Learning analytics in the EFL classroom: Technology as a forecasting tool

Abstract

Ideal e-learning management systems (eLMSs) offer many applications given their huge dataset. They are a repository of information needed for the smooth functioning of the teaching-learning process in tech-enabled spaces. Databases created in these analytical systems permit not only the retrieval of data but also the updation of changing learning and pedagogical needs. This study aims to provide insights into predictive educational benefits to EFL classrooms as a result of the application of analytic methods by using grades in final exam scores of 25 EFL female learners of an undergraduate course at Prince Sattam bin Abdulaziz University (PSAU), Saudi Arabia. The study also analyzed the learning behaviours on Blackboard. Results show that lesson videos have the highest hit out of the three parameters (lesson videos, textual notes, practice exercises) offered on the e-learning platform (BlackBoard), followed by practice questions, and the least visitations were registered for the textual notes. Correlation between the learning videos (related to the three literature lessons taught) and final grades showed statistical significance at 95% confidence level, in addition to the factor of topic completion by the learners being a highly influencing factor in final grades. As a predictive model, Learning Analytics (LA) established that videos are a preferred way of learning English in the Saudi EFL context. The results of the study are likely to be of significance to course developers, teachers, and learners of EFL.