Data Analytics

Abstract

The limitations of traditional methods result in a lack of personalization and real-world immersion in language learning, particularly in foreign language learning skills. It leads to limited conversational practices and slower development. Also, traditional methods fail to adjust the respective needs of every individual learner and varying proficiency levels. To address these kinds of problems and to enhance foreign language education, the present study supports with its unique novel framework called the Cognitive Collaborative Language Optimizer (CCLO) model, which combines the benefits of collaborative filtering, fuzzy cognitive systems, and neighborhood-based recommendation algorithms based on distributed systems and big data analytics (BDA). By using real-time data based on every learner’s performance, preferences, and progress, the CCLO framework adjusts learning experiences for each individual and provides an effective path for language development. While neighborhood-based algorithms form clusters of learners with shared learning paths, on the other hand, collaborative filtering identifies patterns in learner behavior. It suggests education materials that work well for learners with comparable backgrounds. This guarantees a personalized learning experience. Managing uncertainty is an important function in CCLO, and this was performed using fuzzy cognitive systems, where learners commonly prove their partial knowledge of learning. CCLO provides the best experience by adjusting every learner’s needs and offers the benefit of vocabulary, grammar, and conversation practice at the correct time. Finally, the simulation of the suggested CCLO is conducted using a seven-week study based on the Chinese as a Foreign Language (CFL) dataset. The efficacy of the suggested model shows a notable improvement in conversation proficiency. The detailed illustrations and experiments are discussed.