Application of a Combined Approach of Text Mining and QFD Methodology Based on Single Valued Neutrosophic Numbers for Efficient Curriculum Design

Yıl 2022, Cilt: 10 Sayı: 2, 127 - 138, 31.12.2022

Sevgi Abdalla

https://doi.org/10.17093/alphanumeric.1127620

Öz

In this study, an alternate curriculum design for an undergraduate program of Statistics is suggested carrying out a combined approach of the QFD methodology, text mining techniques under single valued neutrosophic set environment. To capture the employers’ expectations from their potential employees, 640 job advertisements, obtained from two of the most important career and job posting sites in Turkey, were analyzed using TF-IDF technique, which is one of the text mining methods. By using single-valued neutrophic set (SVNS) theory in QFD, the technical requirements representing the courses included in the curriculum were found their priorities. Hence, the technical characteristics that play a critical role in evaluating the curriculum quality of the undergraduate program were revealed. In addition, single valued neutrosophic sets have provided a flexible decision-making procedure to improve the quality of individuals’ subjective assessments. Consequently, this is expected to be a good reference for researchers working on these issues, both in terms of the proposed approach and the problem addressed.

Anahtar Kelimeler

single valued neutrosophic sets, quality function deployment, tf-idf measure, curriculum design

Proje Numarası

yok

Kaynakça

• Abdul‐Rahman, H., Kwan, C. L., & Woods, P. C. (1999). Quality function deployment in construction design: application in low‐cost housing design. International Journal of Quality & Reliability Management.
• Abuzid, H. F. T. (2017). Applying QFD tools for quality improvements in curriculum design and teaching strategies to meet with the customer (learner) needs. Journal of Engineering and Applied Sciences, 12 (3), 684-690.
• Bafna, P., Pramod, D., & Vaidya, A. (2016, March). Document clustering: TF-IDF approach. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 61-66). IEEE.
• Boonyanuwat, N., Suthummanon, S., Memongkol, N., & Chaiprapat, S. (2008). Application of quality function deployment for designing and developing a curriculum for Industrial Engineering at Prince of Songkla University. Songklanakarin Journal of Science & Technology, 30(3).
• Dean, E. B. (1995). Quality Function Deployment. Design for Competitive Advantage, World.
• Hanushek, E. A., Schwerdt, G., Wiederhold, S., & Woessmann, L. (2015). Returns to skills around the world: Evidence from PIAAC. European Economic Review, 73, 103-130.
• Erdil, N. O., & Arani, O. M. (2018). Quality function deployment: more than a design tool. International Journal of Quality and Service Sciences.
• Gupta, R., Gupta, S., & Nagi, K. (2012). Analysis & designing an engineering course using QFD. International Journal of Modern Engineering Research, 2(3), 896-901.
• Güran, A., & Kınık, D. (2021). TF-IDF ve Doc2Vec Tabanlı Türkçe Metin Sınıflandırma Sisteminin Başarım Değerinin Ardışık Kelime Grubu Tespiti ile Arttırılması. Avrupa Bilim ve Teknoloji Dergisi, (21), 323-332.
• Jnanesh, N. A., & Hebbar, C. K. (2008, October). Use of quality function deployment analysis in curriculum development of engineering education and models for curriculum design and delivery. In Proceedings of the world congress on engineering and computer science (pp. 22-24).
• Kamvysi, K., Gotzamani, K., Andronikidis, A., & Georgiou, A. C. (2014). Capturing and prioritizing students’ requirements for course design by embedding Fuzzy-AHP and linear programming in QFD. European Journal of Operational Research, 237(3), 1083-1094.
• Kelesbayev, D. (2014). Türk Dünyasinin Eğitim Sistemindeki Ortak Mesele: Kalite. Uluslararası Türkçe Edebiyat Kültür Eğitim (TEKE) Dergisi, 3 (2), https://dergipark.org.tr/tr/pub/teke/issue/12850/155765
• Köksal, G., & Eği̇tman, A. (1998). Planning and design of industrial engineering education quality. Computers & industrial engineering, 35(3-4), 639-642.
• Patil, L. H., & Atique, M. (2013, February). A novel approach for feature selection method TF-IDF in document clustering. In 2013 3rd IEEE international advance computing conference (IACC)(pp. 858-862). IEEE.
• Ramos, J. (2003, December). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning (Vol. 242, No. 1, pp. 29-48).
• Pramanik, S., Dalapati, S., Alam, S., Smarandache, F., & Roy, T. K. (2018). NS-cross entropy-based MAGDM under single-valued neutrosophic set environment. Information, 9(2), 37.
• Smarandache F. A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic. Rehoboth: American Research Press; 1999.
• Sodenkamp, M. A., Tavana, M., & Di Caprio, D. (2018). An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets. Applied Soft Computing, 71, 715-727. Smithson, M (2015) Probability judgments under ambiguity and conflict. Frontiers in Psychology. 6 674. doi: 103389/fpsyg201500674
• Ünal, Y. Z., & Uysal, Ö. (2014). A new mixed integer programming model for curriculum balancing: Application to a Turkish university. European Journal of Operational Research, 238(1), 339-347.
• Van LH, Yu VF, Dat LQ, Dung CC, Chou S-Y, Loc NV (2018) New integrated quality function deployment approach based on interval neutrosophic set for green supplier evaluation and selection Sustainability. 10 (3):838. https://doi.org/10.3390/su10030838
• Wang, H., Smarandache, F., Zhang Y. and Sunderraman, R., (2010). Single valued Neutrosophic Sets, Multi-space and multi-structure, 4, 410-413.
• Ye, J. (2014). Single valued neutrosophic cross-entropy for multicriteria decision making problems. Applied Mathematical Modelling, 38(3), 1170-1175.
• Zadeh, LA. Fuzzy sets. Inf Control. 1965; 8: 338- 353.
• Zhang, W., Yoshida, T., & Tang, X. (2011). A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Systems with Applications, 38(3), 2758-2765.
• Zhou, H. (2022). Research of Text Classification Based on TF-IDF and CNN-LSTM. In Journal of Physics: Conference Series(Vol. 2171, No. 1, p. 012021). IOP Publishing.