Journal of Soft Computing Paradigm
IRO Journals
Volume 2 — Issue 3 — September 2020

MOOCVERSITY - Deep Learning Based Dropout Prediction in MOOCs over Weeks

https://doi.org/10.36548/jscp.2020.3.001
Vignesh Muthukumar
Department of Information Technology, S.S.N. College of Engineering, Anna University, Chennai, India
N. Bhalaji
Associate Professor Department of Information Technology, S.S.N. College of Engineering, Anna University, Chennai, India
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How to Cite

Muthukumar, Vignesh, and N. Bhalaji. 2020. “MOOCVERSITY - Deep Learning Based Dropout Prediction in MOOCs over Weeks”. Journal of Soft Computing Paradigm 2 (3): 140-52. https://doi.org/10.36548/jscp.2020.3.001.
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Keywords

— Deep Learning
— Dropout Prediction
— MOOCs
— Multilayer Feedforward Neural Network
— Clickstream data analysis
— Personalized student performance
— Student Attrition
— Tomek Links
Published: 27-06-2020

Abstract

Massive Open Online Courses (MOOCs) has seen a dramatic increase of participants in the last few years with an exponential growth of internet users all around the world. MOOC allows users to attend lectures of top professors from world class universities. Despite flexible accessibility, the common trend observed in each course is that the number of active participants appears to decrease exponentially as the week's progress. The structure and nature of the courses affects the number of active participants directly. A comprehensive review of the available literature shows that very little intensive work was done using the pattern of user interaction with courses in the field of MOOC data analysis. In this paper, we take an initial step to use the deep learning algorithm to construct the dropout prediction model and produce the predicted individual student dropout probability. Additional improvements are made to optimize the performance of the dropout prediction model and provide the course providers with appropriate interventions based on a temporal prediction mechanism. Our Exploratory Data Analysis demonstrates that there is a strong correlation between click stream actions and successful learner outcomes. Among other features, the deep learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behaviour over time.

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