Year 2018, Volume 7, Issue 1, Pages 286 - 297 2018-06-28

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

Zafer C├Âmert [1] , ├ľzge C├ľMERT [2]

66 136

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, ­ŁĹś-nearest neighbors, decision tree and Na├»ve 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.
Distance Education, Web Technologies, Learning Management Systems, Algorithms
  • Abdi H and Williams LJ (2010) Principal component analysis. Wiley interdisciplinary reviews: computational statistics, Wiley Online Library 2(4): 433ÔÇô459.
  • Aha DW, Kibler D and Albert MK (1991) Instance-based learning algorithms. Machine Learning 6(1): 37ÔÇô66. 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 uygulamalar─▒. In: XII. T├╝rkiyeÔÇÖde Internet Konferans─▒.
  • Bibeault B and Kats Y (2008) jQuery in Action. Dreamtech Press.
  • Borham-Puyal M and Olmos-Miguel├í├▒ez S (2011) Improving the use of feedback in an online teaching-learning environment: An experience supported by Moodle. US- China Foreign Language 9(6): 371ÔÇô382.
  • Cohen MA (2001) Automated web site creation using template driven generation of active server page applications. Google Patents.
  • C├Âmert Z (2012) Web madencili─či entegre edilmi┼č semantik web tabanl─▒ ├Â─črenme ortamlar─▒n─▒n ├Â─črenci akademik ba┼čar─▒ ve tutumlar─▒na etkisi. F─▒rat ├ťniversitesi.
  • C├Âmert Z, Sevindik T and Gen├ž 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. 902ÔÇô908.
  • C├Âmert Z, Kocamaz AF and ├ç─▒buk M (2015) Web Tabanl─▒ Hibrit Bir Uygulama Modeliyle Personel Bilgi Sistemi Tasar─▒m─▒. In: Akademik Bili┼čim, Eski┼čehir, T├╝rkiye.
  • Demirli C and K├╝t├╝k ├ľF (2010) Anlamsal Web (Web 3.0) ve ontolojilerine genel bir bak─▒┼č. ─░stanbul Ticaret ├ťniversitesi Fen Bilimleri Dergisi, {\.I}stanbul Ticaret ├ťniversitesi 18(9).
  • Garrison DR (1985) Three generations of technological innovations in distance education. Distance education, Taylor & Francis 6(2): 235ÔÇô241.
  • Gen├ž Z (2010) Web 2.0 yeniliklerinin e─čitimde kullan─▒m─▒: Bir Facebook e─čitim uygulama ├Ârne─či. In: Akademik Bili┼čim, pp. 237ÔÇô242.
  • 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): 597ÔÇô610. Available from: http://dx.doi.org/10.1080/18756891.2012.696923.
  • Karabatak M (2008) ├ľzellik Se├žimi, S─▒n─▒flama ve ├ľng├Âr├╝ Uygulamalar─▒na Y├Ânelik Birliktelik Kural─▒ ├ç─▒kar─▒m─▒ ve Yaz─▒l─▒m Geli┼čtirilmesi. F─▒rat University Turkey.
  • Karaman S, Y─▒ld─▒r─▒m S and Kaban A (2008) ├ľ─črenme 2.0 yayg─▒nla┼č─▒yor: Web 2.0 uygulamalar─▒n─▒n e─čitimde kullan─▒m─▒na ili┼čkin ara┼čt─▒rmalar ve sonu├žlar─▒. In: XIII. T├╝rkiyeÔÇÖde ─░nternet Konferans─▒, 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. 389ÔÇô428.
  • Kotsiantis SB (2012) Use of machine learning techniques for educational proposes: a decision support system for forecasting studentsÔÇÖ grades. Artificial Intelligence Review 37(4): 331ÔÇô344. Available from: https://doi.org/10.1007/s10462-011-9234-x.
  • Livieris IE, Drakopoulou K and Pintelas P (2012) Predicting studentsÔÇÖ performance using artificial neural networks. In: 8th PanHellenic Conference with International Participation Information and Communication Technologies in Education, pp. 321ÔÇô328.
  • 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. 841ÔÇô848.
  • Pandey M and Taruna S (2014) A Multi-level Classification Model Pertaining to The StudentÔÇÖs 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): 437ÔÇô448.
  • 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): 660ÔÇô674.
  • Schwenk H and Bengio Y (1998) Training methods for adaptive boosting of neural networks. In: Advances in neural information processing systems, pp. 647ÔÇô653.
  • Sevindik T and C├Âmert Z (2010) Using algorithms for evaluation in web based distance education. In: Procedia - Social and Behavioral Sciences, pp. 1777ÔÇô1780.
  • Sevindik T, Demirkeser N and C├Âmert Z (2010) Virtual education environments and web mining. In: Procedia - Social and Behavioral Sciences, pp. 5120ÔÇô5124.
  • 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. 391ÔÇô398.
  • Varank I, Fatih Erko├ž M, B├╝y├╝kimdat 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): 395ÔÇô404.
  • Wood L, Nicol G, Robie J, et al. (2004) Document Object Model (DOM) level 3 core specification. W3C Recommendation.
  • Y├Ândem D (2009) ASP. net 3.5 AJAX. Pusula Yay─▒nc─▒l─▒k.
  • Yu J, Benatallah B, Casati F, et al. (2008) Understanding Mashup Development. IEEE Internet Computing 12(5): 44ÔÇô52.
Journal Section Ara┼čt─▒rma Makaleleri
Authors

Orcid: 0000-0001-5256-7648
Author: Zafer C├Âmert (Primary Author)
Institution: B─░TL─░S EREN ├ťN─░VERS─░TES─░
Country: Turkey


Orcid: 0000-0001-7419-1848
Author: ├ľzge C├ľMERT
Institution: B─░TL─░S EREN ├ťN─░VERS─░TES─░
Country: Turkey


Bibtex @review { bitlissos376426, journal = {Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi}, issn = {2147-5962}, eissn = {2147-5598}, address = {Bitlis Eren University}, year = {2018}, volume = {7}, pages = {286 - 297}, doi = {}, title = {A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms}, key = {cite}, author = {C├Âmert, Zafer and C├ľMERT, ├ľzge} }
APA C├Âmert, Z , C├ľMERT, ├ľ . (2018). A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms. Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi, 7 (1), 286-297. Retrieved from http://dergipark.org.tr/bitlissos/issue/38061/376426
MLA C├Âmert, Z , C├ľMERT, ├ľ . "A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms". Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi 7 (2018): 286-297 <http://dergipark.org.tr/bitlissos/issue/38061/376426>
Chicago C├Âmert, Z , C├ľMERT, ├ľ . "A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms". Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi 7 (2018): 286-297
RIS TY - JOUR T1 - A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms AU - Zafer C├Âmert , ├ľzge C├ľMERT Y1 - 2018 PY - 2018 N1 - DO - T2 - Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi JF - Journal JO - JOR SP - 286 EP - 297 VL - 7 IS - 1 SN - 2147-5962-2147-5598 M3 - UR - Y2 - 2018 ER -
EndNote %0 Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms %A Zafer C├Âmert , ├ľzge C├ľMERT %T A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms %D 2018 %J Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi %P 2147-5962-2147-5598 %V 7 %N 1 %R %U
ISNAD C├Âmert, Zafer , C├ľMERT, ├ľzge . "A Study of Technologies Used in Learning Management Systems and Evaluation of New Trend Algorithms". Bitlis Eren ├ťniversitesi Sosyal Bilimler Enstit├╝s├╝ Dergisi 7 / 1 (June 2018): 286-297.