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Understanding Learner’s Drop-Out in MOOCs

  • Alya Itani
  • Laurent BrissonEmail author
  • Serge Garlatti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

Abstract

This paper focuses on anticipating the drop-out among MOOC learners and helping in the identification of the reasons behind this drop-out. The main reasons are those related to course design and learners behavior, according to the requirements of the MOOC provider OpenClassrooms. 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.

Keywords

Learning analytics Supervised machine learning Massive Open Online Courses Modeling drop-out 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.IMT Atlantique, Lab-STICC, UBLBrestFrance

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