Predicting student academic performance in Video-Based learning

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era due to the impact of COVID-19 pandemic has promoted the rise of the big data era in the e-learning platform. A new educational norm has been created due to the emerge of the educationa...

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Main Author:	Teoh, Chin Wei
Format:	Thesis
Published:	2023
Subjects:	LB1025-1050.75 Teaching (Principles and practice)
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id	my-mmu-ep.11846
record_format	uketd_dc
spelling	my-mmu-ep.118462023-11-27T02:39:00Z Predicting student academic performance in Video-Based learning 2023-04 Teoh, Chin Wei LB1025-1050.75 Teaching (Principles and practice) The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era due to the impact of COVID-19 pandemic has promoted the rise of the big data era in the e-learning platform. A new educational norm has been created due to the emerge of the educational technology domain where many students have accessing e-learning web platforms such as Edpuzzle, Coursera, and Udemy as well as Youtube for learning new knowledge. However, there are some drawbacks especially in the asynchronous video-based learning. Sense of isolation could be occurred between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that delivered by the teacher may not reach to students effectively and cause a drop in student performance in the coming examination. Moreover, growing of video-based learning has create the huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93. 2023-04 Thesis http://shdl.mmu.edu.my/11846/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Computing and Informatics (FCI) EREP ID: 11557
institution	Multimedia University
collection	MMU Institutional Repository
topic	LB1025-1050.75 Teaching (Principles and practice)
spellingShingle	LB1025-1050.75 Teaching (Principles and practice) Teoh, Chin Wei Predicting student academic performance in Video-Based learning
description	The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era due to the impact of COVID-19 pandemic has promoted the rise of the big data era in the e-learning platform. A new educational norm has been created due to the emerge of the educational technology domain where many students have accessing e-learning web platforms such as Edpuzzle, Coursera, and Udemy as well as Youtube for learning new knowledge. However, there are some drawbacks especially in the asynchronous video-based learning. Sense of isolation could be occurred between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that delivered by the teacher may not reach to students effectively and cause a drop in student performance in the coming examination. Moreover, growing of video-based learning has create the huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93.
format	Thesis
qualification_level	Master's degree
author	Teoh, Chin Wei
author_facet	Teoh, Chin Wei
author_sort	Teoh, Chin Wei
title	Predicting student academic performance in Video-Based learning
title_short	Predicting student academic performance in Video-Based learning
title_full	Predicting student academic performance in Video-Based learning
title_fullStr	Predicting student academic performance in Video-Based learning
title_full_unstemmed	Predicting student academic performance in Video-Based learning
title_sort	predicting student academic performance in video-based learning
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics (FCI)
publishDate	2023
_version_	1783726130223644672

Predicting student academic performance in Video-Based learning

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