University blended English teaching based on big data analytics

Abstract

English language teaching varies with the universities and faculties for improving student knowledge through adaptability. In improving the adaptability features, multiple practices are blended based on previous outcomes. The outcomes are considered through the accumulated big data for leveraging student performance. This article introduces a Blended Model using Big Data Analytics (BM-BDA) to provide an upgraded teaching environment for different students. This study applied learning analytics and educational big data methods for the early prediction of students’ final academic performance in a blended model for English teaching. The model aims at rectifying the performance inaccuracies observed in the previous sessions through the pursued teaching methods. Furthermore, the identification is pursued using teaching model classification and its results over students’ performance. The classification is pursued using conventional classifier learning based on different inaccuracies. The inaccuracy in teaching efficiency using the implied model is classified for different types of students for step-by-step model tuning. The tuning is performed by inheriting the successful implications from the other methods. This improves the inclusion and blending of the diverse method to a required level for teaching efficiency. The successful blending method is discarded from the classification process post the outcome verification. This requires intense data analysis using diverse student performance and implied teaching methods.