Adaptive Mobile-Assisted Language Learning: A Bayesian Framework Study for Optimal Learning Content Selection

With the development of mobile technology, mobile-assisted language learning (MALL) is increasingly becoming a mainstream method. This study explores using a Bayesian framework to optimize learning content selection to meet learners' diverse and individual needs. The researcher briefly introduces Bayes' Theorem and explores prior and posterior selection and modeling. Considering the challenges of learner diversity and resources, the researcher also discusses how these challenges affect adaptive content selection. Based on these theoretical foundations, a Bayesian model of adaptive MALL is constructed, and the design of data processing, parameter selection, and adaptive selection algorithms are described. The effectiveness and advantages of the Bayesian framework in optimizing content selection are validated through a case study of Duolingo, a selected MALL application. The study concludes with a summary of key findings, recommendations for practical MALL applications, and directions for future research and possible challenges. Specifically, the results demonstrate that the Bayesian framework significantly enhances the adaptability and personalization of content selection in MALL, evidenced by improved learner engagement and efficiency in the case study of Duolingo. These results highlight the potential of the Bayesian approach in revolutionizing personalized learning experiences in digital platforms.