Understanding Learner’s Drop-Out in MOOCs

Alya Itani 1 Laurent Brisson 2, 3 Serge Garlatti 1
1 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_IMTA_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
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.
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Chapitre d'ouvrage
Yin Hujun; Camacho David; Novais Paulo; Antonio J; Tallón-Ballesteros. Intelligent Data Engineering and Automated Learning -- IDEAL 2018, Springer International Publishing, pp.233-244, 2018, Lecture Notes in Computer Science book series (LNCS, volume 11314), 978-3-030-03493-1. 〈10.1007/978-3-030-03493-1_25〉
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https://hal.archives-ouvertes.fr/hal-01953030
Contributeur : Serge Garlatti <>
Soumis le : mercredi 12 décembre 2018 - 15:36:23
Dernière modification le : mercredi 19 décembre 2018 - 15:26:07

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Alya Itani, Laurent Brisson, Serge Garlatti. Understanding Learner’s Drop-Out in MOOCs. Yin Hujun; Camacho David; Novais Paulo; Antonio J; Tallón-Ballesteros. Intelligent Data Engineering and Automated Learning -- IDEAL 2018, Springer International Publishing, pp.233-244, 2018, Lecture Notes in Computer Science book series (LNCS, volume 11314), 978-3-030-03493-1. 〈10.1007/978-3-030-03493-1_25〉. 〈hal-01953030〉

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