Bir e-öğrenme platformu kriter seçimi için bulanık çok kriterli karar verme yöntemi

Year 2021, , 797 - 806, 31.12.2021

Meral Güldeş , Ömer Faruk Gürcan , Uğur Atıcı , Cenk Şahin

https://doi.org/10.31590/ejosat.1041281

Cited By: 2

Abstract

Gelişen teknoloji sayesinde insanların günlük hayat rutinlerinde meydana gelen en büyük değişimlerden biri de eğitim sürecinde ortaya çıkmıştır. Geliştirilen yazılımlar ve donanımlar nedeniyle insanlar zaman ve mekandan bağımsız olarak bilgiye ulaşmaya, bilgiyi işlemeye ve paylaşmaya başlamışlardır. Tek başına veya geleneksel eğitim tekniklerine entegre olarak kullanılan internet tabanlı öğretim süreci pek çok avantajı da beraberinde getirmiş ve vazgeçilmez bir duruma gelmiştir. Özellikle 2019 yılının sonlarına doğru Çin’in Wuhan kentinde ortaya çıkan ve tüm dünyaya yayılan COVID-19 salgını bu sistemlerin geleceğin dünyası için büyük önem taşıdığını bir kere daha ortaya koymuştur. Salgın süresince geleneksel yüz yüze eğitim dünyanın pek çok bölgesinde sekteye uğramıştır ve online eğitim kullanımı artış göstermiştir. Bu durum bu sistemlerin konu edildiği çalışmaların da artmasına fırsat vermiştir. Özellikle bu sistemlerin etkili tasarımlarını konu alan çalışmalar eğitim sistemlerinin paydaşları için önem arzetmektedir. Bu çalışmada önemli kriter ve alt kriterler literatüre göre seçilmiş ve bulanık analitik hiyerarşi methodu kullanılarak değerlendirlimiştir.

Keywords

FAHP, FAHP, Optimizasyon, E-öğrenme, ÇKKV

A fuzzy multi-criteria decision-making method for selection of criteria for an e-learning platform

Abstract

Thanks to developing technology, one of the most significant changes in people’s daily life routines have emerged in the education system. Due to developed software and hardware, people have begun to access, process, and share information regardless of time and place. Therefore, the internet-based teaching process, used alone or integrated with traditional education techniques, has brought many advantages and has become indispensable. Especially towards the end of 2019, the COVID-19 epidemic, which emerged in Wuhan, China, and spread worldwide, has again revealed that these systems have great importance for the future world. During the epidemic, traditional face-to-face education has been disrupted in many parts of the world, increasing online education. This situation has allowed increasing the number of studies on these systems. In particular, studies on the practical design of these systems are essential for the stakeholders of education systems. In this study, some significant criteria and sub-criteria are chosen based on literature and evaluated by using the fuzzy analytic hierarchy process.

Keywords

FAHP, Optimization, E-Learning, MCDM

References

• Akcan, S., & Güldeş, M. (2019). Integrated multi-criteria decision-making methods to solve supplier selection problem: a case study in a hospital. Journal of healthcare engineering, 2019.
• Anggrainingsih, R., Umam, M., & Setiadi, H. (2018). Determining e-learning success factor in higher education based on user perspective using Fuzzy AHP. MATEC Web of Conferences, 154, 03011. doi: 10.1051/ matecconf/ 201815403011
• Başaran, S., & Haruna, Y. (2017). Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics.
• Bo, L., Xuning, P., & Bingquan, B. (2009). Modeling of network education effectiveness evaluation in fuzzy analytic hierarchy process. Paper presented at the 2009 International Conference on Networking and Digital Society.
• Chandna, R., Saini, S., & Kumar, S. (2021). Fuzzy AHP based performance evaluation of massive online courses provider for online learners.
• Chang, C. C., Liang, C., Shu, K. M., & Tsai, C. W. (2015). Key successful factors of knowledge management for university students using e‐portfolios: Approach of Fuzzy Delphi and Fuzzy AHP. Computer Applications in Engineering Education, 23(5), 673-681.
• Chao, R. J., & Chen, Y. H. (2009). Evaluation of the criteria and effectiveness of distance e-learning with consistent fuzzy preference relations. Expert Systems with Applications, 36(7), 10657-10662. doi:10.1016/j.eswa.2009.02.047
• Chen, S. Y. (2009). Identifying and prioritizing critical intellectual capital for e-learning companies. European Business Review, 21(5), 438-452. doi:10.1108/09555340910986664
• Garg, R., & Jain, D. (2017). Fuzzy multi-attribute decision making evaluation of e-learning websites using FAHP, COPRAS, VIKOR, WDBA. Decision Science Letters, 6(4), 351-364. doi:10.5267/j.dsl.2017.2.003
• Güldeş, M., Atici, U., & Şahin, C. (2021). Fuzzy Resource-Constrained Project Scheduling Under Learning Considerations. Paper presented at the International Conference on Intelligent and Fuzzy Systems.
• Gürcan, Ö. F., Yazıcı, İ., Beyca, Ö. F., Arslan, Ç. Y., & Eldemir, F. (2016). Third party logistics (3PL) provider selection with AHP application. Procedia-Social and Behavioral Sciences, 235, 226-234.
• Hong, F. L. (2010). Determining the sustainability of virtual learning communities in E-learning platform.
• Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software: John Wiley & Sons.
• Işık, A. H., Ince, M., & Yigit, T. (2015). A fuzzy AHP approach to select learning management system. International Journal of Computer Theory and Engineering, 7(6), 499.
• Jami Pour, M., Hosseinzadeh, M., Bagherzadeh Azar, M., & Taheri, F. (2017). Developing a new framework for evaluating e-learning systems: integrating BSC and FAHP. Kybernetes, 46(8), 1303-1324. doi:10.1108/K-02-2017-0060
• Jie, C. (2010). Evaluation and modeling of online course using fuzzy AHP.
• Kurilovas, E., & Vinogradova, I. (2016). Improved fuzzy AHP methodology for evaluating quality of distance learning courses. International journal of engineering education, 32(4), 1618-1624.
• Li, L., & Li, D. (2020) A research of FAHP approach in evaluating online training system alternatives. Vol. 1002 (pp. 40-48): Springer Verlag.
• Li, W., Gao, X., & Fu, G. (2012). Fuzzy comprehensive assessment of network environment and learning quality combined with the Analytic Hierarchy Process.
• Lin, C. T., Chang, S. J., & Chen, Y. H. (2021). Cognitive learning assessment based on FAHP and RSM: A case study of introduction to network course. Journal of Educational Computing Research, 07356331211012685.
• Lin, H. F. (2010). An application of fuzzy AHP for evaluating course website quality. Computers and Education, 54(4), 877-888. doi:10.1016/j.compedu.2009.09.017
• Linkov, I., & Moberg, E. (2011). Multi-criteria decision analysis: environmental applications and case studies: CRC Press.
• Liu, Q., Peng, R., Chen, A., & Xie, J. (2009). E-learning platform evaluation using fuzzy AHP.
• Lo, T. S., Chang, T. H., Shieh, L. F., & Chung, Y. C. (2011). Key factors for efficiently implementing customized e-learning system in the service industry. Journal of Systems Science and Systems Engineering, 20(3), 346-364. doi:10.1007/s11518-011-5173-y
• Mazaheri Asad, M., Ebrahimi Kermani, S., & Monteiro da Hora, H. R. (2015). A Proposed Framework for Evaluating Student’s Performance and Selecting the Top Students in E-Learning System, Using Fuzzy AHP Method.
• Mehregan, M. R., Jamporazmey, M., Hosseinzadeh, M., & Mehrafrouz, M. (2011a). Application of fuzzy analytic hierarchy process in ranking modern educational systems’ success criteria. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(4), 299.
• Mehregan, M. R., Jamporazmey, M., Hosseinzadeh, M., & Mehrafrouz, M. (2011b). Proposing an approach for evaluating e-learning by integrating critical success factor and fuzzy AHP. Paper presented at the International conference on innovation, management and service, Singapore.
• Nagpal, R., Mehrotra, D., Bhatia, P. K., & Bhatia, A. (2015). FAHP approach to rank educational websites on usability. International Journal of Computing and Digital Systems, 4(04).
• Naveed, Q. N., Qureshi, M. R. N., Alsayed, A. O., Muhammad, A., Sanober, S., & Shah, A. (2018). Prioritizing barriers of E-Learning for effective teaching-learning using fuzzy analytic hierarchy process (FAHP).
• Naveed, Q. N., Qureshi, M. R. N., Tairan, N., Mohammad, A., Shaikh, A., Alsayed, A. O., Shah, A., Alotaibi, F. M. (2020). Evaluating critical success factors in implementing e-learning system using multi-criteria decision-making. PLoS ONE, 15(5). doi:10.1371/journal.pone.0231465
• Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281.
• Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073-1076.
• Tseng, F. S. C., Chang, I. P., & Chou, A. Y. H. (2010). Design of an adaptive curriculum portfolio recommendation system by learning object similarity evaluation and Group Decision modeling.
• Turker Altun, Y., Baynal, K., & Turker, T. (2019). The evaluation of learning management systems by using Fuzzy AHP, fuzzy topsis and an integrated method: A case study. Turkish Online Journal of Distance Education, 20(2), 195-218.
• Upadhyay, H., Juneja, S., Juneja, A., Dhiman, G., & Kautish, S. (2021). Evaluation of ergonomics-related disorders in online education using fuzzy AHP. Computational Intelligence and Neuroscience, 2021. doi:10.1155/2021/2214971
• Wan, L., Shi, F., & Zhao, C. (2012). Evaluation of learning content management systems by using fuzzy analytic hierarchy process. Advanced Science Letters, 7, 714-718. doi:10.1166/asl.2012.2736
• Wang, C. S., & Lin, S. L. (2012). Combining fuzzy AHP and association rule to evaluate the activity processes of e-learning system. Paper presented at the 2012 Sixth International Conference on Genetic and Evolutionary Computing.
• Wang, C. S., & Lin, S. L. (2019). How Instructors Evaluate an e-Learning System? An Evaluation Model Combining Fuzzy AHP with Association Rule Mining. Journal of Internet Technology, 20(6), 1947-1959.
• Xing, H. H. (2010) Study of evaluating web-based courses based on FAHP. Vol. 78. Advances in Intelligent and Soft Computing (pp. 435-443).
• Yadegaridehkordi, E., Nizam Bin Md Nasir, M. H., Fazmidar Binti Mohd Noor, N., Shuib, L., & Badie, N. (2018). Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches. Applied Soft Computing, 66, 77-89. doi:https://doi.org/10.1016/j.asoc.2017.12.051
• Yang, M., & Chen, Y. (2010). The research of evaluation system of network self- learning based on fuzzy theory.
• Yazici, I., Beyca, O. F., Gurcan, O. F., Zaim, H., Delen, D., & Zaim, S. (2020). A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria. Annals of Operations Research, 1-24.
• Zare, M., Pahl, C., Rahnama, H., Nilashi, M., Mardani, A., Ibrahim, O., & Ahmadi, H. (2016). Multi-criteria decision making approach in E-learning: A systematic review and classification. Applied Soft Computing Journal, 45, 108-128. doi:10.1016/j.asoc.2016.04.020