Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review

Elliot Mbunge, John Batani, Racheal Mafumbate, Caroline Gurajena, Stephen Fashoto, Talent Rugube, Boluwaji Akinnuwesi, Andile Metfula

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Predicting student dropout is becoming imperative in online learning platforms. Before COVID-19, predicting student dropout was systematically done manually. Therefore, there is a need to analyse students’ behaviour, cognitive learning styles, and other metacognitive patterns of learning in real-time from available data repositories to reduce student dropout and subsequently develop robust strategies for instructional design and remedial interventions to enhance student success and retention. In this study, we present a comprehensive review of deep learning models applied to predict student dropout in online learning platforms. In addition, challenges and opportunities associated with online learning are presented in this study. The study revealed that convolutional neural networks, recurrent neural networks, long short-term memory and bidirectional long short-term memory have been predominantly used to predict student dropout using predictors such as course assessments, socio-economic, access to online resources, personal skills and course attributes. However, the study revealed that the psychological state of students was not taken into consideration by many authors, yet it impacts students’ learning outcomes and assists policymakers in providing remedial interventions. Therefore, future work can delve deeper into the integration of psychological attributes such as stress, anxiety, attitude towards studying, student interests and counselling sessions to predict student dropout during disasters and health emergencies.

Original languageEnglish
Title of host publicationCybernetics Perspectives in Systems - Proceedings of 11th Computer Science On-line Conference, CSOC 2022, Vol 3
EditorsRadek Silhavy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages212-231
Number of pages20
ISBN (Print)9783031090721
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event11th Computer Science On-line Conference, CSOC 2022 - Virtual, Online
Duration: 26 Apr 202226 Apr 2022

Publication series

NameLecture Notes in Networks and Systems
Volume503 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference11th Computer Science On-line Conference, CSOC 2022
CityVirtual, Online
Period26/04/2226/04/22

Keywords

  • Deep learning
  • Massive open online courses
  • Prediction
  • Student dropout

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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