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Opportunities for Moodle data and learning intelligence in Virtual Environments

Corsatea, BM and Walker, S (2015) Opportunities for Moodle data and learning intelligence in Virtual Environments. In: UNSPECIFIED, ? - ?.

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Abstract

© 2015 IEEE. Virtual Learning Environments (VLEs) have increased immensely in their popularity worldwide. In the UK, every higher educational institution is using one or more VLEs to assist teaching. Because of the growing number of online interactions with these environments, students are leaving trails of information regarding their online activities. This data is descriptive of students' behavior and has the potential of becoming a goldmine of educational data. This research is a case study of applying data mining and machine learning algorithms to the data resulting from the usage of Moodle at the University of Essex in the School of Computer Science and Electronic Engineering. It was the first department to introduce Moodle in 2006. Moreover, with every year an increasing number of academics have a preference for using Moodle as a tool for sharing and exchanging information within a course. The resulting Moodle data is enriched by that available from the students' records. The methodology discussed explores the relationship between students' performance and Moodle usage. The process for mining e-learning data is described in detail, starting with the preprocessing step to the evaluation and discussion of results. The approach uses clustering to discover the natural groupings of patterns in the dataset.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2015
Subjects: L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 09 Jan 2016 16:15
Last Modified: 17 Aug 2017 17:29
URI: http://repository.essex.ac.uk/id/eprint/15751

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