Evaluating the Suitability of Online Courses using the ELECTRE Method

S Sahyunu; Jimmy Moedjahedy; Iwan Adhicandra; Yogasetya Suhanda; Usanto S; Robbi Rahim

doi:10.35445/alishlah.v15i3.3912

Evaluating the Suitability of Online Courses using the ELECTRE Method

S Sahyunu, Jimmy Moedjahedy, Iwan Adhicandra, Yogasetya Suhanda, Usanto S, Robbi Rahim

Abstract

This study aims to explore the use of the Elimination and Choice Expressing Reality (ELECTRE) method for selecting and evaluating online courses. The pairwise comparison method determined a set of criteria and weights, including course quality, instructor experience, accreditation, student engagement, flexibility, technical support, and cost. The study used a mixed-methods approach, which means it combines quantitative and qualitative data. The ELECTRE method was then applied to rank five online courses based on their suitability for the participants' needs and expectations. The concordance and discordance indices were calculated for each course, and the net flow was used to determine the ranking. The results showed that the ELECTRE method can be a useful tool for participants in choosing and evaluating online courses based on a set of tailored criteria and weights. Future research could investigate how the results of the ELECTRE method can be combined with other methods to enhance the accuracy and validity of the ranking. Overall, the ELECTRE method provides a useful framework for participants to decide which online course is best suited to their individual preferences and goals.

Keywords

Decision-making; ELECTRE Method; Evaluation; Online Courses

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References

Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314. https://doi.org/10.1016/J.CHB.2013.10.035

Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004

Bai, Y., Tang, B., Wang, B., Mo, D., Zhang, L., Rozelle, S., Auden, E., & Mandell, B. (2023). Impact of online computer assisted learning on education: Experimental evidence from economically vulnerable areas of China. Economics of Education Review, 94, 102385. https://doi.org/10.1016/J.ECONEDUREV.2023.102385

Boulos, A. N. (2022). Evaluation of the effectiveness of online education in anatomy for medical students during the COVID-19 pandemic. Annals of Anatomy - Anatomischer Anzeiger, 244, 151973. https://doi.org/10.1016/J.AANAT.2022.151973

Cui, Y., Ma, Z., Wang, L., Yang, A., Liu, Q., Kong, S., & Wang, H. (2023). A survey on big data-enabled innovative online education systems during the COVID-19 pandemic. Journal of Innovation & Knowledge, 8(1), 100295. https://doi.org/10.1016/J.JIK.2022.100295

Efthymiou, L., & Zarifis, A. (2021). Modeling students’ voice for enhanced quality in online management education. The International Journal of Management Education, 19(2), 100464. https://doi.org/10.1016/J.IJME.2021.100464

Emanuel, F., Ricchiardi, P., Sanseverino, D., & Ghislieri, C. (2021). Make soft skills stronger? An online enhancement platform for higher education. International Journal of Educational Research Open, 2, 100096. https://doi.org/10.1016/J.IJEDRO.2021.100096

Finlay, M. J., Simpson, T., & Tinnion, D. J. (2022). Association between attendance, online course activity time, and grades: Analysis of undergraduate sport science cohorts during the COVID-19 pandemic. Journal of Hospitality, Leisure, Sport & Tourism Education, 31, 100397. https://doi.org/10.1016/J.JHLSTE.2022.100397

Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE Methods. International Series in Operations Research & Management Science, 155–185. doi:10.1007/978-1-4939-3094-4_5

Govindan, Kannan. (2015). ELECTRE: A Comprehensive Literature Review on Methodologies and Application. European Journal of Operational Research. 250 (1): 1-29.

Hardt, D., Nagler, M., & Rincke, J. (2023). Tutoring in (online) higher education: Experimental evidence. Economics of Education Review, 92, 102350. https://doi.org/10.1016/J.ECONEDUREV.2022.102350

Li, X., Chen, Q., Fang, F., & Zhang, J. (2016). Is online education more like the global public goods? Futures, 81, 176–190. https://doi.org/10.1016/J.FUTURES.2015.10.001

Liu, W., & Zhang, L. (2023). Performance Evaluation and Identification of Key Influencing Factors for Student Achievement Based on the Entropy-Weighted TOPSIS Model. Computational Intelligence and Neuroscience, 2023, 1–12. https://doi.org/10.1155/2023/1253824

Minarni and A. Fadhillah. (2017). Expert System in Detecting Rice Plant Diseases. Jounal Dyn: 2 (1): 11–15.doi:10.22216/JoD.2017.V2.11-15.

Shwartz-Asher, D., Raviv, A., & Herscu-Kluska, R. (2022). Teaching and assessing active learning in online academic courses. Social Sciences & Humanities Open, 6(1), 100341. https://doi.org/10.1016/J.SSAHO.2022.100341

S. Amirghodsi, A. B. Naeini and A. Makui. (2022). An Integrated Delphi-DEMATEL-ELECTRE Method on Gray Numbers to Rank Technology Providers. in IEEE Transactions on Engineering Management, vol. 69, no. 4, pp. 1348-1364, doi: 10.1109/TEM.2020.2980127.

Zhang, S., Ma, R., Wang, Z., Li, G., & Fa, T. (2022). Academic self-concept mediates the effect of online learning engagement on deep learning in online courses for Chinese nursing students: A cross-sectional study. Nurse Education Today, 117, 105481. https://doi.org/10.1016/J.NEDT.2022.105481

DOI: https://doi.org/10.35445/alishlah.v15i3.3912