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  Dropout Prediction Using Multi-Classifier Prediction and Score Integration Method  
  Authors : Rakhi Viswan; Deepa Rajan S; Dr.Radhakrishnan B
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A massive open online course is an online course aimed at unlimited participation and open access via the web. In addition to traditional course materials, such as filmed lectures, readings, and problem sets, many MOOCs provide interactive courses with user forums to support community interactions among students, professors, and teaching assistants(TAs), as well as immediate feedback to quick quizzes and assignments. MOOCs are a recent and widely researched development in MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. In this paper, system proposes a model to predict the MOOC dropout by considering the events or features of the MOOC course .The proposed approach is designed to estimate the student dropout rate using a neural network ensemble model. The neural network ensemble model is an architecture designed to process the data parallel with two different mechanisms namely MLP, Recurrent Neural Network. The proposed system takes students activities such as problems, videos, seminars etc as input. The Proposed system offers better accuracy which is more than the current systems.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 266-269

Figures :02

Tables : 01


Rakhi Viswan : received her B.Tech (CSE) degree from University of Kerala in 2017. She is currently pursuing her Masters in Computer Science & Engineering from KTU.

Deepa Rajan S : is working as Assistant Professor in Computer Science and Engineering Department. She has 10 years' experience in teaching. Her research interests focuses on Data Security, Image Processing.

Dr. Radhakrishnan B : is working as the Head of CSE department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining.


MOOC drop out, MLP, Recurrent neural, Ensemble model

Predicting dropout student is an important and challenging task for universities,policymakers and educators especially in online learning.Therefore,this study examined whether the use of ensemble model can be helpful in dealing with this problem in an online program.Here,the student activities are selected as input. In this study we use two neural network for training.This project worked on building models to get best of prediction about dropout in MOOC platform.For this different models,tuned the parameters and ensemble them together to reach good accuracy.The result shows that the proposed model is better as compared with the previous models.Also ,this model gives an accurate result.


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