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Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses

Al-Shabandar, R, Hussain, A, Laws, A, Keight, R and Lunn, J (2017) Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses. In: Neural Networks (IJCNN) . (2017 International Joint Conference on Neural Networks (IJCNN 2017), 14 May 2017 - 19 May 2017, Anchorage, Alaska, USA).

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With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click steam actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest.

Item Type: Conference or Workshop Item (Paper)
Subjects: L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: IEEE
Date Deposited: 07 Feb 2017 10:02
Last Modified: 13 Apr 2022 15:15
URI: https://researchonline.ljmu.ac.uk/id/eprint/5452
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