Evaluating the impact of MOOC participation on skill development in autonomous engineering colleges
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.spl.07Keywords:
Massive open online courses (MOOCs), Higher education, Engineering colleges, Descriptive statistics, Regression analysis.Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The integration of massive open online courses (MOOCs) in higher education has introduced new avenues for skill development and academic achievement. This study investigates the impact of MOOC participation on students’ academic performance within autonomous engineering colleges. Specifically, we examine whether students who engage in MOOCs achieve higher academic outcomes compared to their peers who follow traditional coursework only. A sample of 450 engineering students from autonomous colleges was surveyed regarding their MOOC participation, academic performance, and engagement levels. To analyze the hypothesis that MOOC participation positively influences academic performance, multiple statistical methods were employed. Descriptive statistics provided an overview of student participation and performance trends, while a t-test was used to compare academic performance scores between MOOC participants and non-participants. Regression analysis was applied to determine if MOOC participation is a significant predictor of academic success. Additionally, a Chi-square test examined the association between MOOC engagement and academic achievement. The results indicate that MOOC participation positively correlates with academic performance, supporting the hypothesis that MOOCs can serve as a valuable supplement to traditional education. These findings underscore the potential of MOOCs to enhance learning outcomes in engineering education and suggest that autonomous colleges might benefit from promoting MOOC engagement as part of their curriculum.Abstract
How to Cite
Downloads
Similar Articles
- Nabab Ali, Equabal Jawaid, Spatial Insect Biodiversity and Community Analysis in Selected Rice Fields of North Bihar , The Scientific Temper: Vol. 13 No. 01 (2022): The Scientific Temper
- Regasa Begna, Worku Masho, Wondosan Wondimu, Yaregal Tilahun, Tilahun Bekele, Benyam Tadesse, Haile Negash, Participatory evaluation and demonstration of productive performance of Bovans Brown chicken under village production system in Menit Shasha Woreda, West Omo Zone, Ethiopia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- J. Mohan, R. Arun Kumar, In-vitro study on the antidiabetic property of Pisonia grandis , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- J. Helan Shali Margret, N. Amsaveni, A study on recency patterns of cited resources in the cytokine publications from web of science , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- L. Praveen Kumar, Vajha S. Kumar, Periods and periodic points of linear cellular automata , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Nupur Dogra, Shaveta Sharma, Impact of social networking sites on adolescent alienation and depression with special reference to Facebook usage , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Neeraj ., Anita Singhrova, Quantum Key Distribution-based Techniques in IoT , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- T. Kanimozhi, V. Rajeswari, R. Suguna, J. Nirmaladevi, P. Prema, B. Janani, R. Gomathi, RWHO: A hybrid of CNN architecture and optimization algorithm to predict basal cell carcinoma skin cancer in dermoscopic images , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S. Vnuchko, O. Batrymenko, О. Ткach, М. Karashchuk, M. Volkivskyi, Models of interaction between business and government in the conditions of the European integration course of Ukraine , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 23 24 25 26 27 28 29 30 31 32 > >>
You may also start an advanced similarity search for this article.