AI and Machine Learning in Predictive Analytics for Language Learning Success
Adlin Jerusha J, R. Rajakumari · 2024
This research examines the use of predictive analytics in assessing language learning ability in relation to artificial intelligence (AI) and machine learning (ML). Data was collected from language learners and teachers from various educational institutions using an online survey. The data was obtained via a quantitative research methodology. The study aimed to evaluate the predictive capabilities of AI and ML models in language learning by identifying key criteria that influence the outcomes. Statistical analyses were conducted using SPSS to ensure validity and reliability. The conducted research included descriptive statistics, Chi-Square testing, analysis of variance, and regression analysis. The findings indicate that the proficiency in acquiring a new language is significantly influenced by factors such as internal drive, study routines, and prior knowledge. The language learning results predicted by Machine Learning (ML) and artificial intelligence (AI) models were very precise, providing valuable insights for the development of more customized educational techniques. Striking a balance between technological resources and traditional instructional approaches is crucial, and this study presents effective strategies for incorporating predictive analytics into language learning programs. These results provide more proof that Machine Learning (ML) and Artificial Intelligence (AI) may enhance language learning by providing individualized feedback and adaptive learning paths. This, in turn, increases student engagement and academic performance. Ultimately, the essay proposes methods for policymakers and educators to use predictive analytics in order to enhance students' language proficiency. Additionally, it highlights prospective areas of exploration for future study on the capabilities of artificial intelligence and machine learning in educational settings.