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Dergiler / Turkish Online Journal of Distance Education / 2020 / Cilt: 21 - Sayı: 2

A Machine Learning Based Approach to Enhance Mooc Users’ Classification

Youssef MOURDI
Mohammed SADGAL
Wafa BERRADA FATHI
Hamada EL KABTANE
Sayfa
47–68
Yayın Tarihi
2020-04-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC.
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Temel Bulgular & Veri:Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%.
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Sonuç & Katkı:In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2010 Verileri
⚙️Değişkenler:Distance Education · Dropout · Feature Selection

Abstract

At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners’ experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures.

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Bu Makaleye Atıf Yap

MOURDI, Y., SADGAL, M., FATHI, W. B., KABTANE, H. E. (2020). A Machine Learning Based Approach to Enhance Mooc Users’ Classification. Turkish Online Journal of Distance Education, Cilt: 21 - Sayı: 2, 47–68. https://ajindex.com/articles/137570

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Kaynakça (9)

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