Sergio Simonian* and Serge Miranda

CIFRE Researcher, Datum Academy and University Cote d’Azur, I3S CNRS Research Laboratory, France and Professor Serge Miranda University Côte d’Azur, Nice, France

*Corresponding Author: Sergio Simonian, CIFRE Researcher, Datum Academy and University Cote d’Azur, I3S CNRS Research Laboratory, France and Professor Serge Miranda University Côte d’Azur, Nice, France

Received: August 09, 2021; Published: November 30, 2021

Abstract

Massively Open Online Courses (MOOCs) have been demonstrating their potential to transform traditional online education since their disruption in 2012 with two major features : instructor tutoring and social networking of learners. However some drawback lies in their completion and student success rate remaining very low. In this article, we are focusing on deep learning applications to analyze MOOC data and generate real-time indicators for learners and online professors to improve MOOC acceptability. For this survey we searched for relevant articles using Google Scholar (during the period 2017-2021) and the ERIC database (period 2019-2021) and selected twenty-one articles that demonstrated deep learning methods for MOOC approach improvement. We categorized them into three areas: “MOOC student final outcome prediction” (twelve articles), “MOOC forum post classification” (six articles), and “MOOC student current/next outcome prediction and recommendation” (three articles). We conclude that section by identifying that most deep learning architectures built for these applications focus on student final outcome prediction and use variants of recurrent neural networks (RNN). We also remark that the most occurring datasets were the Stanford MOOC Posts dataset and OULAD. Then in the second part of this article, we functionally compare these major deep learning platforms for MOOC improvement with the one we are building with France Université Numérique(FUN) named HOOK (Human Open Online Knowledge) platform.

Keywords: MOOC (Massive Open Online Courses); E-learning, Online Learning, Big Data; AI, Machine Learning; Deep Learning

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Citation

Citation: Sergio Simonian and Serge Miranda. “Survey and Comparison of Deep Learning Applications to Improve MOOC Acceptability (The HOOK Project)". Acta Scientific Computer Sciences 2.11 (2021): 52-61.

Copyright

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