Analyzing the Cognitive Process Dimension and Rate of Learning to Identify the Slow Learners in e-Learning

Abstract

The advancement of Internet technology has expanded the horizon of face-to-face classroom learning environments to an open, borderless learning space that is no longer curbed to the walls of a classroom. E-Learning encompasses all forms of electronically supported teaching and learning. Asynchronous e-Learning has the potential to be customized to the unique needs of each learner. Despite the possible benefits of e-Learning, the experience of educators confirms that there are many students who have lower rates of learning and require special attention and assistance in digital learning. These slow learners, as with classroom learning, also constitute a noticeable part of the student community in the e-Learning environment. Over the past decade, rapid developments in the field of big data and data analytics have offered opportunities to discover useful insights from massive volumes of educational data. In this paper, the authors have explored the possibilities in identifying and supporting slow learners in e-Learning, which will bring learning satisfaction and academic improvement. Data mining of log files from a Learning Management System (LMS) can have the power to support, challenge, and reshape current educational practices in e-Learning. The potentials of Machine Learning (ML) and Educational Data mining techniques can be employed to classify these learners based on the rate of learning and assessments conducted. An intelligent personalized remedial instruction system that addresses each learner's learning necessities and preferences will help slow learners to reach their optimum levels in the e-Learning situation and will ensure the best quality of education.