Abstract
Evaluating courses’ quality in education is an important part of the entire training process, to ensure that the program is successfully practiced. In the process of researching and improving teaching materials to enhance courses’ quality, understand students’ expectations and objectives, the problem they are facing and give the appropriate advice, courses evaluation based on traditional methods such as valuing assessment data by averaging results will not apply to criteria of different importance levels. Proposal to build a decision support system using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) - AHP (Analytic Hierarchy Process) evaluates course quality based on the Newhouse ICT criteria set and set of criteria from the University of Economics Ho Chi Minh City (UEH). Experimental results showed that the proposed model using TOPSIS - AHP integration method improved over traditional methods, contributing to helping managers make the right decision with multiple purpose criteria.
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References
Popkova, E.G., Ragulina, Y.V., Bogoviz, A.V.: Industry 4.0: Industrial Revolution of the 21st Century. Springer (2019). https://doi.org/10.1007/978-3-319-94310-7
Li, S.-T.T., Klein, M.D., Balmer, D.F., Gusic, M.E.: Scholarly evaluation of curricula and educational programs: using a systematic approach to produce publishable scholarship. Acad. Pediatr. 20(8), 1083–1093 (2020)
Somasundaram, M., Junaid, K.M., Mangadu, S.: Artificial intelligence (AI) enabled intelligent quality management system (IQMS) for personalized learning path. Procedia Computer Science 172, 438–442 (2020)
Newhouse, C., Clarkson, B., Trinidad, S.: A framework for leading school change in using ICT. Journal of Computer Assisted Learning (2005)
Akoka, J.: A framework for decision support systems evaluation. Inf. Manage. 4(3), 133–141 (1981)
Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple attribute decision making, pp. 58–191 (1981). Springer https://doi.org/10.1007/978-3-642-48318-9_3
Kumar, A., Baldea, M., Edgar, T.F., Ezekoye, O.A.: Smart manufacturing approach for efficient operation of industrial steam-methane reformers. Industrial Eng. Chemistry Res. 54(16), 4360–4370 (2015)
Vavrek, R., Bečica, J.: Population size and transport company efficiency–Evidence from Czech Republic. Transportation Res. Interdisciplinary Perspectives 6, 100145 (2020)
Yin, J., Yang, X., Zheng, X., Jiao, N.: Analysis of the investment security of the accommodation industry for countries along the B&R: an empirical study based on panel data. Tour. Econ. 23(7), 1437–1450 (2017)
Marković, L., Milić Marković, L., Mitrović, S., Stanarević, S.: Vrednovanje varijantnih rešenja trase autoputa E-763 Beograd-Južni Jadran: studija slučaja u Srbiji. Tehnički vjesnik 24(6), 1951–1958 (2017)
Mandić, K., Delibašić, B., Knežević, S., Benković, S.: Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods. Economic Research-Ekonomska istraživanja 30(1), 550–565 (2017)
Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A.A., Ghorbani, M.A., Shahbazi, F.: Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma 310, 178–190 (2018)
Kaynak, S., Altuntas, S., Dereli, T.: Comparing the innovation performance of EU candidate countries: an entropy-based TOPSIS approach. Economic research-Ekonomska istraživanja 30(1), 31–54 (2017)
Rădulescu, C.Z., Rădulescu, I.C.: An extended TOPSIS approach for ranking cloud service providers. Studies in Informatics Control 26(2), 183–192 (2017)
Chang, D.-Y.: Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95(3), 649–655 (1996)
Saaty, T.L.: The analytic hierarchy process: decision making in complex environments. In: Avenhaus, R., Huber, R.K. (eds.) Quantitative Assessment in Arms Control, pp. 285–308. Springer, Boston (1984). https://doi.org/10.1007/978-1-4613-2805-6_12
Lin, C., Kou, G.: A heuristic method to rank the alternatives in the AHP synthesis. Appl. Soft Comput. 100, 106916 (2021)
Oliva, G., Setola, R., Scala, A.: Sparse and distributed analytic hierarchy process. Automatica 85, 211–220 (2017)
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This research is supported by University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, Vietnam under project CTD-2022–11.
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Truong, V.P., Nguyen, Q.H. (2023). Applied Decision Support System Using TOPSIS – AHP, and ICT Newhouse Indicators for Evaluation of Courses at University of Economics Ho Chi Minh City (UEH), Vietnam. In: García Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds) Computational Intelligence, Data Analytics and Applications. ICCIDA 2022. Lecture Notes in Networks and Systems, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-27099-4_4
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