AN EFFECTIVE DATA MINING APPROACH FOR ASSESSING STUDENT’S SATISFACTION IN ONLINE EDUCATION DURING COVID-19 PANDEMIC

Authors

  • Ansar Riaz Faculty of Computing, Student Inn College Faisalabad
  • Muhammad Hassan Faculty of Computing, Kips College Faisalabad
  • Sufyan Hameed Faculty of Computing, Govt University Faisalabad (GCUF)
  • Muhammad Adeel Imdad Faculty of Computing, Comsats University Wah Catt

DOI:

https://doi.org/10.36755/jac.v2i1.59

Keywords:

Higher education organizations, random forest, feature selection, educational data mining, Data Center

Abstract

The advent of the COVID-19 outbreak has caused widespread public-health concerns. As a result of these emergency conditions, several governments have opted to implement lockdowns to reduce social interaction and minimize infection. COVID-19 has significantly impacted Higher Education Organizations (HEOs). Many unorthodox educational methods are proposed to ensure the continuation of the learning system in light of the effects of this pandemic and the necessity for alternative remedies. Online Education (OE), also based on learning together in a synchronous or asynchronous environment by employing various equipment, including mobile devices, Computers, and so forth, for Internet access, was among the options. All education systems are primarily concerned with boosting students' academic achievement to improve the overall standard of teaching.  In this regard, Educational Data Mining (EDM) seems to be an expeditiously growing field of research that employs the significance of Data Mining (DM) ideas to assist the education system in determining valuable information just on Student Satisfaction Learning (SSL) with both Online Learning procedure (OL) as during COVID-19. Various approaches have been explored using EDM to forecast students' behaviors to provide the optimum educational settings. As a result, Feature Selection (FS) was commonly used to find one of the most indicates.

the status of characteristics with the least cardinality. For COVID-19 to find accuracy result in this research KNN and SVM algorithm used by using modified data set from Kaggle. Results showed 79.9% precision of education level wise prediction using KNN, 73.7% precision of devices wise prediction using KNN and 88.5% precision of educational level wise predication using SVM, 73.8% precision of device wise prediction using SVM which is showing that the proposed model is significant.

References

Alam, T., Gupta, R., Qamar, S., & , A. (2022). Recent applications of Artificial Intelligence for Sustainable Development in smart cities. In Recent Innovations in Artificial Intelligence and Smart Applications (pp. 135-154). Cham: Springer International Publishing.

Sebai, D., & Shah, A. U. (2023). Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders. Signal, Image and Video Processing, 17(1), 285-293.

Alam, T., Ullah, A., & Benaida, M. (2023). Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems. Journal of Ambient Intelligence and Humanized Computing, 14(8), 9959-9972.

Ouhame, S., Hadi, Y., & , A. (2021). An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model. Neural Computing and Applications, 33, 10043-10055.

Aznaoui, H., A., Raghay, S., Aziz, L., & Khan, M. H. (2021). An efficient GAF routing protocol using an optimized weighted sum model in WSN. Indonesian Journal of Electrical Engineering and Computer Science, 22(1), 396-406.

Ullah, A., & Nawi, N. M. (2021). An improved in tasks allocation system for virtual machines in cloud computing using HBAC algorithm. Journal of Ambient Intelligence and Humanized Computing, 1-14.

Ullah, A., Nawi, N. M., & Ouhame, S. (2022). Recent advancement in VM task allocation system for cloud computing: review from 2015 to2021. Artificial Intelligence Review, 1-45.

Hussein, E., Daoud, S., Alrabaiah, H., & Badawi, R. (2020). Exploring undergraduate students’ attitudes towards emergency online learning during COVID-19: A case from the UAE. Children and Youth Services Review, 119(8), 105-699.

[Clark, A. E., Nong, H., Zhu, H., & Zhu, R. (2021). Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic. China Economic Review, 68(8), 1-14.

Ali, M. M., Paul, B. K., Ahmed, K., Bui, F. M., Quinn, J. M., & Moni, M. A. (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136(10), 104-672.

Kovacic, Z. (2010). Early prediction of student success: mining students enrolment data. Proceedings of the 2010 In Informing Science Information Technology Education Joint Conference, 19th & 24th June, Italy (pp. 647–665). InSITE.

Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers and Education, 65(13), 1–11.

Bovo, A., Sanchez, S., Héguy, O., & Duthen, Y. (2013). Clustering moodle data as a tool for profiling students. Second international conference on E-Learning and E-Technologies in

education, 23th & 25th, September Poland (pp. 121-126). IEEE.

Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.

Clark, A. E., Nong, H., Zhu, H., & Zhu, R. (2021). Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic. China Economic Review, 68(8), 1-14.

Maqableh, M., Jaradat, M., & Azzam, A. (2021). Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention. Education and Information Technologies, 26(4), 4003–4025.

Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D. J. (2019). Identifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7(3), 227–245.

Zollanvari, A., Kizilirmak, R. C., Kho, Y. H., & Hernandez-Torrano, D. (2017). Predicting students’ gpa and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access, 5(17), 23792–23802.

Ahmed, S., Paul, R., & Hoque, A. S. M. L. (2014). Knowledge discovery from academic data using association rule mining. International Conference on Computer and Information Technology, 22th & 23th December, Bangladesh (pp. 314–319). IEEE.

Quille, K., & Bergin, S. (2018). Programming: Predicting student success early in CS1. A re-validation and replication study. Annual Conference on Innovation and Technology in Computer Science Education, 2nd July, Cyprus (pp. 15–20). ITiCSE.

Phungsuk, R., Viriyavejakul, C., & Ratanaolarn, T. (2017). Development of a problem-based learning model via a virtual learning environment. Kasetsart .

Hasan, R., Hossain, M. M., & Khan, R. (2015, March). Aura: An iot based cloud infrastructure for localized mobile computation outsourcing. In 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (pp. 183-188). IEEE.

Haghnegahdar, L., Joshi, S. S., & Dahotre, N. B. (2022). From IoT-based cloud manufacturing approach to intelligent additive manufacturing: Industrial Internet of Things—An overview. The International Journal of Advanced Manufacturing Technology, 1-18.

Downloads

Published

2024-01-31

How to Cite

Ansar Riaz, Muhammad Hassan, Sufyan Hameed, & Muhammad Adeel Imdad. (2024). AN EFFECTIVE DATA MINING APPROACH FOR ASSESSING STUDENT’S SATISFACTION IN ONLINE EDUCATION DURING COVID-19 PANDEMIC. Journal of Advancement in Computing, 2(1), 21–29. https://doi.org/10.36755/jac.v2i1.59