Abstract—Virtual learning environments (VLEs) form part of modern pedagogy in education; they contain student usage data that has the potential to inform and improve this pedagogy. The question this paper explores is how might the development of data mining and log analysis systems for the Moodle virtual learning environment improve students’ course engagement? The paper proposes that a student will complete missed tasks sooner if their utilisation of the VLE is automatically tracked and electronic prompts are sent when VLE activities are missed. To explore and test the hypothesis a software tool, MooTwit was developed to contact students when they fell behind in their VLE study. To establish if student timely engagement improved the study used MooTwit with two groups of students over a period of 15 weeks, messaging one group only when they fell behind. Statistical analysis and comparisons were made between how quickly each group engaged with the missed items. Using MooTwit to track and contact students did influence the timeliness of their engagement with the VLE activities. Specifically, the results suggest by direct messaging a student to engage with missed material, they completed missed activities closer to required completion date. The findings within the thesis show that educational data mining has the potential to improve pedagogy in VLE linked education offering opportunities to increase timely engagement and to raise course designers’ acceptance of data mining to improve the validity and quality of course evaluation.
Index Terms—Virtual learning environments, student engagement, higher education, social networks.
Cite: Stephen Smith, David Cobham, and Kevin Jacques, "The Use of Data Mining and Automated Social Networking Tools in Virtual Learning Environments to Improve Student Engagement in Higher Education," International Journal of Information and Education Technology vol. 12, no. 4, pp. 263-271, 2022.Copyright © 2022 by the authors. 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 (CC BY 4.0).