Tsoni, R.; Paxinou, E.; Gkoulalas-Divanis, A.; Karapiperis, D.; Kalles, D.; Verykios, V.S. Exploiting Properties of Student Networks to Enhance Learning in Distance Education. Information2024, 15, 234.
Tsoni, R.; Paxinou, E.; Gkoulalas-Divanis, A.; Karapiperis, D.; Kalles, D.; Verykios, V.S. Exploiting Properties of Student Networks to Enhance Learning in Distance Education. Information 2024, 15, 234.
Tsoni, R.; Paxinou, E.; Gkoulalas-Divanis, A.; Karapiperis, D.; Kalles, D.; Verykios, V.S. Exploiting Properties of Student Networks to Enhance Learning in Distance Education. Information2024, 15, 234.
Tsoni, R.; Paxinou, E.; Gkoulalas-Divanis, A.; Karapiperis, D.; Kalles, D.; Verykios, V.S. Exploiting Properties of Student Networks to Enhance Learning in Distance Education. Information 2024, 15, 234.
Abstract
Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.
Keywords
distance learning; learning analytics; social network analysis
Subject
Social Sciences, Education
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.