Predicting Student English Academic Performance by using Data-Driven Multiple Regression Analysis

Interactive English teaching has evolved as a crucial method to boost learner engagement, language acquisition, and communication competence. Current systems tend to lack a rational means to quantitatively assess the wide range of factors that affect learning outcomes. Conventional models of instruction are highly dependent on subjective observations and group-type instructional strategies, which may be ineffective in responding to individual differences among learners and the dynamic processes involved in classroom interactions. To solve these issues, this study suggests the use of a Multiple Regression Model to examine and forecast the impacts of different interactive teaching elements—like multimedia utilization, student engagement, and instant feedback—on English learning achievement. The suggested model identifies in a systematic manner the most influential factors that contribute to successful learning, allowing instructors to maximize their teaching approaches. An experimental study was conducted to compare traditional evaluation techniques with the proposed regression-based model. The results indicate that the multiple regression approach provides a more accurate and exhaustive understanding of the relationships between teaching variables and learning outcomes. Comparative analysis shows that the proposed model performs better than traditional methods in predicting student achievement and adapting interactive teaching strategies. This study showcases the potential of data- driven models in furthering the effectiveness and personalization of interactive English language training.