Developing a Predictive Model for Language Acquisition in Multilingual Environments Based on Cognitive and Social Factors

Abstract

This study comes up with a predictive model of the language acquisition process in multilingual contexts and it is a combination of both cognitive and social factors. Due to the increasingly global world, people tend to be exposed to different languages and thus it is even more important to understand how things work so that one stands a chance to learn languages successfully. The study fills in the gap in the current literature because it develops a data-driven model that combines factors of cognitive abilities and social context to predict the outcomes of language proficiency. A stratified random sample of 200 multilingual participants all aged between 18-30 years was selected. Structured questionnaires, standardized tests of language proficiency, and cognitive tests, were used to collect data which assessed the working memory and the ability to control attention. Statistical analysis, including descriptive statistics, Pearson correlation, and multiple regression, was performed using IBM SPSS (Version 26), and machine learning models using Python in scikit-learn. The data set was divided into training (80%) and testing (20%), with 5-fold cross-validation. Results indicate that language exposure (