Predicting Student Performance from Video-Based Learning System: A Case Study
Journal of Logistics, Informatics and Service Science
; 9(3):64-77, 2022.
Article
in English
| Scopus | ID: covidwho-2081526
ABSTRACT
The major impact of the COVID-19 pandemic on the shift of education norms from physical classroom learning to MOOCs (Massive Open Online Courses) could accelerate the big data era growth for the e-learning platform. This circumstance has provided an opportunity for a teacher to use MOOC data to help students learn and perform better. Moreover, this research study goal is to propose a combination of machine learning algorithms and the feature selection benefit with the SMOTE (Synthetic Minority Oversampling Technique) algorithm for balancing the output features number to predict student performance in a video-based learning platform. As a result, the proposed machine learning classifier, Naïve Bayes algorithm with the combination of chi-square test and SMOTE has shown the highest accuracy in prediction of more than 90%. Results by the proposed classifier with feature selection and SMOTE have outperformed the traditional machine learning classifiers. © 2022, Success Culture Press. All rights reserved.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Case report
/
Prognostic study
Language:
English
Journal:
Journal of Logistics, Informatics and Service Science
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS