Exploring Student Engagement, Performance, and Satisfaction in an e-Learning Environment
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International Journal of Science and Management Studies (IJSMS) © 2025 by IJSMS Journal Volume-8 Issue-3 Year of Publication : 2025 Authors : Rex P. Flejoles, Joey S. Aviles DOI: 10.51386/25815946/ijsms-v8i3p102 |
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| Citation: MLA Style: Rex P. Flejoles, Joey S. Aviles "Exploring Student Engagement, Performance, and Satisfaction in an e-Learning Environment" International Journal of Science and Management Studies (IJSMS) V8.I3 (2025):12-23. APA Style: Rex P. Flejoles, Joey S. Aviles, Exploring Student Engagement, Performance, and Satisfaction in an e-Learning Environment, International Journal of Science and Management Studies (IJSMS), v8(i3), 12-23. |
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| Abstract: Varying perspectives were observed about students’ behavioral engagement, academic performance, and course satisfaction. To present a validation on their relationship in an e-learning environment, an available dataset containing relevant data was explored.This study was conducted to discover patterns in an e-learning environment, focusing on the relationship among the behavioral engagement, academic performance, and course satisfaction of students. This study employed the process of discovering knowledge from big data, also simply referred to as data mining. This study utilized a freely accessible dataset from Kaggle, Inc. containing data from the e-learning platform of Tableau. Although the dataset contained 11 tables, only five were used for the purpose of the study.The results revealed variation in the time spent for using learning videos in the different courses, high scores obtained by the students in the different exams, and high ratings by the students given to the different courses. However, sufficient correlation was not observed among students’ behavioral engagement, academic performance, and course satisfaction to continue with the regression analysis. Instead, the direct, very strong correlation was detected between the completion time and allotted duration for an exam, hence corresponding model was created. |
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| Keywords: Data Mining, E-Learning, Learning Analytics, Regression Analysis. | ||
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