Bulut Tabanlı Öğrenme Yönetim Sistemi Seçiminde Bulanık Çok Kriterli Karar Analizi Yaklaşımı

Year 2020, Volume: 13 Issue: 1, 97 - 111, 31.01.2020

Hakan Özcan , Bülent Gürsel Emiroğlu

https://doi.org/10.17671/gazibtd.589757

Cited By: 5

Abstract

Bulut bilişim teknolojisinin gelişmesiyle öğrenme yönetim sistemleri (ÖYS’ler) yeni özellikler ve servis seçenekleri kazanmıştır. Buna bağlı olarak artan ürün alternatifleri arasından seçim yapma süreci zorlaşmıştır. Belli kriterlere bağlı en uygun bulut tabanlı ÖYS’yi seçmek kurumlar için önemli bir karar verme sorunu olmuştur. Bu çalışmada, kurumların bir grup bulut tabanlı ÖYS arasından belli kriterlere uygun seçim yapabilmesini kolaylaştıracak Bulanık Analitik Hiyerarşi Süreci (BAHS) tabanlı bir model geliştirilmiştir. Bu modelde, bulut tabanlı ÖYS seçiminde etkili olabilecek içerik desteği, etkileşim ve iş birliği, ölçme ve değerlendirme, ders yapısı, arayüz, verimlilik araçları, platform esnekliği, ölçeklenebilirlik, güvenlik, destek ve lisanslama kriterleri literatüre ve uzman görüşlerine dayalı incelenmiş ve oluşturulan bir hiyerarşik yapı ile sunulmuştur. Çalışmada hem kriterler hem de durum çalışması kapsamında ele alınan altı alternatif, çevrim-içi eğitim alanında uzman yedi karar verici tarafından değerlendirilmiştir. Belirlenen kriterlere bağlı olarak, seçilen alternatifler arasında yapılan bulanık ikili karşılaştırmalar sonucu en uygun bulut tabanlı ÖYS, TalentLMS olarak belirlenmiştir.

Keywords

çok kriterli karar analizi, bulanık analitik hiyerarşi süreci, bulut tabanlı öğrenme yönetim sistemleri

A Fuzzy Multi-Criteria Decision Analysis Approach for Cloud-Based Learning Management System Selection

Abstract

With the development of cloud computing technology, learning management systems (LMSs) have gained new features and service options. Consequently, the process of choosing among increasing product alternatives has become difficult. Choosing the most appropriate cloud-based LMS with certain criteria has been an important decision-making problem. In this study, a model has been developed by using Fuzzy Analytic Hierarchy Process (FAHP) to facilitate selection of the most suitable one from a group of cloud-based LMSs according to certain criteria. In this model, content support, interaction and collaboration, measurement and evaluation, course structure, interface, productivity tools, platform flexibility, scalability, security, support and licensing criteria that can be effective in cloud-based LMS selection are examined based on literature and expert opinions, and presented in a hierarchical structure. In the study, both the criteria and six alternatives in the context of a case study were evaluated by seven decision-makers specialized in the field of online education. Based on the determined criteria, the most appropriate cloud-based LMS was chosen as TalentLMS as a result of fuzzy pairwise comparisons between the selected alternatives.

Keywords

multi criteria decision analysis, fuzzy analytic hierarchy process, cloud based learning management systems

References

• A. A. Piña, “Learning management systems: A look at the big picture”, Learning management systems and instructional design: Best practices in online education, IGI Global, A.B.D, 1–19, 2013.
• O. S. Turan, M. R. Canal, “Öğrenme yönetim sistemi kullanılabilirlik incelemesi; Gazi İngilizce Dil Okulu örneği”, Bilişim Teknolojileri Dergisi, 4(3), 47–52, 2011.
• K. Bakshi, L. Beser, “Cloud Reference Frameworks”, Encyclopedia of Cloud Computing, Editör: S. Murugesan, I. Bojanova, John Wiley & Sons, Ltd,, Chichester, UK, 71-88, 2016.
• A. O. Akande, J.-P. Van Belle, “Cloud computing in higher education: A snapshot of software as a service”, 2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), Ota, Nigeria, 1-5, 2014.
• A. A. Piña, “An Educational Leader’s View of Learning Management Systems”, Leading and Managing e-Learning, Editör: A. A. Piña, V. L. Lowell, B. R. Harris, Springer, Cham, 101-113, 2018.
• P. Lal, “Organizational learning management systems: time to move learning to the cloud!”, Dev. Learn. Organ. Int. J., 29(5), 13-15, 2015.
• A. Mardani, A. Jusoh, K. MD Nor, Z. Khalifah, N. Zakwan, A. Valipour, “Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014”, Econ. Res.-Ekon. Istraživanja, 28(1), 516-571, 2015.
• A. Çetin, A. H. Işık, İ. Güler, “Learning management system selection with analytic hierarchy process”, 13th International Conference on Interactive Computer aided Learning(ICL2010), Hasselt, Belgium, 921–926, 2010.
• F. Colace, M. De Santo, “Evaluation models for e-learning platforms and the AHP approach: A case study”, IPSI BGD Trans. Internet Res., 7(1), 31–43, 2011.
• B. Srđević, M. Pipan, Z. Srđević, T. Arh, “AHP supported evaluation of LMS quality”, International workshop on the interplay between user experience (UX) Evaluation and system development (I-UxSED 2012), Copenhagen, Denmark, 50–57, 2012.
• E. Kurilovas, V. Dagiene, “Learning objects and virtual learning environments technical evaluation criteria”, Electron. J. E-Learn., 7(2), 127-136, 2009.
• A. Özbek, “PERFORMANCE EVALUATION OF LEARNING MANAGEMENT SYSTEM”, E-J. New World Sci. Acad., 8(2), 164-178, 2013.
• A. H. Işık, M. Ince, T. Yigit, “A fuzzy AHP approach to select learning management system”, Int. J. Comput. Theory Eng, 7(6), 499, 2015.
• E. Karagöz, L. Ö. Oral, O. H. Kaya, V. Tecim, “LMS Selection Process for Effective Distance Education System in Organizations”, KnE Soc. Sci., 1(2), 343–356, 2017.
• Y. A. Turker, K. Baynal, T. Turker, “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, 2019.
• Yahfizham, F. Purwani, K. Rukun, Krismadinata, “A review of Cloud Learning Management System (CLMS) based on Software as a Service (SaaS)”, 2017 International Conference on Electrical Engineering and Informatics (ICELTICs), Banda Aceh, 205-210, 2017.
• V. Petrova, “Using the Analytic Hierarchy Process for LMS selection”, Proceedings of the 20th International Conference on Computer Systems and Technologies - CompSysTech ’19, Ruse, Bulgaria, 332-336, 2019.
• Y. Gambo, P. Y. Mshelia, N. Nachandiya, “A Systematic Review on Applications of Multi-Criteria Decision Method (MCDM) in Evaluating Factors Influencing User Acceptance of Cloud-Based System‖”, J. Multidiscip. Eng. Sci. Stud. JMESS ISSN, 2017.
• E. Çakir, B. Kutlu Karabiyik, “Bütünleşik SWARA - COPRAS Yöntemi Kullanarak Bulut Depolama Hizmet Sağlayıcılarının Değerlendirilmesi”, Bilişim Teknolojileri Dergisi, 10(4), 417-434, 2017.
• Y. Şahin, D. M. İs, “Çok Kriterli Karar Verme Yöntemleri ile E-Alışveriş Siteleri Tercih Analizi”, Bilişim Teknolojileri Dergisi, 12(4), 265-275, 2019.
• A. Shawish, M. Salama, “Cloud Computing: Paradigms and Technologies”, Inter-cooperative Collective Intelligence: Techniques and Applications, Cilt: 495, Editör: F. Xhafa, N. Bessis, Springer Berlin Heidelberg, Berlin, Heidelberg, 39-67, 2014.
• J. A. González-Martínez, M. L. Bote-Lorenzo, E. Gómez-Sánchez, R. Cano-Parra, “Cloud computing and education: A state-of-the-art survey”, Comput. Educ., 80, 132-151, 2015.
• İ. S. Yildirim, Y. Göktaş, N. Temur, A. Kocaman, “İyi bir öğrenme yönetimi sistemi (ÖYS) için kriter önerisi”, Türk Eğitim Bilimleri Dergisi, 2(4), 455-462, 2004.
• E. W. Black, D. Beck, K. Dawson, S. Jinks, M. DiPietro, “Considering implementation and use in the adoption of an LMS in online and blended learning environments”, TechTrends, 51(2), 35–53, 2007.
• J. L. Salmeron, “Augmented fuzzy cognitive maps for modelling LMS critical success factors”, Knowledge-Based Systems, 22(4), 275-278, 2009.
• H. Ma, Z. Zheng, F. Ye, S. Tong, “The applied research of cloud computing in the construction of collaborative learning platform under e-learning environment”, 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization, Washington, DC, USA, 190–192, 2010.
• J. P. Laverty, D. F. Wood, D. Tannehill, F. Kohun, J. Turchek, “Improving the LMS Selection Process: Instructor Concerns, Usage and Perceived Value of Online Course Delivery Tools”, Inf. Syst. Educ. J., 10(1), 75-88, 2012.
• N. Cavus, “Selecting a learning management system (LMS) in developing countries: instructors’ evaluation”, Interact. Learn. Environ., 21(5), 419–437, 2013.
• K. Atchariyachanvanich, N. Siripujaka, N. Jaiwong, “What makes university students use cloud-based e-learning?: Case study of KMITL students”, International Conference on Information Society (I-Society 2014), London, UK, 112–116, 2014.
• S. Çoban, “Üniversitelerde Öğretim Yönetim Sistemleri Yazılımları Kullanımına Yönelik Bir İnceleme”, Batman Üniversitesi Yaşam Bilimleri Dergisi, 6(1), 1-12, 2016.
• D. Walker, J. Lindner, T. Pesl Murphrey, K. Dooley, “Learning Management System Usage: Perspectives From University Instructors”, Q. Rev. Distance Ducation, 12, 41-50, 2016.
• H. Hamidi, S. Rouhani, “The Effects of Cloud Computing Technology on E-Learning: Empirical Study”, Robot Autom Eng J, 2(5), 2018.
• E. K. Zavadskas, Z. Turskis, S. Kildienė, “STATE OF ART SURVEYS OF OVERVIEWS ON MCDM/MADM METHODS”, Technol. Econ. Dev. Econ., 20(1), 165-179, 2014.
• L. G. Vargas, “An overview of the analytic hierarchy process and its applications”, Eur. J. Oper. Res., 48(1), 2-8, 1990.
• M. Velasquez, P. T. Hester, “An analysis of multi-criteria decision making methods”, Int. J. Oper. Res., 10(2), 56–66, 2013.
• T. L. Saaty, J. S. Shang, “Group decision-making: Head-count versus intensity of preference”, Socio-Economic Planning Sciences, 41(1), 22-37, 2007.
• A. Özdağoğlu, G. Özdağoğlu, “Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(11), 65-85, 2007.
• T. L. Saaty, “Analytic Heirarchy Process”, Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, J. L. Teugels, John Wiley & Sons, Ltd, Chichester, UK, 2014.
• T.-C. Wang, Y.-H. Chen, “Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP”, Inf. Sci., 178(19), 3755-3765, 2008.
• M.-S. Kuo, G.-S. Liang, W.-C. Huang, “Extensions of the multicriteria analysis with pairwise comparison under a fuzzy environment”, Int. J. Approx. Reason., 43(3), 268-285, 2006.
• D.-Y. Chang, “Applications of the extent analysis method on fuzzy AHP”, Eur. J. Oper. Res., 95(3), 649-655, 1996.
• T. L. Saaty, “How to make a decision: the analytic hierarchy process”, Interfaces, 24(6), 19–43, 1994.
• C.-K. Kwong, H. Bai, “Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach”, Iie Trans., 35(7), 619–626, 2003.
• T. L. Saaty, L. T. Tran, “On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process”, Math. Comput. Model., 46(7-8), 962-975, 2007.
• T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting. McGraw-Hill, USA: Resource Allocation, 1980.
• Internet: eLearning Industry, https://elearningindustry.com, 19.10.2019.
• A. Kumar, M. K. Dash, Fuzzy optimization and multi-criteria decision making in digital marketing, Business Science Reference, An Imprint of IGI Global, Hershey, A.B.D, 2016.