A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms

Year 2018, Volume: 7 Issue: 1, 286 - 297, 28.06.2018

Zafer Cömert Özge Cömert

Abstract

Distance education is a completely different way of learning, separated from traditional face-to-face education, independent of time and place. The journey of distance education that started with communication tools such as letters, radio, and television continues to evolve based on the use of web-based technologies such as social media and learning management systems (LMSs), depending on the developments in technology today. In this study, a review has been carried out to outline the technologies used in LMS, first. In particular, the developments of the widely used advanced algorithms and LMSs have been taken into consideration in the study by examining internet-web based technologies and standards. Then, an investigation on new trends algorithms in this field has been performed. In this scope, five supervised (linear regression, logistic regression, 𝑘-nearest neighbors, decision tree and Naïve Bayes), two unsupervised (Apriori and principal component analysis) and lastly one ensemble learning algorithm (Adaptive Boosting) have been examined. Consequently, the new algorithms have been proposed to be used for different purposes, such as analyzing of users' hidden behaviors, performance prediction, producing automatic recommendations as well.

Keywords

Distance Education, Web Technologies, Learning Management Systems, Algorithms

References

• Abdi H and Williams LJ (2010) Principal component analysis. Wiley interdisciplinary reviews: computational statistics, Wiley Online Library 2(4): 433–459.
• Aha DW, Kibler D and Albert MK (1991) Instance-based learning algorithms. Machine Learning 6(1): 37–66. Available from: https://doi.org/10.1007/BF00153759.
• Andersen P (2007) What is Web 2.0?: ideas, technologies and implications for education. JISC Bristol.
• Aslan B (2007) Web 2.0, teknikleri ve uygulamaları. In: XII. Türkiye’de Internet Konferansı.
• Bibeault B and Kats Y (2008) jQuery in Action. Dreamtech Press.
• Borham-Puyal M and Olmos-Migueláñez S (2011) Improving the use of feedback in an online teaching-learning environment: An experience supported by Moodle. US- China Foreign Language 9(6): 371–382.
• Cohen MA (2001) Automated web site creation using template driven generation of active server page applications. Google Patents.
• Cömert Z (2012) Web madenciliği entegre edilmiş semantik web tabanlı öğrenme ortamlarının öğrenci akademik başarı ve tutumlarına etkisi. Fırat Üniversitesi.
• Cömert Z, Sevindik T and Genç Z (2011) The Use Of Google Chart for Visual Presentation of Data In Semantic Web Based Learning Management System. In: 5th International Computer & Instructional Technologies Symposium, pp. 902–908.
• Cömert Z, Kocamaz AF and Çıbuk M (2015) Web Tabanlı Hibrit Bir Uygulama Modeliyle Personel Bilgi Sistemi Tasarımı. In: Akademik Bilişim, Eskişehir, Türkiye.
• Demirli C and Kütük ÖF (2010) Anlamsal Web (Web 3.0) ve ontolojilerine genel bir bakış. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, {\.I}stanbul Ticaret Üniversitesi 18(9).
• Garrison DR (1985) Three generations of technological innovations in distance education. Distance education, Taylor & Francis 6(2): 235–241.
• Genç Z (2010) Web 2.0 yeniliklerinin eğitimde kullanımı: Bir Facebook eğitim uygulama örneği. In: Akademik Bilişim, pp. 237–242.
• Gerken T and Ratschiller T (2000) Web Application Development with PHP. New Riders Publishing.
• Graham IS (1995) The HTML sourcebook. John Wiley & Sons, Inc.
• Jovanovic M, Vukicevic M, Milovanovic M, et al. (2012) Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study. International Journal of Computational Intelligence Systems, Taylor & Francis 5(3): 597–610. Available from: http://dx.doi.org/10.1080/18756891.2012.696923.
• Karabatak M (2008) Özellik Seçimi, Sınıflama ve Öngörü Uygulamalarına Yönelik Birliktelik Kuralı Çıkarımı ve Yazılım Geliştirilmesi. Fırat University Turkey.
• Karaman S, Yıldırım S and Kaban A (2008) Öğrenme 2.0 yaygınlaşıyor: Web 2.0 uygulamalarının eğitimde kullanımına ilişkin araştırmalar ve sonuçları. In: XIII. Türkiye’de İnternet Konferansı, p. 35.
• Keegan D (1996) Foundations of distance education. Psychology Press.
• Kleinbaum DG and Klein M (2010) Analysis of matched data using logistic regression. In: Logistic regression, Springer, pp. 389–428.
• Kotsiantis SB (2012) Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review 37(4): 331–344. Available from: https://doi.org/10.1007/s10462-011-9234-x.
• Livieris IE, Drakopoulou K and Pintelas P (2012) Predicting students’ performance using artificial neural networks. In: 8th PanHellenic Conference with International Participation Information and Communication Technologies in Education, pp. 321–328.
• Moore MG (2013) Handbook of distance education. Routledge.
• Ng AY and Jordan MI (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Advances in neural information processing systems, pp. 841–848.
• Pandey M and Taruna S (2014) A Multi-level Classification Model Pertaining to The Student’s Academic Performance Prediction. International Journal of Advances in Engineering & Technology, IAET Publishing Company 7(4): 1329.
• Preacher KJ, Curran PJ and Bauer DJ (2006) Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of educational and behavioral statistics, Sage Publications Sage CA: Los Angeles, CA 31(4): 437–448.
• Ramakrishnan R and Gehrke J (2000) Database management systems. McGraw Hill.
• Safavian SR and Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, IEEE 21(3): 660–674.
• Schwenk H and Bengio Y (1998) Training methods for adaptive boosting of neural networks. In: Advances in neural information processing systems, pp. 647–653.
• Sevindik T and Cömert Z (2010) Using algorithms for evaluation in web based distance education. In: Procedia - Social and Behavioral Sciences, pp. 1777–1780.
• Sevindik T, Demirkeser N and Cömert Z (2010) Virtual education environments and web mining. In: Procedia - Social and Behavioral Sciences, pp. 5120–5124.
• Silberschatz A, Korth HF, Sudarshan S, et al. (1997) Database system concepts. McGraw-Hill New York.
• Spivack N (2007) How the WebOS evolves? Nova Spivack. Available from: http://www.novaspivack.com/technology/how-the-webos-evolves.
• Thomas JP, Thomas M and Ghinea G (2003) Modeling of web services flow. In: E-Commerce, 2003. CEC 2003. IEEE International Conference on, pp. 391–398.
• Varank I, Fatih Erkoç M, Büyükimdat MK, et al. (2014) Effectiveness of an online automated evaluation and feedback system in an introductory computer literacy course. Eurasia Journal of Mathematics, Science and Technology Education, Eurasian Society of Educational Research 10(5): 395–404.
• Wood L, Nicol G, Robie J, et al. (2004) Document Object Model (DOM) level 3 core specification. W3C Recommendation.
• Yöndem D (2009) ASP. net 3.5 AJAX. Pusula Yayıncılık.
• Yu J, Benatallah B, Casati F, et al. (2008) Understanding Mashup Development. IEEE Internet Computing 12(5): 44–52.