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İngilizce Kitapların CEFR Düzeylerinin Belirlenmesinde Web Programlama Teknikleri

Yıl 2026, Cilt: 10 Sayı: 23 , 34 - 49 , 30.04.2026
https://doi.org/10.57135/jier.1731184
https://izlik.org/JA48UC73ZR

Öz

Bir dilin en anlamlı birimi “kelime” olarak adlandırılabilir. Sadece kelimeleri kullanarak, dilbilgisel yapılara başvurmadan da belli ölçüde iletişim kurmak mümkündür. Bu nedenle, bir dilde en sık kullanılan kelimelerin öğrenilmesi ve öğretilmesi önemlidir. Kelime öğreniminde en çok kullanılan beceri ise okuma becerisidir. Basitleştirilmiş kitaplar, okuma becerisini edinmede başvurulan temel kaynaklardır. Çalışmamızda, CEFR kelime listesinde yer alan kelimelerin öğretimi sürecinde eğitmenler tarafından önerilen kitap veya okuma materyallerinin öğrencilerin düzeylerine uygun olup olmadığını belirlemek amacıyla web tabanlı bir uygulama geliştirmek hedeflenmiştir. Sistem Geliştirme Yaşam Döngüsü (SGYD) yöntemiyle geliştirilen bu uygulamanın bir karar destek sistemi olarak kullanılması amaçlanmaktadır. Bilindiği üzere, öğrencinin seviyesine uygun olmayan kitaplar öğrenme sürecini olumsuz etkileyebilmekte, hatta bazı durumlarda öğrencinin okuma sürecini tamamen sonlandırarak öğrenme amacının bozulmasına yol açabilmektedir.

Kaynakça

  • Ahmadi, M. R. (2018). The use of technology in English language learning: A literature review. International Journal of Research in English Education, 3(2), 115-125. https://doi.org/10.29252/ijree.3.2.115
  • Arnold, T., Ballier, N., Gaillat, T., & Lissón, P. (2018). Predicting CEFRL levels in learner English on the basis of metrics and full texts. arXiv. https://arxiv.org/abs/1806.11099 arXiv+2taylorarnold.org+2
  • Bourgeois, D. T., Smith, J. L., Wang, S., & Mortati, J. (2019). Information systems for business and beyond. Open Textbooks. https://open.umn.edu/opentextbooks/textbooks/information-systems-for-business-and-beyond
  • Cathoven. (2023). CEFR Checker – Online CEFR text analysis tool. Retrieved November 12, 2025, from https://cefrchecker.cathoven.com
  • CEFRlevels. (n.d.). English Text Analyzer – CEFR level estimation. Retrieved November 12, 2025, from https://cefrlevels.com
  • Chen, Y., & Suzuki, W. (2025). CVLA 3.0: A CEFR-J-based text analysis system for educational applications. Tokyo: National Institute for Japanese Language Education.
  • Claridge, G. (2012). Graded readers: How the publishers make the grade. Reading in a Foreign Language, 24(1), 106–119.
  • Council of Europe. (2018, May 23). Common European Framework of Reference for Languages. https://www.coe.int/en/web/common-european-framework-reference-languages
  • Cervone, H. F. (2007). The system development life cycle and digital library development. OCLC Systems & Services: International Digital Library Perspectives, 23(4), 348–352. https://doi.org/10.1108/10650750710831484
  • English Text Level Analyser. (n.d.). Online CEFR and lexical frequency analysis tool. Retrieved November 12, 2025, from https://englishtextlevel.com
  • Habib, B., & Ashfaq, R. A. R. (2013). Relationship between factors of quality models and the system development life cycle. International Journal of Computer Applications. https://doi.org/10.5120/14051-2216
  • Hill, D. R. (2008). Graded readers in English. ELT Journal, 62(2), 184-204. https://doi.org/10.1093/elt/ccn006
  • Kamil, M. L., & Hiebert, E. H. (2005). Teaching and learning vocabulary: Perspectives and persistent issues. Routledge.
  • Kurdi, M. Z. (2020, January 7). Text complexity classification based on linguistic information: Application to intelligent tutoring of ESL. arXiv. https://arxiv.org/abs/2001.01863 arXiv+1
  • Nation, I. S. P. (2001). Learning vocabulary in another language. Cambridge, UK: Cambridge University Press.
  • Papyrus Author. (n.d.). Papyrus CEFR Authoring Tool – Readability and language level analysis. Retrieved November 12, 2025, from https://papyrusauthor.com
  • Pilán, I., Alfter, D., & Volodina, E. (2016, December). Coursebook texts as a helping hand for classifying linguistic complexity in language learners’ writings. In D. Brunato, F. Dell’Orletta, G. Venturi, T. François, & P. Blache (Eds.), Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC) (pp. 120-126). Osaka, Japan: The COLING 2016 Organizing Committee.
  • Prtljaga, J., Palinkašević, R., & Brkić, J. (2015). Choosing the adequate level of graded readers: Preliminary study. Research in Pedagogy, 5(2), 1–16. https://doi.org/10.17810/2015.11
  • Raine, P. (2018). Developing web-based English learning applications: Principles and practice. CALL-EJ, 19, 126–138.
  • Rashid, Z., Kadiman, S., Zulkifli, Z., Selamat, J., Hisyam, M., & Mohd Hashim, M. H. (2016). Review of web-based learning in TVET: History, advantages and disadvantages. International Journal of Vocational Education and Training Research, 2(2), 7–17. https://doi.org/10.11648/j.ijvetr.20160202.11
  • Rodrigo, V. (2016). Graded readers: Validating reading levels across publishers. Hispania, 99(1), 66–86. https://doi.org/10.1353/hpn.2016.0027
  • Ruparelia, N. B. (2010). Software development lifecycle models. ACM SIGSOFT Software Engineering Notes, 35(3), 8–13. https://doi.org/10.1145/1764810.1764814
  • Weitzel, J. R., & Kerschberg, L. (1989). Developing knowledge-based systems: Reorganizing the system development life cycle. Communications of the ACM, 32(9), 110–121. https://doi.org/10.1145/63334.63340
  • Sarıca, G. N., & Çavuş, N. (2008). Web-based English language learning. In Proceedings of the 8th International Educational Technology Conference (pp. 972–976). Anadolu University.
  • Siripol, P., Rhee, S., Thirakunkovit, S., & Liang-Itsara, A. (2025). Evaluating the consistency of automated CEFR analyzers: A study of English language text classification. International Journal of Evaluation and Research in Education, 14(4), 3283–3294. https://doi.org/10.11591/ijere.v14i4.33528
  • Sung, Y.-T., Lin, W.-C., Dyson, S. B., Chang, K.-E., & Chen, Y.-C. (2015). Leveling L2 texts through readability: Combining multilevel linguistic features with the CEFR. The Modern Language Journal, 99(2), 371–391. https://doi.org/10.1111/modl.12213
  • Text Inspector. (2018). Online text analysis tool for CEFR level estimation. Retrieved November 12, 2025, from https://textinspector.com
  • Thienthong, A., & Lian, A. (2014). The use of internet resources and applications for language instruction. Voices in Asia, 1, 107–116.
  • Tick, A. (2006). A web-based e-learning application of self study multimedia programme in military English. In A. Szakál (Ed.), Proceedings of the 3rd Romanian-Hungarian Joint Symposium on Applied Computational Intelligence (pp. 621–633). IEEE Hungary Section.
  • Twee. (2025). CEFR Level Checker – Text difficulty estimation for teachers. Retrieved November 12, 2025, from https://twee.com/tools/cefr-checker
  • Valacich, J. S., & George, J. F. (2017). Modern systems analysis and design (8th ed.). Boston, MA: Pearson.
  • Valacich, J. S., George, J. F., & Hoffer, J. (2015). Essentials of systems analysis and design (Global ed.). Harlow, England: Pearson Education UK.
  • Velleman, E., & van der Geest, T. (2014). Online test tool to determine the CEFR reading comprehension level of text. Procedia Computer Science, 27, 350–358. https://doi.org/10.1016/j.procs.2014.02.039
  • Webb, S., & Chang, A. C.-S. (2015). Second language vocabulary learning through extensive reading with audio support: How do frequency and distribution of occurrence affect learning? Language Teaching Research, 19(6), 667–686. https://doi.org/10.1177/1362168814559800
  • Yorulmaz, M., Yavuzcan, H. G., & Togay, A. (2012). A web-based management system and its application for student design projects. Journal of Educational and Instructional Studies in the World, 2(2), Article 26.
  • Yusof, N. A., & Saadon, N. (2012). The effects of web-based language learning on university students' grammar proficiency. Procedia-Social and Behavioral Sciences, 67, 402–408. https://doi.org/10.1016/j.sbspro.2012.11.344
  • Zhang, X., & Lu, X. (2025). Aligning linguistic complexity with the difficulty of English texts for L2 learners based on CEFR levels. Studies in Second Language Acquisition. https://doi.org/10.1017/S0272263125101125 pure.psu.edu+1

Web-Programming Techniques in Determining the CEFR Levels of English Books

Yıl 2026, Cilt: 10 Sayı: 23 , 34 - 49 , 30.04.2026
https://doi.org/10.57135/jier.1731184
https://izlik.org/JA48UC73ZR

Öz

We can call the most meaningful unit of a language a word. It is possible to communicate with a person to a certain extent by using only words, without using grammatical structures. Therefore, it is important to learn and teach the most frequently used vocabulary of a language. The most important skill used in learning words is reading. Simplified books are the primary sources available for acquiring reading skills. Our aim in our study is to prepare a web-based application to determine whether the books or reading materials recommended by instructors in the process of teaching words in the CEFR word list are appropriate for the students' levels and to use this application, which is developed with the System Development Life Cycle (SDLC), as a decision support system. As it is known, books that are not appropriate for the students' levels negatively affect the learning process and even end the student's reading process in some cases, which spoils the purpose of the learning process.

Kaynakça

  • Ahmadi, M. R. (2018). The use of technology in English language learning: A literature review. International Journal of Research in English Education, 3(2), 115-125. https://doi.org/10.29252/ijree.3.2.115
  • Arnold, T., Ballier, N., Gaillat, T., & Lissón, P. (2018). Predicting CEFRL levels in learner English on the basis of metrics and full texts. arXiv. https://arxiv.org/abs/1806.11099 arXiv+2taylorarnold.org+2
  • Bourgeois, D. T., Smith, J. L., Wang, S., & Mortati, J. (2019). Information systems for business and beyond. Open Textbooks. https://open.umn.edu/opentextbooks/textbooks/information-systems-for-business-and-beyond
  • Cathoven. (2023). CEFR Checker – Online CEFR text analysis tool. Retrieved November 12, 2025, from https://cefrchecker.cathoven.com
  • CEFRlevels. (n.d.). English Text Analyzer – CEFR level estimation. Retrieved November 12, 2025, from https://cefrlevels.com
  • Chen, Y., & Suzuki, W. (2025). CVLA 3.0: A CEFR-J-based text analysis system for educational applications. Tokyo: National Institute for Japanese Language Education.
  • Claridge, G. (2012). Graded readers: How the publishers make the grade. Reading in a Foreign Language, 24(1), 106–119.
  • Council of Europe. (2018, May 23). Common European Framework of Reference for Languages. https://www.coe.int/en/web/common-european-framework-reference-languages
  • Cervone, H. F. (2007). The system development life cycle and digital library development. OCLC Systems & Services: International Digital Library Perspectives, 23(4), 348–352. https://doi.org/10.1108/10650750710831484
  • English Text Level Analyser. (n.d.). Online CEFR and lexical frequency analysis tool. Retrieved November 12, 2025, from https://englishtextlevel.com
  • Habib, B., & Ashfaq, R. A. R. (2013). Relationship between factors of quality models and the system development life cycle. International Journal of Computer Applications. https://doi.org/10.5120/14051-2216
  • Hill, D. R. (2008). Graded readers in English. ELT Journal, 62(2), 184-204. https://doi.org/10.1093/elt/ccn006
  • Kamil, M. L., & Hiebert, E. H. (2005). Teaching and learning vocabulary: Perspectives and persistent issues. Routledge.
  • Kurdi, M. Z. (2020, January 7). Text complexity classification based on linguistic information: Application to intelligent tutoring of ESL. arXiv. https://arxiv.org/abs/2001.01863 arXiv+1
  • Nation, I. S. P. (2001). Learning vocabulary in another language. Cambridge, UK: Cambridge University Press.
  • Papyrus Author. (n.d.). Papyrus CEFR Authoring Tool – Readability and language level analysis. Retrieved November 12, 2025, from https://papyrusauthor.com
  • Pilán, I., Alfter, D., & Volodina, E. (2016, December). Coursebook texts as a helping hand for classifying linguistic complexity in language learners’ writings. In D. Brunato, F. Dell’Orletta, G. Venturi, T. François, & P. Blache (Eds.), Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC) (pp. 120-126). Osaka, Japan: The COLING 2016 Organizing Committee.
  • Prtljaga, J., Palinkašević, R., & Brkić, J. (2015). Choosing the adequate level of graded readers: Preliminary study. Research in Pedagogy, 5(2), 1–16. https://doi.org/10.17810/2015.11
  • Raine, P. (2018). Developing web-based English learning applications: Principles and practice. CALL-EJ, 19, 126–138.
  • Rashid, Z., Kadiman, S., Zulkifli, Z., Selamat, J., Hisyam, M., & Mohd Hashim, M. H. (2016). Review of web-based learning in TVET: History, advantages and disadvantages. International Journal of Vocational Education and Training Research, 2(2), 7–17. https://doi.org/10.11648/j.ijvetr.20160202.11
  • Rodrigo, V. (2016). Graded readers: Validating reading levels across publishers. Hispania, 99(1), 66–86. https://doi.org/10.1353/hpn.2016.0027
  • Ruparelia, N. B. (2010). Software development lifecycle models. ACM SIGSOFT Software Engineering Notes, 35(3), 8–13. https://doi.org/10.1145/1764810.1764814
  • Weitzel, J. R., & Kerschberg, L. (1989). Developing knowledge-based systems: Reorganizing the system development life cycle. Communications of the ACM, 32(9), 110–121. https://doi.org/10.1145/63334.63340
  • Sarıca, G. N., & Çavuş, N. (2008). Web-based English language learning. In Proceedings of the 8th International Educational Technology Conference (pp. 972–976). Anadolu University.
  • Siripol, P., Rhee, S., Thirakunkovit, S., & Liang-Itsara, A. (2025). Evaluating the consistency of automated CEFR analyzers: A study of English language text classification. International Journal of Evaluation and Research in Education, 14(4), 3283–3294. https://doi.org/10.11591/ijere.v14i4.33528
  • Sung, Y.-T., Lin, W.-C., Dyson, S. B., Chang, K.-E., & Chen, Y.-C. (2015). Leveling L2 texts through readability: Combining multilevel linguistic features with the CEFR. The Modern Language Journal, 99(2), 371–391. https://doi.org/10.1111/modl.12213
  • Text Inspector. (2018). Online text analysis tool for CEFR level estimation. Retrieved November 12, 2025, from https://textinspector.com
  • Thienthong, A., & Lian, A. (2014). The use of internet resources and applications for language instruction. Voices in Asia, 1, 107–116.
  • Tick, A. (2006). A web-based e-learning application of self study multimedia programme in military English. In A. Szakál (Ed.), Proceedings of the 3rd Romanian-Hungarian Joint Symposium on Applied Computational Intelligence (pp. 621–633). IEEE Hungary Section.
  • Twee. (2025). CEFR Level Checker – Text difficulty estimation for teachers. Retrieved November 12, 2025, from https://twee.com/tools/cefr-checker
  • Valacich, J. S., & George, J. F. (2017). Modern systems analysis and design (8th ed.). Boston, MA: Pearson.
  • Valacich, J. S., George, J. F., & Hoffer, J. (2015). Essentials of systems analysis and design (Global ed.). Harlow, England: Pearson Education UK.
  • Velleman, E., & van der Geest, T. (2014). Online test tool to determine the CEFR reading comprehension level of text. Procedia Computer Science, 27, 350–358. https://doi.org/10.1016/j.procs.2014.02.039
  • Webb, S., & Chang, A. C.-S. (2015). Second language vocabulary learning through extensive reading with audio support: How do frequency and distribution of occurrence affect learning? Language Teaching Research, 19(6), 667–686. https://doi.org/10.1177/1362168814559800
  • Yorulmaz, M., Yavuzcan, H. G., & Togay, A. (2012). A web-based management system and its application for student design projects. Journal of Educational and Instructional Studies in the World, 2(2), Article 26.
  • Yusof, N. A., & Saadon, N. (2012). The effects of web-based language learning on university students' grammar proficiency. Procedia-Social and Behavioral Sciences, 67, 402–408. https://doi.org/10.1016/j.sbspro.2012.11.344
  • Zhang, X., & Lu, X. (2025). Aligning linguistic complexity with the difficulty of English texts for L2 learners based on CEFR levels. Studies in Second Language Acquisition. https://doi.org/10.1017/S0272263125101125 pure.psu.edu+1
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Eğitim Teknolojisi ve Bilgi İşlem
Bölüm Araştırma Makalesi
Yazarlar

Hüseyin Köksal Çobanoğlu 0000-0002-3861-1128

Emre Karagöz 0000-0002-4887-8168

Gönderilme Tarihi 30 Haziran 2025
Kabul Tarihi 11 Ocak 2026
Yayımlanma Tarihi 30 Nisan 2026
DOI https://doi.org/10.57135/jier.1731184
IZ https://izlik.org/JA48UC73ZR
Yayımlandığı Sayı Yıl 2026 Cilt: 10 Sayı: 23

Kaynak Göster

APA Çobanoğlu, H. K., & Karagöz, E. (2026). Web-Programming Techniques in Determining the CEFR Levels of English Books. Disiplinlerarası Eğitim Araştırmaları Dergisi, 10(23), 34-49. https://doi.org/10.57135/jier.1731184