MOOC dropout prediction using machine learning techniques: Review and research challenges

Abstract

MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.

Understanding Dropouts in MOOCs

“…Whitehill et al (2015) design an online intervention strategy to boost users' callback in MOOCs. Dalipi et al (2018) review the techniques of dropout prediction and propose several insightful suggestions for this task. What's more, XuetangX has organized the KDDCUP 2015 2 for dropout prediction.…”

Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs

“…In this section, we will briefly discuss the studies related to MOOC dropout prediction. Dalipi et al [6] performed a survey of studies conducted for MOOC dropout prediction. They observed that different research groups tended to choose different data sources and extract different features.…”

“…However, this process is difficult because of several reasons. First, the MOOC learners may drop out for different reasons [6] . Some learners may only attempt to experience MOOC as a new type of learning.…”

Ontology-Based Design of the Learner’s Knowledge Domain in Electrical Engineering

“…However, such studies as [4], [5] note that the new learning technology faces such challenges as low completion and retention rates, certification, graduation, and verification of student's identity, and unsuitability for complex and engineering education. Nevertheless, this process continues expanding despite declining educational attainment in these spheres [6].…”

“…Many research papers are devoted to the exploration of motives that drive students to enrol in one or another e-course and the reasons of their failure. In [4], lack of student's motivations is identified as an influential factor in preventing Electrical, Control and Communication Engineering ________________________________________________________________________________________2019, vol. 15, no.…”