E-Öğrenme Araştırmalarındaki Temel Eğilimler ve Bilgi Alanları: 2008-2018 Yılları Arasında Yayımlanan Makalelerle Konu Modelleme Analizi

Yıl 2020, Cilt: 8 Sayı: 16, 738 - 756, 20.10.2020

Fatih Gürcan , Özcan Özyurt

https://doi.org/10.18009/jcer.769349

Cited By: 9

Öz

Son yıllarda e-öğrenme konusunda, farklı alanlarda birçok çalışma gerçekleştirilmiştir. E-öğrenme alanında yapılan çalışmaların bütünleşik olarak geniş bir perspektif ile incelenmesi ve alanın genel bir resminin görülmesi son derece zordur. Bu çalışmada, e-öğrenme alanında son on yılda gerçekleştirilmiş olan tüm çalışmalar taranarak 27.735 dergi makalesi üzerinde olasılıksal konu modellemeye dayalı bir içerik analizi gerçekleştirilmiştir. Metin madenciliği yöntemleri ile yapılan analizler sonucunda e-öğrenmenin temel boyutları olarak değerlendirilebilecek beş ana boyut keşfedilmiştir. Ölçme ve değerlendirme, öğrenme ortamları, öğretim modelleri, öğretim alanları ve öğretim araçları olarak isimlendirilen bu beş ana boyutun e-öğrenme çalışmalarına ciddi katkılar sunabileceği öngörülmektedir.

Anahtar Kelimeler

E-öğrenme eğilimleri, konu modelleme, gizli dirichlet tahsisi, e-öğrenme araştırma ve uygulama alanları

Emerging Trends and Knowledge Domains in E-Learning Researches: Topic Modeling Analysis with the Articles Published between 2008-2018

Öz

In recent years, many studies on e-learning have been carried out in different fields. It is extremely difficult to examine the studies carried out in the field of e-learning from a broad perspective and to see a general picture of the field. In this study, all studies conducted in the field of e-learning in the last ten years were extracted and a content analysis based on probabilistic topic modeling was performed on 27,735 journal articles. As a result of this analysis performed by text mining methods, five main dimensions which can be considered as the main dimensions of e-learning have been discovered. These five main dimensions, which are named as measurement and evaluation, learning environments, teaching models, teaching areas, and teaching tools, are also considered to be able to contribute significantly to e-learning studies.

Anahtar Kelimeler

E-learning trends, topic modeling, latent dirichlet allocation, e-learning research and application domains

Kaynakça

• Aggarwal, C. C., & Zhai, C. (2013). Mining text data. Springer, USA.
• Allen, M., Mabry, E., Mattrey, M., Bourhis, J., Titsworth, S., & Burrell, N. (2004). Evaluating the effectiveness of distance learning: A comparison using meta-analysis. Journal of Communication, 54(3), 402-420.
• Al-Samarraie, H., Selim, H., Teo, T., & Zaqout, F. (2017). Isolation and distinctiveness in the design of e-learning systems influence user preferences. Interactive Learning Environments, 25(4), 452-466.
• Bernard, R. M., Bethel, E. C., Borokhovski, E., Abrami, P. C., Wade, C. A., Surkes, M. A., & Tamim, R. M. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243-1289.
• Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87-122.
• Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
• Blei, D. M., Edu, B. B., Ng, A. Y., Edu, A. S., Jordan, M. I., & Edu, J. B. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
• Cinquin, P. A., Guitton, P., & Sauzéon, H. (2019). Online e-learning and cognitive disabilities: A systematic review. Computers & Education, 130(2019), 152-167.
• Demi̇r, C , & Maskan, A . (2014). Web destekli öğrenme halkası yaklaşımı uygulamalarına ilişkin öğrenci görüşleri. Journal of Computer and Education Research, 2(3), 136-150.
• Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.
• Göksu, İ. & Atıcı, B (2015). Web tabanlı öğrenme ortamında veri madenciliğine dayalı öğrenci değerlendirmesi. Journal of Computer and Education Research, 3(5), 59-76.
• González, C. L., Saroil, D., & Sánchez, Y. (2015). Scientific production on e-learning in Latin America, a preliminary study from SciELO database. Revista Cubana de Educacion Medica Superior, 29(1), 155–165.
• Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(Supplement 1), 5228–5235.
• Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2), 211–244.
• Guàrdia, L., Crisp, G., & Alsina, I. (2017). Trends and challenges of e-assessment to enhance student learning in higher education. In E. Cano & G. Ion (Ed.), Innovative practices for higher education assessment and measurement(pp. 36-56). IGI Global, USA.
• Gurcan, F. (2018). Multi-class classification of turkish texts with machine learning algorithms. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE.
• Gurcan, F. (2019). Extraction of core competencies for big data: implications for competency-based engineering education. International Jounal of Engineering Education, 35(4), 1110-1115.
• Gürcan, F. (2009). Web içerik madenciliği ve konu sınıflandırması [Web content mining and subject classification], Master Thesis, Karadeniz Technical University, Institute of Science, Trabzon.
• Hung, J. L. (2012). Trends of e-learning research from 2000 to 2008: Use of text mining and bibliometrics. British Journal of Educational Technology, 43(1), 5–16.
• Hung, J. L., & Zhang, K. (2012). Examining mobile learning trends 2003-2008: A categorical meta-trend analysis using text mining techniques. Journal of Computing in Higher Education, 24(1), 1-17.
• Martínez-Caro, E., Cegarra-Navarro, J. G., ve Cepeda-Carrión, G. (2015). An application of the performance-evaluation model for e-learning quality in higher education. Total Quality Management and Business Excellence, 26(5-6), 632-647.
• McCallum, A. K. (2002). MALLET: A Machine Learning for Language Toolkit. http://Mallet.Cs.Umass.Edu. Retrieved from http://mallet.cs.umass.edu
• Simonson, M., Schlosser, C., & Orellana, A. (2011). Distance education research: A review of the literature. Journal of Computing in Higher Education. 23, 124-142.
• Srivastava, A. N., & Sahami, M. (2009). Text mining: Classification, clustering, and applications. CRC Press.
• Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077.
• URL-1:https://www.elsevier.com/__data/assets/pdf_file/0007/69451/0597-Scopus-Content-Coverage-Guide-US-LETTER-v4-HI-singles-no-ticks.pdf, Accessed: December 2018.
• Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing and Management, 50(1), 104–112.
• Wallach, H. M. (2006). Topic modeling: Beyond bag-of-words. ICML, (1), 977–984.
• Wanner, T., & Palmer, E. (2015). Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers and Education, 88, 354-369.
• Wu, W. H., Jim Wu, Y. C., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers and Education, 59(2), 817-827.
• Zawacki-Richter, O., ve Naidu, S. (2016). Mapping research trends from 35 years of publications in Distance Education. Distance Education, 37(3), 245-269.