Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine

Authors

  • Agung Eka Widarta Widarta Islamic University of Indonesia
  • Ahmad Luthfi Islamic University of Indonesia
  • Chandra Kusuma Dewa Islamic University of Indonesia

DOI:

10.33395/sinkron.v9i1.12857

Keywords:

Student performance, Covid-19 Pandemic, E-Learning, Support Vector Machine (SVM)

Abstract

The COVID-19 crisis has profoundly impacted many sectors globally, including education, necessitating the shift from traditional in-person learning to independent or online learning through various digital platforms. The integrity of e-learning can be ensured by leveraging e-learning behavioral data. The objective of this research is to develop a novel data model to navigate the educational challenges of the COVID-19 era. Previous studies employed the Support Vector Machine (SVM) technique to predict student performance in an e-learning setting, yet they failed to contrast different SVM kernels and their outcomes. In contrast, this study uses SVM and compares three types of kernels: Radial, Polynomial, and Linear. The dataset used for this research was procured from X-API-Edu-Data. The SVM technique was utilized in a unique way to process the data, which comprised 17 variables and 40 observations. Notably, all 17 variables were character variables, with only four being numeric. Two variables, Raisedhands and Discussion, were selected for analysis due to their key role in effective learning and their association with student performance in an e-learning environment. The evaluation of the model was performed using the Topic variable, which represents the subjects in the dataset. The research findings revealed a marked improvement in accuracy compared to earlier studies. Among the three SVM kernels tested - Radial, Polynomial, and Linear, the Polynomial kernel demonstrated superior accuracy with a score of 0.9979. Therefore, the Polynomial model was deemed most appropriate for analyzing the Topic variable. In conclusion, the study indicates that the application of the e-learning method, specifically during the COVID-19 pandemic, proved highly effective in forecasting student performance.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Amrieh, EA, Hamtini, T., & Aljarah, I. (2016). Mining Educational Data to Predict Student's academic Performance using Ensemble Methods. International Journal of Database Theory and Application, 9(8), 119-136.

Amrieh, EA, Hamtini, T., & Aljarah, I. (2015, November). Preprocessing and analyzing educational data sets using X-API for improving student's performance. In Applied Electrical Engineering and Computing Technologies (AEECT), 2015 IEEE Jordan Conference on (pp. 1-5). IEEE.

Emzir. 2010. Educational Research Methodology: Quantitative and Qualitative. Jakarta: Rajawali Press.

Hartono, jogiyanto. 2018. Methods of data collection and analysis techniques (ANDI, 2018) page 31

J, Han (2012). Data mining: Data mining concepts and techniques . Morgan Kaufmann.

Kyriacou, C. (2009) Effective Teaching in Schools: Theory and Practice. Third Edition. Delta Place, Cheltenham, UK: Nelson Thornes Ltd

Maswan & Khoirul Muslimin. (2017). Educational Technology Application of Systematic Learning. Yogyakarta: Student Libraries.

R., Dr. Gopinath. (2015). Research Proposal-Flow Chart-New Research Scholars-Reg

Sagala, Saiful. 2010. The Concept and Meaning of Learning to Help Solve Learning and Teaching Problems. Bandung: Alphabet

Sahfitri, V., & Ulfa, M. (2015). Evaluation of the Usability of the E-Learning System as an Application Supporting the Learning Process in Higher Education Using the USE Questionnaire. MATRIK Scientific Journal, 17(1), 53-66.

Samsu, Research University, Jambi: STS Press, 2011, p. 4.

Wulandari A., et al. 2020. The Relationship between Individual Characteristics and Knowledge on Prevention of Coronavirus Disease 2019 in Communities in South Kalimantan. Journal of Indonesian Public Health, 15(1):42-46.

Downloads


Crossmark Updates

How to Cite

Widarta, A. E. W., Luthfi, A., & Kusuma Dewa, C. . (2024). Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 332-345. https://doi.org/10.33395/sinkron.v9i1.12857