The Use of Data Mining and Automated Social Networking Tools in Virtual Learning Environments to Improve Student Engagement in Higher Education

<p>Virtual learning environments (VLEs) form part of modern pedagogy in education; they contain student usage data that can inform and improve this pedagogy. This paper explores the extent to which data mining and log analysis systems for the Moodle virtual learning environment can improve student course engagement. The paper proposes that a student will complete missed tasks sooner if their use 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 15 week period, messaging one group only when they fell behind. Statistical analysis and comparisons were made between how quickly each group engaged with the missed items. It was found that 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 the published 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.</p>