Understanding Learner’s Drop-Out in MOOCs

Abstract

This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this dropout. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider Open-Classrooms. Two critical business needs are identified in this context. First, the accurate detection of at-risk droppers, which allows sending automated motivational feedback to prevent learners drop-out. Second, the investigation of possible drop-out reasons, which allows making the necessary personalized interventions. To meet these needs, we present a supervised machine learning based drop-out prediction system that uses Predictive algorithms (Random Forest and Gradient Boosting) for automated intervention solutions, and Explicative algorithms (Logistic Regression, and Decision Tree) for personalized intervention solutions. The performed experimentations cover three main axes; (1) Implementing an enhanced reliable dropout-prediction system that detects at-risk droppers at different specified instants throughout the course. (2) Introducing and testing the effect of advanced features related to the trajectories of learners' engagement with the course (backward jumps, frequent jumps, inactivity time evolution). (3) Offering a preliminary insight on how to use readable classifiers to help determine possible reasons for drop-out. The findings of the mentioned experimental axes prove the viability of reaching the expected intervention strategies.

Factors affecting student dropout in MOOCs: a cause and effect decision‐making model

“…Furthermore, using traditional methods such as surveys, interviews, focus groups, and observations for data collection are time consuming and limited especially when carried out on a large scale (Chen et al, 2019;Xing et al, 2016). This includes providing an in-depth understanding of the main reasons behind the high dropout rate of MOOC student and its prevention measures (Itani, Brisson, & Garlatti, 2018). Thus, to understand the most critical factors that cause high rate of dropout in MOOCs and enhance the efficiency of MOOC courses, only factors with strong relationships need to be taken into consideration.…”

Aspect-Based Sentiment Analysis in Education Domain

“…Manual data collection was performed in [11] using Web Crawler and APIs to collect restaurant and hotel reviews (2000 and 4000 respectively). As can be seen for the reviewed paper [23], which anticipates the dropout rates in Massive Open Online Courses (MOOC) and the reasons behind [29][30][31][32], the dataset consists of activity traces of 20,142 premium learners in OpenClassroom platform. More specifically, the activity traces were collected from "Create your website with HTML" and "Understanding the Web" courses.…”

“…Finally, a somewhat different approach has been seen in [7], where authors have made use of BERT to construct auxiliary sentences from the aspect and convert Aspect-Based Sentiment Analysis into a sentence-pair classification task. When focusing our attention to education domain research, we can see that in [23] a supervised machine learning based drop-out prediction that uses predictive algorithms (Random Forest and Gradient Boosting) was designed as an automated intervention solution. While, for personalized intervention solutions, Explicative algorithms (Logistic Regression and Decision Tree) are used.…”

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

“…Despite the current increase of interest in MOOCs globally, they still suffer from high dropout rates [3]. The research indicates that the main factors that influence the retention rates in MOOC-like courses are closely connected to course design and learners' behaviour [4].…”