Extracting the Relationships Among Students Based on Accessing Pattern of Digital Learning Attributes

Abstract

A paradigm shift can be expected in the education sector, especially after the COVID-19 pandemic. E-learning systems are being adopted by all the stakeholders as physical meetings are not feasible. Different online learning attributes, such as video conferencing tools, coding platforms, online learning frameworks, digital books, and online videos, are available, which are enhancing the traditional learning methodology. Now, the main challenge for the educationists is to identify how these attributes are utilized by the learners. In this article, we have represented any online class as a social network where students are connected through learning platforms. We have mined the network based on several network measuring parameters to recognize the maneuvering pattern of these digital resources or attributes by the students. We have also proposed a community detection method that would form different groups among the students based on their comfortable learning patterns. As a case study, we have scrutinized the accessing patterns of different digital learning resources by some particular students. The experimental results show the significant relationship between the digital resource accessing patterns of the students with their immediate performance in the test. The satisfactory inquisitive results of our approach would definitely inspire the researchers of interdisciplinary areas to probe further in this domain.