In the past two decades, with the globalisation of education, there has been a continuous increase in the diversity of students in Higher Education. This diversity form a basis for a culturally rich environment, although, the cultural and language differences and the diversity in teaching and learning styles also bring challenges. From a university’s perspective, providing the maximum support to overcome these challenges and achieving maximised student engagement would be in its best interest. Recent advances in Big Data and increase in electronically available education data can help in achieving these aims. This paper reports the findings of a preliminary study which applies Big Data analysis methods to analyse education data gathered from learning management systems. The aims was to understand ways to improve student engagement and reduce student dropout. This paper documents the experience gained in this early exploration and preliminary analysis, and thereby provides background knowledge for reporting of data from the formal data collection stage which will be conducted at a later stage of research.
History
Publication title
Lecture Notes in Computer Science 9992: 29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence)
Volume
9992
Editors
BH Kang and Q Bai
Pagination
702-707
ISBN
978-3-319-50126-0
Department/School
Faculty of Education
Publisher
Springer
Place of publication
Switzerland
Event title
29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence
Event Venue
Hobart, Tasmania
Date of Event (Start Date)
2016-12-05
Date of Event (End Date)
2016-12-08
Rights statement
Copyright 2016 Springer
Repository Status
Open
Socio-economic Objectives
Information systems, technologies and services not elsewhere classified