Abstract:

Virtual learning environments (VLEs) provide a fundamental contribution to modern pedagogy in education; they contain student usage data that has potential to inform and improve this pedagogy. In earlier research the authors explored how the development of data mining and log analysis systems for the Moodle virtual learning environment might improve students’ course engagement. They proposed 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. In this paper the authors extend their research to explore how the development of data mining and log analysis systems for the Moodle virtual learning environment might encourage course designers’ future engagement with data analysis methods for the evaluation of course resources.

The paper hypothesises that presenting a simple-to-use data mining and visualisation tool to course designers increases their future acceptance of data mining technology for informing course design with a longer-term intent and that this will improve the quality of the online learning experience ultimately improving student engagement. Exploring the hypothesis required the development of MooLog – a tool that extracts and presents summative information on VLE course utilisation. To ascertain if the acceptance of data mining for course evaluation could be improved, surveys were used before and after a demonstration of MooLog to a group of course designers. The pre-demonstration survey assessed existing planning and evaluation processes. The post-demonstration survey collected evaluations of the relevance of the information provided by MooLog and the likelihood of the software being used to evaluate course effectiveness. The results of the study established that many designers currently do not use data analysis as a method of informing course improvement and there was evidence to suggest the MooLog demonstration significantly increased acceptance of the potential of data mining. The findings within the thesis show that educational data-mining has the potential to improve the quality of VLE mediated education; it identifies opportunities to raise course designers’ acceptance of data mining to improve the validity and quality of course evaluation.

Keywords:

Virtual Learning Environments, Course Design, Higher Education, Social Networks.