Uzaktan Eğitimde Kullanılan Bulanık Mantık Tabanlı Öğrenme Modelleri, Platformlar, Ölçme ve Değerlendirme Yöntemleri

Year 2021, Issue: 25, 406 - 416, 31.08.2021

Beyza Esin Özseven Naim Cagman

https://doi.org/10.31590/ejosat.898349

Cited By: 2

Abstract

Uzaktan eğitim, geleneksel eğitimin teknolojik araçlar yardımıyla zaman ve mekan bağımsız olarak gerçekleştirilmesidir. Teknolojik gelişmeler ve pandemi sürecinin başlaması ile birlikte uzaktan eğitime olan talep çok yüksek seviyelere ulaşmıştır. Uzaktan eğitim sürecinin başarı ile idame ettirilmesi ve öğrencinin eğitim hayatını başarılı bir şekilde sürdürülmesi için kullanılacak öğrenme modelleri, platformlar ve ölçme değerlendirme yöntemleri önem arz etmektedir. Bu çalışma, uzaktan eğitimde kullanılan öğrenme modelleri, platformlar, ölçme ve değerlendirme ve uzaktan eğitimde bulanık mantığın kullanımına ilişkin bir derleme çalışmasıdır. Araştırma kapsamında uzaktan eğitimin uygulanmasına ilişkin literatür taraması yapılmıştır. Araştırma sonucunda, senkron ve asenkron sistemleri destekleyen platformların daha etkin eğitim sağladığı, öğrencilerin sistemi kullanım desenlerinin de ön planda olduğu, akademik güvensizliğin önüne geçmek için yapay zeka tekniklerinden yararlanıldığı ortaya konulmuştur. Ayrıca, bulanık mantığın öğrenme desenlerinin belirlenmesi, platform seçimi ve ölçme ve değerlendirme de yaygın olarak kullanıldığı sonucuna ulaşılmıştır.

Keywords

Uzaktan Eğitim, Yapay Zeka, Bulanık Mantık, Ölçme ve Değerlendirme

Learning Models, Platforms, Measurement and Evaluation Methods Based on Fuzzy Logic Used in Distance Education

Abstract

Distance education is the realization of traditional education independent of time and place with the help of technological tools. With the technological developments and the pandemic, the demand for distance education has reached very high levels. Learning models, platforms and assessment and evaluation methods to be used for the successful continuation of the distance education process and the successful education of the student are important. This study is a review study on learning models, platforms, measurement and evaluation used in distance education, and the use of fuzzy logic in distance education. Within the scope of the research, literature review on the application of distance education has been made. As a result of the research, it was revealed that platforms that support synchronous and asynchronous systems provide more effective training, students' system usage patterns are also at the forefront, and artificial intelligence techniques are used to prevent academic insecurity. In addition, it was concluded that fuzzy logic is widely used in determining learning patterns, platform selection, and assessment and evaluation.

Keywords

Distance Learning, Artificial Intelligance, Fuzzy Logic, Measurement and Evaluation

References

• Abu Bakar, N., Rosbi, S., & Bakar, A. A. (2020). Robust Estimation of Student Performance in Massive Open Online Course using Fuzzy Logic Approach. International Journal of Engineering Trends and Technology, 143-152.
• Abubakar, Y., & Ahmad, N. B. H. (2017). Prediction of Students’ Performance in E-Learning Environment Using Random Forest. International Journal of Innovative Computing, 7(2), Article 2.
• Al Duhayyim, M. (2019). Concept-based and fuzzy adaptive e-learning (CaFAE) [Doctoral, University of Sussex]. http://sro.sussex.ac.uk/id/eprint/86221/
• Almohammadi, K., Hagras, H., Yao, B., Alzahrani, A., Alghazzawi, D., & Aldabbagh, G. (2017). A type-2 fuzzy logic recommendation system for adaptive teaching. Soft Computing, 21(4), 965-979.
• Altun Türker, Y. (2012). Uzaktan eğitim öğretim yönetim sisteminin bulanık çok kriterli karar verme yöntemleri ile seçimi [Master’s Thesis]. Kocaeli Universitesi, Fen Bilimleri Enstitusu.
• Annabestani, M., Rowhanimanesh, A., Mizani, A., & Rezaei, A. (2019). Descriptive evaluation of students using fuzzy approximate reasoning. arXiv:1905.02549 [cs].
• Antony Rosewelt, L., & Arokia Renjit, J. (2020). A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN. Journal of Intelligent & Fuzzy Systems, 39(1), 795-808.
• Aydoğdu Karaaslan, I. (2019). Açık Kaynak Kodlu ve Ticari Web Tabanlı Uzaktan Eğitim Yazılımlarının Karşılaştırılması. Journal of International Social Research, 12(62), 979-990.
• Ayouni, S., Menzli, L. J., Hajjej, F., Madeh, M., & Al-Otaibi, S. (2021). Fuzzy Vikor Application for Learning Management Systems Evaluation in Higher Education. Http://Services.Igi-Global.Com/Resolvedoi/Resolve.Aspx?Doi=10.4018/IJICTE.2021040102, 17(2), 17-35.
• Azimjonov, J., Selvi̇, İ. H., & Özbek, U. (2016). Evaluatıon of dıstance learnıng students performance usıng fuzzy logıc. Yönetim Bilişim Sistemleri Dergisi, 2(2), 87-97.
• Azzi, I., Jeghal, A., Radouane, A., Yahyaouy, A., & Tairi, H. (2020). A robust classification to predict learning styles in adaptive E-learning systems. Education and Information Technologies, 25(1), 437-448.
• Baran, H. (2020). Açık ve uzaktan eğitimde ölçme ve değerlendirme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 6(1), 28-40.
• Barlybayev, A., Sharipbay, A., Ulyukova, G., Sabyrov, T., & Kuzenbayev, B. (2016). Student’s Performance Evaluation by Fuzzy Logic. Procedia Computer Science, 102, 98-105.
• Behr, A., Giese, M., K, H. D. T., & Theune, K. (2020). Early Prediction of University Dropouts – A Random Forest Approach. Jahrbücher Für Nationalökonomie Und Statistik, 240(6), 743-789.
• Bhattacharya, S., Chowdhury, S., & Roy, S. (2017). Enhancing Quality of Learning Experience Through Intelligent Agent in E-Learning. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(01), 31-52.
• Bozkurt, A., & Ucar, H. (2018). E-Öğrenme ve E-Sınavlar: Çevrimiçi Ölçme Değerlendirme Süreçlerinde Kimlik Doğrulama Yöntemlerine İlişkin Öğrenen Görüşlerinin İncelenmesi.
• Can, Ş. (2008). Fen eğitiminde web tabanlı eğitim. Celal. Bayar Üniversitesi.
• Cebi, A., & Karal, H. (2017). An application of fuzzy analytic hierarchy process (FAHP) for evaluating students project. Educational Research and Reviews, 12(3), 120-132.
• Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle. Frontiers in Psychology, 8, 1403.
• Cervero, A., Castro-Lopez, A., Álvarez-Blanco, L., Esteban, M., & Bernardo, A. (2020). Evaluation of educational quality performance on virtual campuses using fuzzy inference systems. PLOS ONE, 15(5), e0232802.
• Cisco Webex. (t.y.). Geliş tarihi 27 Şubat 2021, gönderen https://www.webex.com/
• Çöpgeven, S., & Fırat, M. (2019). Uzaktan eğitimde algoritmalar: 2007-2019 sistematik alanyazın taraması.
• Dashko, Y., Vitchenko, O., & Kadomtsev, M. (2020). Soft models of competence assessment in professional education. E3S Web of Conferences, 210, 18011.
• David, J., Lobov, A., & Lanz, M. (2018). Leveraging Digital Twins for Assisted Learning of Flexible Manufacturing Systems. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 529-535.
• Dias, S. B., Dolianiti, F. S., Hadjileontiadou, S. J., Diniz, J. A., & Hadjileontiadis, L. J. (2020). On modeling the quality of concept mapping toward more intelligent online learning feedback: A fuzzy logic-based approach. Universal Access in the Information Society, 19(3), 485-498.
• Doğ, M. F. (2012). Uzaktan Eğitim Sistemlerinde Kullanılabilirlik Ölçütleri [Yüksek Lisans Tezi]. Bahçeşehir Üniversitesi.
• Durak, G., Çankaya, S., & İzmirli, S. (2020). COVID-19 pandemi döneminde Türkiye’deki üniversitelerin uzaktan eğitim sistemlerinin incelenmesi. Necatibey Eğitim Fakültesi Elektronik Fen ve Matematik Eğitimi Dergisi, 14(1), 787-809.
• Echauz, J. R., & Vachtsevanos, G. J. (1995). Fuzzy Grading System. IEEE Transactions on Education, 38(2), 158-165.
• El Aissaoui, O., El Alami El Madani, Y., Oughdir, L., & El Allioui, Y. (2019). A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies, 24(3), 1943-1959.
• Ghatasheh, N. (2015). Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis. International Journal of Advanced Computer Science and Applications, 6(4).
• Gocheva-Ilieva, S., Kulina, H., & Ivanov, A. (2021). Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS. Mathematics, 9(1), 62.
• GoToMeeting. (t.y.). Geliş tarihi 27 Şubat 2021, gönderen https://www.gotomeeting.com/en-tr
• Gültaş, İ. (2007). Endüstri Mühendisliği Eğitiminde Matematik Ders İçeriklerinin Belirlenmesine Bulanık Ahp Yöntemi İle Çözüm Önerisi [Thesis, Fen Bilimleri Enstitüsü]. https://polen.itu.edu.tr/handle/11527/5845
• Hassan, S.-U., Waheed, H., Aljohani, N. R., Ali, M., Ventura, S., & Herrera, F. (2019). Virtual learning environment to predict withdrawal by leveraging deep learning. International Journal of Intelligent Systems, 34(8), 1935-1952.
• Herand, D., & Hatipoğlu, Z. A. (2014). Uzaktan Eğitim ve Uzaktan Eğitim Platformları’nın Karşılaştırılması. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(1).
• Hogo, M. A. (2010). Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert Systems with Applications, 37(10), 6891-6903.
• Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018, Ekim 2). Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores [Research Article]. Computational Intelligence and Neuroscience; Hindawi.
• Ingoley, S. N., & Bakal, J. W. (2012). Students’ performance evaluation using fuzzy logic. 2012 Nirma University International Conference on Engineering (NUiCONE), 1-6.
• Işık, A. H., Karacı, A., Özkaraca, O., & Biroğul, S. (2010). Web tabanlı eş zamanlı (senkron) uzaktan eğitim sistemlerinin karşılaştırmalı analizi. Akademik Bilişim, 10-12.
• İşman, A. (1998). Uzaktan eğitim: Genel tanımı, Türkiye’deki gelişimi, proje değerlendirmeleri. Değişim Yayınları.
• Ivanova, V., & Zlatanov, B. (2019). Implementation of Fuzzy Functions Aimed at Fairer Grading of Students’ Tests. Education Sciences, 9(3), 214.
• İzmirli, S., & Akyüz, H. İ. (2017). Eş Zamanlı Sanal Sınıf Yazılımlarının İncelenmesi. Eğitimde Kuram ve Uygulama, 13(4), 788-810.
• Jamsandekar, S. S., & Mudholkar, R. R. (2013). Performance Evaluation by Fuzzy Inference Technique. /paper/Performance-Evaluation-by-Fuzzy-Inference-Technique-Jamsandekar-Mudholkar/3fdf3fe33aaec8ce33873f6760c37af1c33dd3dc
• Jing, X., Yan, Z., Shen, Y., Pedrycz, W., & Yang, J. (2020). A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering. IEEE Transactions on Cybernetics, 1-14.
• Jyothi, G., Parvathi, M. C., Srinivas, M. P., & Althaf, M. S. (2014). Fuzzy Expert Model for Evaluation of Faculty Performance in Technical Educational Institutions. 4(5), 10.
• Karacı, A. (2013). Ses Sentezleme Ve Tanıma Teknolojilerini Kullanarak Türkçenin Ana Dil Olarak Öğretimi İçin Zeki Öğretim Sistemi Geliştirilmesi [Doktora Tezi]. Gazi Üniversitesi.
• Karadimas, N. V. (2018). Comparing Learning Management Systems from Popularity Point of View. 2018 5th International Conference on Mathematics and Computers in Sciences and Industry (MCSI), 141-146.
• Khawar, K., Munawar, S., & Naveed, N. (2020). Fuzzy Logic-based Expert System for Assessing Programming Course Performance of E-Learning Students. Journal of Information Communication Technologies and Robotic Applications, 54-64.
• Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting Students’ Performance in Distance Learning Using Machine Learning Techniques. Applied Artificial Intelligence, 18(5), 411-426.
• Küçükönder, N., & İbrahim, K. I. R. (2016). Uzaktan Eğitim Uygulamalarında Açık Kaynak Kodlu Öğrenme Yönetim Sistemlerinin Yeniden Yapılandırılmasının İncelenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 13(1).
• Lavolette, E., Venable, M. A., Gose, E., & Huang, E. (2010). Comparing synchronous virtual classrooms: Student, instructor and course designer perspectives. TechTrends, 54(5), 54-61.
• Lee, T.-S., Wang, C.-H., & Yu, C.-M. (2019). Fuzzy Evaluation Model for Enhancing E-Learning Systems. Mathematics, 7(10), 918.
• Lin, C.-B., Young, S. S.-C., Chan, T.-W., & Chen, Y.-H. (2005). Teacher-oriented adaptive Web-based environment for supporting practical teaching models: A case study of “school for all”. Computers & Education, 44(2), 155-172.
• Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), 372-380.
• Machado, M. A. S., Moreira, T. D. R. G., Gomes, L. F. A. M., Caldeira, A. M., & Santos, D. J. (2016). A Fuzzy Logic Application in Virtual Education. Procedia Computer Science, 91, 19-26.
• Mahboob, T., Irfan, S., & Karamat, A. (2016). A machine learning approach for student assessment in E-learning using Quinlan’s C4.5, Naive Bayes and Random Forest algorithms. 2016 19th International Multi-Topic Conference (INMIC), 1-8.
• Megahed, M., & Mohammed, A. (2020). Modeling adaptive E-Learning environment using facial expressions and fuzzy logic. Expert Systems with Applications, 157, 113460.
• Microsoft Teams. (t.y.). Geliş tarihi 27 Şubat 2021, gönderen https://www.microsoft.com/tr-tr/microsoft-teams/group-chat-software
• Moodle—Open-source learning platform | Moodle.org. (t.y.). Geliş tarihi 27 Şubat 2021, gönderen https://moodle.org/?lang=tr
• 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), e0231465.
• Ndukwe, I. G., Daniel, B. K., & Amadi, C. E. (2019). A Machine Learning Grading System Using Chatbots. Içinde S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, &
• R. Luckin (Ed.), Artificial Intelligence in Education (ss. 365-368). Springer International Publishing.
• Ozdemir, A., Alaybeyoglu, A., Mulayim, N., & Balbal, K. F. (2016). Performance evaluation of learning styles based on fuzzy logic inference system. Computer Applications in Engineering Education, 24(6), 853-865.
• Ozek, M., Akpolat, Z., & Orhan, A. (2010). Web tabanli akilli ög̀retim sistemlerinde tip-2 bulanik mantik kullanarak öǧrenci ög̀renme stili modelleme. Firat Üniversitesi Mühendislik Bilimleri Dergisi, 22, 37-44.
• Ölmez, Ç. (2010). Uzaktan Eğitim Sistemlerindeki Soru Bankalarının Bulanık Mantık Yöntemi İle Analizi [Yüksek Lisans Tezi, Afyon Kocatepe Üniversitesi]. http://acikerisim.aku.edu.tr/xmlui/handle/11630/6283
• Özkaraca, O. (2005). İnternet tabanlı güç elektroniği eğitimi [PhD Thesis]. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 1–125.
• Pandey, H., & Singh, V. K. (2015). A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework. International Journal of Computer Applications, 122(17), 18-21.
• Pariserum Perumal, S., Sannasi, G., & Arputharaj, K. (2019). An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests. The Journal of Supercomputing, 75(8), 5145-5160.
• Patriarcheas, K., & Xenos, M. (2009). Modelling of distance education forum: Formal languages as interpretation methodology of messages in asynchronous text-based discussion. Computers & Education, 52(2), 438-448.
• Raval, S., & Tailor, B. (2020). Mathematical Modelling of Students’ Academic Performance Evaluation Using Fuzzy Logic. International Journal of Statistics and Reliability Engineering, 7(1), 149-159.
• Robinson, C., Yeomans, M., Reich, J., Hulleman, C., & Gehlbach, H. (2016). Forecasting student achievement in MOOCs with natural language processing. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 383-387.
• Salmi, K., Magrez, H., & Ziyyat, A. (2014). A fuzzy expert system in evaluation for E-learning. 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), 225-229.
• Saraç, M. A. Y. (2020). Preparing a national roadmap for online higher education. University World News. https://www.universityworldnews.com/post.php?story=20200415120209980
• Schullo, S., Hilbelink, A., Venable, M., & Barron, A. E. (2007). Selecting a virtual classroom system: Elluminate live vs. Macromedia breeze (adobe acrobat connect professional). MERLOT Journal of Online Learning and Teaching, 3(4), 331-345.
• Sevindik, T., & Cömert, Z. (2010). Using algorithms for evaluation in web based distance education. Procedia-Social and Behavioral Sciences, 9, 1777-1780.
• Silva, J. C. S., Ramos, J. L. C., Rodrigues, R. L., Gomes, A. S., Souza, F. D. F. D., & Maciel, A. M. A. (2016). An EDM Approach to the Analysis of Students’ Engagement in Online Courses from Constructs of the Transactional Distance. 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), 230-231.
• Simonson, M., Smaldino, S., & Zvacek, S. M. (Ed.). (2014). Teaching and Learning at a Distance: Foundations of Distance Education, 3rd Edition (Revised ed. edition). Information Age Publishing.
• Sindre, G., & Vegendla, A. (2015). E-exams versus paper exams: A comparative analysis of cheating-related security threats and countermeasures. NISK Journal, 34-45.
• Sisovic, S., Matetic, M., & Bakaric, M. B. (2016). Clustering of imbalanced moodle data for early alert of student failure. 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 165-170.
• Slater, S., & Baker, R. (2019). Forecasting future student mastery. Distance Education, 40(3), 380-394.
• Sokkhey, P., & Okazaki, T. (2019). Comparative Study of Prediction Models on High School Student Performance in Mathematics. 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 1-4.
• Turan, C., Reis, Z. A., & Gülseçen, S. (2018). Bakış Takibi ile E-Öğrenme Materyalinde Konu Odağı ve Öğrenci Bakış Reflekslerinin İlgisini Değerlendirme.
• Turan, H. (2018). Assessment factors affecting e-learning using fuzzy analytic hierarchy process and SWARA. The International Journal of Engineering Education, 34(3), 915-923.
• Ulutaş, F., & Ubuz, B. (2008). Matematik Eğitiminde Araştırmalar ve Eğilimler: 2000 ile 2006 Yılları Arası. Ilkogretim Online, 7(3).
• Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2017). On predicting academic performance with process mining in learning analytics. Journal of Research in Innovative Teaching & Learning, 10(2), 160-176.
• Ünver, H. M. (2020). Design of a Fuzzy Logic Based Custom Exam Production System for High Performance. Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, 745-752.
• Vandamme, J.-P., Meskens, N., & Superby, J.-F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405.
• Waheed, H., Hassan, S.-U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189.
• Wardoyo, R., & Yuniarti, W. D. (2020). Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning. IJID (International Journal on Informatics for Development), 9(1), 29-36.
• Yıldırım, D., Tüzün, H., Çınar, M., Akıncı, A., Kalaycı, E., & Bilgiç, H. G. (2011). Uzaktan eğitimde kullanılan eşzamanlı sanal sınıf araçlarının karşılaştırılması. Akademik Bilişim, 451-456.
• Yıldız, O. (2014). Makine öğrenmesi ile uzaktan eğitim öğrencilerinin performanslarının değerlendirilmesi—Tez Arşivi [Doktora Tezi, İstanbul Üniversitesi]. https://tezarsivi.com/makine-ogrenmesi-ile-uzaktan-egitim-ogrencilerinin-performanslarinin-degerlendirilmesi
• Yildiz, O., Bal, A., & Gulsecen, S. (2013). Improved fuzzy modelling to predict the academic performance of distance education students. The International Review of Research in Open and Distributed Learning, 14(5).
• Zoom. (t.y.). Geliş tarihi 27 Şubat 2021, gönderen https://zoom.us/