Assessment and Evaluation Effects of Twitter Data on Distance Education

Yıl 2021, , 196 - 211, 24.12.2021

Tugba Guler

https://doi.org/10.53694/bited.876319

Öz

Changing the balances in the world, Covid-19 has also affected the education sector and caused innovations to be tried and used in the current education system for educators and students. The distance education system, which is one of these innovations and the common usage around the world in the Covid-19 period, has given students the opportunity to continue access and education more comfortably with the help of today's mobile devices. Assessment and evaluation, which aims to determine the knowledge level of students as a result of the education, is carried out with several different test formats online during distance education. Social media and internet provided great convenience to teachers and students in terms of resources and communication during the distance education period. This study performs the analysis of measurement and evaluation variables under the umbrella of distance education on Twitter, one of the popular social media examples. These analyzes were carried out using the RapidMiner platform, which is a data mining software. The analyzes aim to give an idea about the most trending topics in the field of education and to present a method suitable for the literature. As a result of the analysis, it has been understood that the use of Twitter is most common at the secondary education level in terms of measurement and evaluation in the distance education period.

Anahtar Kelimeler

distance education, Assessment Evaluation, Twitter, RapidMiner

Twitter Verilerinin Uzaktan Eğitimdeki Ölçme ve Değerlendirme Etkisi

Öz

Dünyada sağlık sektöründe ve daha birçok alanda dengeleri değiştiren Covid-19 virüsü, eğitim sektörünü de olumsuz yönde etkilemiştir. Öyle ki eğitmenler ve öğrenciler için mevcut eğitim sisteminin daha verimli devam edebilmesi için bu alanda yenilikler denenmesine ve bu yeniliklerin de kullanılmasına sebep olmuştur. Bahsedilen yeniliklerin başında gelen ve Covid-19 sürecinde tüm dünya genelinde yaygınlaşan uzaktan eğitim sistemi bir diğer adıyla çevrimiçi eğitim sistemi, günümüzde kullanılan tablet, telefon gibi mobil cihazların yardımıyla birlikte her yönden öğrencilere erişimi ve eğitimi daha rahat sürdürebilme imkânı vermiştir. Öğrencilerin yapılan eğitim sonucunda bilgi seviyelerinin belirlenmesini amaçlayan ölçme ve değerlendirme, uzaktan eğitim süresince çevrimiçi olan farklı birkaç test formatı ile yürütülmektedir. Sosyal medya ve internet ise günümüz kullanım popülaritesinin yanı sıra uzaktan eğitim periyodunda da öğretmen ve öğrencilere kaynak ve iletişim açısından büyük kolaylıklar sağlamaktadır. Bu çalışma, popüler sosyal medya örneklerinden biri olan Twitter’da uzaktan eğitim çatısı altındaki ölçme değerlendirme değişkenlerinin analizini gerçekleştirmektedir. Yapılan bu analizler, bir veri madenciliği yazılımı olan RapidMiner platformu kullanılarak gerçekleştirilmiştir. Analizler, eğitim alanında en trend olan başlıklar hakkında fikir verme ve alanyazına uygun bir yöntem sunma amacı gütmektedir. Analizlerin sonucunda, Twitter kullanımının uzaktan eğitim döneminde ölçme ve değerlendirme açısından en çok ortaöğretim düzeyinde yaygın olduğu anlaşılmıştır.

Anahtar Kelimeler

Uzaktan Eğitim, Ölçme, Değerlendirme, Twitter, RapidMiner

Kaynakça

• Adedoyin, Olasile Babatunde; Soykan, Emrah (2020). Covid-19 pandemic and online learning: the challenges and opportunities. Interactive Learning Environments, 1-13.
• Anderson, T. (Ed.). (2008). The theory and practice of online learning. Athabasca University Press.
• Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1), 29-42.
• Astin, A. W. (2012). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Rowman & Littlefield Publishers.
• Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 1, pp. 492-499). IEEE.
• Bahasoan, A. N., Ayuandiani, W., Mukhram, M., & Rahmat, A. (2020). Effectiveness of online learning in pandemic COVID-19. International Journal of Science, Technology & Management, 1(2), 100-106.
• Bozkurt, A., & Sharma, R. C. (2020). Emergency remote teaching in a time of global crisis due to CoronaVirus pandemic. Asian Journal of Distance Education, 15(1), i-vi.
• Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020). Using technology to maintain the education of residents during the COVID-19 pandemic. Journal of surgical education, 77(4), 729-732.
• Das, D. D., Sharma, S., Natani, S., Khare, N., & Singh, B. (2017). Sentimental analysis for airline twitter data. In IOP Conference Series: Materials Science and Engineering (Vol. 263, No. 4, p. 042067). IOP Publishing.
• Dubey, P., & Pandey, D. (2020). Distance learning in higher education during pandemic: challenges and opportunities. Int. J. Indian Psychol, 8(2), 43-46.
• Faizi, R., El Afia, A., & Chiheb, R. (2013). Exploring the potential benefits of using social media in education. International Journal of Engineering Pedagogy (iJEP), 3(4), 50-53.
• Flaherty, C. Grading for a pandemic. Inside Higher Ed, 2020.
• Hovorka, D. and Rees, M. J. (2009). Active collaboration learning environments: The class of Web 2.0, 20th Australasian Conference on Information Systems: ACIS 2009, Melbourne, Australia.
• Kunnakorntammanop, S., Thepwuttisathaphon, N., & Thaicharoen, S. (2019). An experience report on building a big data analytics framework using Cloudera CDH and RapidMiner Radoop with a cluster of commodity computers. In International Conference on Soft Computing in Data Science (pp. 208-222). Springer, Singapore.
• Liu, Min, et al. Web 2.0 and its use in higher education from 2007-2009: A review of literature. International Journal on E-Learning, 2012, 11.2: 153-179.
• Osterlind, S. (2002). Constructing Test Items: Multiple-Choice, Constructed-Response, Performance, and Other Formats. New York: Kluwer Academic Publishers.
• Selwyn, N., & Stirling, E. (2016). Social media and education now the dust has settled. Learning, media and technology, 41(1), 1-5.
• Sherry, L. (1995). Issues in distance learning. International journal of educational telecommunications, 1(4), 337-365.
• Sinoplu, M., & Yılmaz, R. (2020). Social media analysis in distance education period due to pandemic: data mining application on Twitter data. Journal of Teacher Education and Lifelong Learning, 2(2), 66-76.
• Udayakumar, S., Senadeera, D. C., Yamunarani, S., & Cheon, N. J. (2018). Demographics analysis of twitter users who tweeted on psychological articles and tweets analysis. Procedia computer science, 144, 96-104.
• Valentine, D. (2002). Distance learning: Promises, problems, and possibilities. Online journal of distance learning administration, 5(3).