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The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

Hughes, G and Dobbins, C (2015) The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs). Research and Practice in Technology Enhanced Learning (RPTEL): Special Issue on Emerging Trends for Open Access Learning, 10 (10). ISSN 1793-7078

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The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data.

Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysis

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Springer Singapore
Date Deposited: 16 Oct 2015 10:18
Last Modified: 04 Sep 2021 13:57
DOI or ID number: 10.1186/s41039-015-0007-z
URI: https://researchonline.ljmu.ac.uk/id/eprint/2021

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