Interpretable MOOC dropout prediction using different Ensemble Methods and XAI

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiske detaljer
Main Authors:	Khan, Labib Hasan, Haque, Mohammed Ashfaqul, Ibrahim, Esaba Ahnaf
Andre forfattere:	Mostakim, Moin
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Machine learning Deep learning MOOC Dropout prediction Ensemble learning Explainable Artificial Intelligence (XAI) Web-based instruction. Teaching > Computer network resources. Education > Computer network resources.
Online adgang:	http://hdl.handle.net/10361/19395

id	10361-19395
record_format	dspace
spelling	10361-193952023-08-23T04:23:10Z Interpretable MOOC dropout prediction using different Ensemble Methods and XAI Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf Mostakim, Moin Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University Machine learning Deep learning MOOC Dropout prediction Ensemble learning Explainable Artificial Intelligence (XAI) Web-based instruction. Teaching--Computer network resources. Education--Computer network resources. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-55). Massive open online course (MOOC) has been around for a while, but started to gain traction since 2012 when Coursera was established. MOOCs use pre-recorded lectures and scheduled weekly tests to provide content and access to students over the internet. Even though there was a high expectation that it would revolutionize the education system, due to the mode of one-way content delivery, the goal was too far-fetched. The flexibility in deadlines and no restrictions of classroom exams meant students were not bound to finish on time. Hence, most students did not finish the course and dropped out. The dataset used in our research was the KDDCUP 2015 dataset, which was publicly available by the organizers of XuetangX platform. We used about 12 features namely browser access, navigate, average chapter delays, server sequential etc to comprehend the possibility of dropout. In this paper, we aim to predict dropout of a learner so that it can be prevented through manual interaction. Additionally, we have implemented XAI to interpret our models to suggest MOOC platforms which feature impact dropout the most. We used different ensemble learning techniques, namely voting classifier and stacking. Our voting classifier uses five of our best performing machine learning models. Then we evaluate the model by using multiple metrics such as precision, recall, F1-score, ROC curve and AUC score. Finally, we managed to obtain a recall of 97.636% with stacking and f1-score of 91.603% with hard voting classifier. Labib Hasan Khan Mohammed Ashfaqul Haque Esaba Ahnaf Ibrahim B. Computer Science 2023-08-14T04:36:30Z 2023-08-14T04:36:30Z 2023 2023-01 Thesis ID: 19101014 ID: 19301002 ID: 20301422 http://hdl.handle.net/10361/19395 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 55 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning Deep learning MOOC Dropout prediction Ensemble learning Explainable Artificial Intelligence (XAI) Web-based instruction. Teaching--Computer network resources. Education--Computer network resources.
spellingShingle	Machine learning Deep learning MOOC Dropout prediction Ensemble learning Explainable Artificial Intelligence (XAI) Web-based instruction. Teaching--Computer network resources. Education--Computer network resources. Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf
format	Thesis
author	Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf
author_sort	Khan, Labib Hasan
title	Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
title_short	Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
title_full	Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
title_fullStr	Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
title_full_unstemmed	Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
title_sort	interpretable mooc dropout prediction using different ensemble methods and xai
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19395
work_keys_str_mv	AT khanlabibhasan interpretablemoocdropoutpredictionusingdifferentensemblemethodsandxai AT haquemohammedashfaqul interpretablemoocdropoutpredictionusingdifferentensemblemethodsandxai AT ibrahimesabaahnaf interpretablemoocdropoutpredictionusingdifferentensemblemethodsandxai
_version_	1814308763649179648

Interpretable MOOC dropout prediction using different Ensemble Methods and XAI

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