Uyarlanabilir Çevrimiçi İngilizce Seviye Tespit Sınavı ile Türkiye’deki İngilizce Seviyesinin Analizi
Abstract
Globalleşen dünyamızda İngilizce artık dünya dili haline gelmiştir. Çoğu ülkede İngilizce; dili ana dil dışında, ikinci dil olarak öğretilmektedir. Gerek ülke politikaları olsun gerekse bireysel tercihler olsun İngilizce dilini öğrenmek için yüksek miktarlarda para harcanmaktadır. Bu alanda özellikle ülkemizde yapılacak çalışmalara, gerçekleştirilecek yatırımlara, karar verilecek politikalara ışık tutmak amacıyla bu çalışmada, ülkemizdeki İngilizce seviyesinin analizi yapılmış ve faydalı bulgular elde edilmiştir. Bu analizin yapılması için proje kapsamında geliştirilen uyarlanabilir İngilizce çevrimiçi eğitim sistemi kullanılarak çok fazla sayıda gerçek kişiye çevrimiçi seviye tespit sınavı uygulanmıştır. Türkiye’deki her bir şehir ve bölge açısından faydalı bilgiler elde edilmiştir. Genel anlamda İngilizce seviyesinin düşük çıktığı ülkemizde beceri bazlı analiz yapılmış ve durumun bazı beceriler açısından daha da kötü olduğunu görülmüştür. Yaş ve cinsiyet bazlı analizler yapılmış olup çalışmada detaylı grafikleri verilmiştir. Çevrimiçi seviye tespit sınavlarında sınav süresi önemli bir etken olduğu için bu çalışmanın ana amaçlarından biri, çevrimiçi seviye tespit sınavlarında makul süresi belirleyebilmektir. Yapay zeka tekniklerini de kullanarak bu süre kabul edilebilir düzeye düşürülebilir. Belli yaş grupları ve özelliklere göre sistem geliştirecek girişimcilerin, bu çalışmadaki analizleri dikkate alıp geliştirmeyi ona göre yapmaları maksimum fayda almada önem arz etmektedir.
Supporting Institution
TÜBİTAK
Project Number
3180703
Thanks
Bu çalışma TÜBİTAK 1501 kapsamında gerçekleştirilen, 3180703’nolu projeden elde edilen sonuçlardan oluşturulmuştur. Çalışmaya katkılarından dolayı Smart Eğitim Çözümleri Sanayi ve Tic. A.Ş.’ye teşekkür ederiz.
References
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- [9] F. Karahan, “Language attitudes of Turkish students towards the English language and its use in Turkish context,” Cankaya Univ. J. Arts Sci., vol. 1, no. 7, pp. 73–87, Aug. 2007.
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- [12] Y. Wang, M.-H. Tseng, and H.-C. Liao, “Data mining for adaptive learning sequence in English language instruction,” Expert Syst. Appl., vol. 36, no. 4, pp. 7681–7686, May 2009, doi: 10.1016/j.eswa.2008.09.008.
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- [14] P. Brusilovsky, “From Adaptive Hypermedia to the Adaptive Web,” 2003, pp. 21–24.
- [15] “Anki.” [Online]. Available: https://apps.ankiweb.net/.
- [16] J. YANG and J. PARK, “Software Review: SuperMemo UX-Extreme English Advanced and Proficient,” CALICO J., vol. 29, no. 4, pp. 718–726, 2012.
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Abstract
In our globalizing world, English has become the world language. English in most countries; is taught as a second language besides the native language. High amounts of money are spent on English language learning, whether it is country policies or individual preferences. In this study, in order to shed light on the studies to be carried out, investments to be made and policies to be decided in this field, the level of English in our country was analyzed and useful results were obtained. In order to perform this analysis, using the adaptive English online education system developed within the scope of the project used and a huge number of real person online exam information was obtained. Useful information was obtained for each of the cities and regions in Turkey. In general, skill level analysis was conducted in our country where the level of English was low and it was found that the situation was even worse in terms of some skills. Age and gender analyze were made and detailed graphics were given in the study. One of the main objectives of this study is to determine the reasonable time in online placement exams, since the exam duration is an important factor in online placement exams. This period can be reduced to an acceptable level by using artificial intelligence techniques. It is important that the entrepreneurs who will develop the system according to certain age groups and characteristics take into consideration the analyzes in this study and develop them accordingly.
Project Number
3180703
References
- [1] M. Saville-Troike and K. Barto, Introducing second language acquisition. Cambridge University Press, 2016.
- [2] A. Insight, “The 2015-2020 Worldwide Digital English Language Learning Market,” 2016.
- [3] H. Aydoğan and A. A. Akbarov, “The Four Basic Language Skills, Whole Language & Intergrated Skill Approach in Mainstream University Classrooms in Turkey,” Mediterr. J. Soc. Sci., May 2014, doi: 10.5901/mjss.2014.v5n9p672.
- [4] A. Pardo, “Designing Learning Analytics Experiences,” in Learning Analytics, New York, NY: Springer New York, 2014, pp. 15–38.
- [5] M. Gusev and G. Armenski, “E-Assessment Systems and Online Learning with Adaptive Testing,” 2014, pp. 229–249.
- [6] H.-K. Wu, C.-Y. Kuo, T.-H. Jen, and Y.-S. Hsu, “What makes an item more difficult? Effects of modality and type of visual information in a computer-based assessment of scientific inquiry abilities,” Comput. Educ., vol. 85, pp. 35–48, Jul. 2015, doi: 10.1016/j.compedu.2015.01.007.
- [7] B. V. Koen, “Creating a Sense of ‘Presence’ in a Web-Based PSI Course: The Search for Mark Hopkins’ Log in a Digital World,” IEEE Trans. Educ., vol. 48, no. 4, pp. 599–604, Nov. 2005, doi: 10.1109/TE.2005.850712.
- [8] M. H. R. Nakayama, “The Impact of Learner Characteristics on Learning Performance in Hybrid Courses among Japanese Students.,” Electron. J. e-Learning, vol. 5, no. 3, pp. 195–206, 2007.
- [9] F. Karahan, “Language attitudes of Turkish students towards the English language and its use in Turkish context,” Cankaya Univ. J. Arts Sci., vol. 1, no. 7, pp. 73–87, Aug. 2007.
- [10] T.-C. Hsieh, T.-I. Wang, C.-Y. Su, and M.-C. Lee, “A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning,” J. Educ. Technol. Soc., vol. 15, no. 1, pp. 273–288, 2012.
- [11] C. Troussas, K. Chrysafiadi, and M. Virvou, “An intelligent adaptive fuzzy-based inference system for computer-assisted language learning,” Expert Syst. Appl., vol. 127, pp. 85–96, Aug. 2019, doi: 10.1016/j.eswa.2019.03.003.
- [12] Y. Wang, M.-H. Tseng, and H.-C. Liao, “Data mining for adaptive learning sequence in English language instruction,” Expert Syst. Appl., vol. 36, no. 4, pp. 7681–7686, May 2009, doi: 10.1016/j.eswa.2008.09.008.
- [13] P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” in The Adaptive Web, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 3–53.
- [14] P. Brusilovsky, “From Adaptive Hypermedia to the Adaptive Web,” 2003, pp. 21–24.
- [15] “Anki.” [Online]. Available: https://apps.ankiweb.net/.
- [16] J. YANG and J. PARK, “Software Review: SuperMemo UX-Extreme English Advanced and Proficient,” CALICO J., vol. 29, no. 4, pp. 718–726, 2012.
- [17] B. McLaren, “Adaptive Online Learning The Present and Future of Education,” Wiley, 2013.
- [18] “Dualingo.” [Online]. Available: https://tr.duolingo.com/. [Accessed: 27-Jan-2020].
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | 3180703 |
| Publication Date | September 30, 2020 |
| Submission Date | March 9, 2020 |
| Published in Issue | Year 2020 Volume: 8 Issue: 3 |
