• Sep 17, 2018 News!Welcome to 2019 5th International Conference on Knowledge and Software Engineering (ICKSE 2019), which will be held in Prague, Czech Republic during March 2-4, 2019.   [Click]
  • May 31, 2018 News!Vol.4, No.1 has been published with online version.   [Click]
  • Dec 25, 2017 News!Welcome to 2018 4th International Conference on Knowledge and Software Engineering (ICKSE 2018), which will be held in Istanbul, Turkey during March 24-26, 2018.   [Click]
General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Editor-in-chief
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2017 Vol.3(2): 43-47 ISSN: 2382-6185
doi: 10.18178/ijke.2017.3.2.085

Prediction of Students’ Dropout in MOOC Environment

Rahila Umer, Teo Susnjak, Anuradha Mathrani, and Suriadi Suriadi
Abstract—Massive Open Online Courses (MOOCs) is a new approach to online learning which provides a platform for learning in highly scalable and flexible manner. Many higher education institutes are developing and delivering a wide range of such courses. MOOCs are gaining popularity, however they are prone to early dropout and low completion rate. Students registering in MOOCs are different than traditional higher education students in terms of age, education background and motivation. These differences pose challenges in understanding their intent in registering for these courses. In order to improve students’ retention in online learning environment, it is necessary to predict the likelihood of dropout. Timely and proper academic intervention could help struggling students during the course. In this paper, we used MOOCs dataset as a case study to predict student dropout based on the count of online activities. We used classification methods that have been utilized in the field of education domain and are suitable for imbalanced dataset. The machine learning algorithms used in our experiments are: Naive Bayes, Random Forest, Logistic Regression and K Nearest Neighbor. Our results show that techniques used in this study are able to make predictions of dropout, and Logistic Regression outperformed other classifiers with maximum accuracy.

Index Terms—Learning analytics, MOOCs, machine learning, data mining, prediction.

Rahila Umer, Teo Susnjak, and Anuradha Mathrani are with Institute of Natural and Mathematical Science, Massey University, Auckland, New Zealand (e-mail: r.umer@massey.ac.nz, T.Susnjak@massey.ac.nz, A.S.Mathrani@massey.ac.nz). Suriadi Suriadi is with Queensland University of Technology, Brisbane, Australia (e-mail: s.suriadi@qut.edu.au4).

[PDF]

Cite: Rahila Umer, Teo Susnjak, Anuradha Mathrani, and Suriadi Suriadi, "Prediction of Students’ Dropout in MOOC Environment," International Journal of Knowledge Engineering vol. 3, no. 2, pp. 43-47, 2017.

Copyright © 2008-2016. International Journal of Knowledge Engineering. All rights reserved.
E-mail: ijke@ejournal.net