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Journals / Uluslararası Özel Amaçlar için İngilizce Dergisi / 2026 / Cilt: 4 Sayı: 1

PiBAL-ML: A Machine Learning-Based Personalized Listening Platform for EFL Learners

PiBAL-ML: Yabancı Dil Olarak İngilizce Öğrenenler İçin Makine Öğrenmesi Tabanlı Kişiselleştirilmiş Dinleme Platformu

Kübra Okumuş Dağdeler(SİVAS CUMHURİYET ÜNİVERSİTESİ)
Yasin Görmez(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0001-8276-2030
Samet Çağrı Kızkapan(SİVAS CUMHURİYET ÜNİVERSİTESİ)
Pages
44–62
Publication Date
2026-06-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The quantitative results revealed that there was no significant difference in overall scores between the control group and the experimental group.

📌 3 Key Takeaways

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Objective & Hypothesis:Individuals exhibit variations in their needs, interests, and affective behaviours during the language learning process.
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Key Findings & Data:The PiBAL-ML system, at its core, featured a diverse array of listening materials carefully curated to align with the learners' specific fields of interest.
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Conclusion & Impact:The quantitative results revealed that there was no significant difference in overall scores between the control group and the experimental group.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kişiselleştirilmiş Öğrenme · Makine Öğrenimi · dinleme

Abstract

Individuals exhibit variations in their needs, interests, and affective behaviours during the language learning process. Recognizing and accommodating these differences is crucial for optimizing language acquisition. Advancements in technology have provided numerous tools and opportunities to tailor language learning experiences to individual preferences, thereby facilitating a more effective learning journey. Regarding these rationales, this study aimed to propose a personalized language learning platform which was called Personal Interest Based Listening- Machine Learning (PiBAL-ML) and then determine its effect on English as Foreign Language learners’ listening skills. Thus, the study unfolded in two distinct phases: the design phase and the testing phase. The PiBAL-ML system, at its core, featured a diverse array of listening materials carefully curated to align with the learners' specific fields of interest. Furthermore, it included a variety of question types, and the number of questions was dynamically adjusted based on participants' performance and progress. The system was tested through explanatory sequential research design which included both quantitative and qualitative data gathered from university students. The quantitative results revealed that there was no significant difference in overall scores between the control group and the experimental group. However, experimental group outperformed control group in multiple-choice test questions. Lastly, the study highlighted that the learners were satisfied with the system as it presented different activities and texts related to their interests. Based on these findings, the study concluded that machine learning based personalization could enhance foreign language learning.

Özet

Bireyler, dil öğrenme süreci boyunca ihtiyaçları, ilgi alanları ve duygusal davranışları açısından farklılıklar sergilerler. Bu farklılıkları fark etmek ve bunlara uyum sağlamak, dil edinimini en verimli hale getirmek açısından hayati önem taşır. Teknolojideki gelişmeler, dil öğrenme deneyimlerini bireysel tercihlere göre uyarlama imkânı sunan çok sayıda araç ve fırsat sağlamış, böylece daha etkili bir öğrenme süreci oluşturulmuştur. Bu gerekçeler doğrultusunda, bu çalışma, Kişisel İlgi Temelli Dinleme-Makine Öğrenimi (PiBAL-ML) adı verilen kişiselleştirilmiş bir dil öğrenme platformu önermeyi ve ardından bunun Yabancı Dil Olarak İngilizce öğrenenlerin dinleme becerileri üzerindeki etkisini belirlemeyi amaçlamıştır. Bu nedenle, çalışma iki ayrı aşamada gerçekleştirilmiştir: tasarım aşaması ve test aşaması. PiBAL-ML sistemi, öğrenenlerin belirli ilgi alanlarına uyacak şekilde özenle seçilmiş çok çeşitli dinleme materyallerini içermektedir. Ayrıca, içerdiği çeşitli soru türleri ve soru sayısı, katılımcıların performansına ve ilerlemesine göre dinamik olarak ayarlandı. Sistem, üniversite öğrencilerinden toplanan hem nicel hem de nitel verileri içeren açıklayıcı sıralı araştırma tasarımıyla test edildi. Nicel sonuçlar, kontrol grubu ile deney grubu arasında genel puanlarda anlamlı bir fark olmadığını ortaya koymuştur. Ancak, deney grubu çoktan seçmeli test sorularında kontrol grubundan daha iyi performans göstermektedir. Son olarak, çalışma, öğrencilerin ilgi alanlarıyla ilgili farklı etkinlikler ve metinler sunan sistemden memnun olduklarını vurgulamaktadır. Bu bulgulara dayanarak, çalışma, makine öğrenimi tabanlı kişiselleştirmenin yabancı dil öğrenimini geliştirebileceği sonucuna varılmştır.

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Cite This Article

Dağdeler, K. O., Görmez, Y., Kızkapan, S. Ç. (2026). PiBAL-ML: A Machine Learning-Based Personalized Listening Platform for EFL Learners. Uluslararası Özel Amaçlar için İngilizce Dergisi, Cilt: 4 Sayı: 1, 44–62. https://doi.org/10.70870/joinesp.1922933

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References (44)

  1. Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher Education Edition. The New Media Consortium.
  2. Basham, J. D., Hall, T. E., Carter, R. A., & Stahl, W. M. (2016). An operationalized understanding of personalized learning. Journal of Special Education Technology, 31(3), 126-136. https://doi.org/10.1177/0162643416660835.
  3. Bernal Parraga, A. P., Coronel Ramírez, E. A., Aldas Macias, K. J., Carvajal Madrid, C. A., Valarezo Espinoza, B. D. C., Vera Alcivar, J. G., & Chávez Cedeño, J. U. (2025). The Impact of Artificial Intelligence on Personalized Learning in English Language Education. Ciencia Latina Revista Científica Multidisciplinar, 9 (1), 5500-5518. https://doi.org/10.37811/cl_rcm.v9i1.16234
  4. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa.
  5. Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data and Society, 22(1), 1-29.
  6. Chen, C.-M., & Chung, C.-J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624-645. https://doi.org/10.1016/j.compedu.2007.06.011
  7. Chen, C.-M., & Hsu, S.-H. (2008). Personalized intelligent mobile learning system for supporting effective English learning. Educational Technology & Society, 11(3), 153-180.
  8. Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty years of personalized language learning: Topic modelling and knowledge mapping. Educational Technology & Society, 24(1), 205-222.
  9. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Sage Publications.
  10. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
  11. Fang, L., Tuan, L. A., Hui, S. C., & Wu, L. (2017). Personalized question recommendation for English grammar learning. Expert Systems, 35(2). https://doi.org/10.1111/exsy.12244
  12. Farrokhi, F., & Mahmoudi-Hamidabad, A. (2012). Rethinking convenience sampling: Defining quality criteria. Theory and Practice in Language Studies, 2(4). https://doi.org/10.4304/tpls.2.4.784-792
  13. Görmez, Y., Okumuş Dağdeler, K., & Kavuklu, M. (2022). Kişiselleştirilmiş yabancı dil öğrenimi için makine öğrenmesi yöntemleriyle ilgi alanı tahmini. Yükseköğretim ve Bilim Dergisi, 12(1), 111-121. https://doi.org/10.5961/higheredusci.982740
  14. Griol, D., Baena, I., Molina, J. M., & de Miguel, A. S. (2014). A multimodal conversational agent for personalized language learning. In C. Ramos, P. Novais, C. E. Nihan, & J. M. Corchado Rodríguez (Eds.), Ambient Intelligence—Software and Applications (pp. 13-21). Springer International Publishing. https://doi.org/10.1007/978-3-319-00566-9_2
  15. Hasan, M. M., & Khaing, H. O. (2008). Learner corpus and its application to automatic level checking using machine learning algorithms. In Proceedings of the 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (Vol. 1, pp. 25–28). https://doi.org/10.1109/ECTICON.2008.4600364
  16. Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327-336. https://doi.org/10.1016/j.compedu.2012.12.004
  17. Jiang, H. (2025). Deep learning based personalized English listening learning path recommendation algorithm. Systems and Soft Computing, 7. https://doi.org/10.1016/j.sasc.2025.200210
  18. Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21, 345–383. https://doi.org/10.1023/A:1012771025575
  19. Jung, J., & Graf, S. (2008). An approach for personalized web-based vocabulary learning through word association games. In International Symposium on Applications and the Internet (pp. 325-328). IEEE. https://doi.org/10.1109/SAINT.2008.95
  20. Kepçeoğlu, M. (2019). Eduolog.com - Özgün ve Bağımsız Çevrimiçi Eğitim Rehberi. Accessed on https://www.eduolog.com/
  21. Kerr, P. (2016). Adaptive learning. ELT Journal, 70(1), 88-93. https://doi.org/10.1093/elt/ccv055
  22. Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20(3), 271–289. https://doi.org/10.1017/S0958344008000335
  23. Kwon, D., Lim, H., Lee, W., Kim, H., Jung, S., Suh, T., & Nam, K. (2010). A personalized English vocabulary learning system based on cognitive abilities related to foreign language proficiency. Transactions on Internet and Information Systems, 4(4), 595–617.
  24. Looi, C.-K., Wong, L.-H., So, H.-Y., Seow, P., Toh, Y., Chen, W., Zhang, B., Norris, C., & Soloway, E. (2009). Anatomy of a mobilized lesson: Learning my way. Computers & Education, 53, 1120-1132. https://doi.org/10.1016/j.compedu.2009.05.019
  25. Murray, H., Gillese, E., Lennon, M., Mercer, P., & Robinson, M. (1996). Ethical principles in university teaching. Accessed on https://files.eric.ed.gov/fulltext/ED407899.pdf
  26. Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. RAND Corporation. Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1). https://doi.org/10.1186/s40561-019-0089-y
  27. Petersen, S. A., Markiewicz, J.-K., & Bjørnebekk, S. S. (2009). Personalized and contextualized language learning: Choose when, where and what. Research and Practice in Technology Enhanced Learning, 4(1), 33-60. https://doi.org/10.1142/S1793206809000635
  28. Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153. https://doi.org/10.2478/jolace-2019-0025
  29. Qi, S., Liu, L., Kumar, B. S., & Prathik, A. (2022). An English teaching quality evaluation model based on Gaussian process machine learning. Expert Systems, 39(6). https://doi.org/10.1111/exsy.12861
  30. Ross, M., Graves, C. A., Campbell, J. W., & Kim, J. H. (2013). Using support vector machines to classify student attentiveness for the development of personalized learning systems. 12th International Conference on Machine Learning and Applications. https://doi.org/10.1109/ICMLA.2013.66
  31. Sharma, S., Mittal, P., Kumar, M., & Bhardwaj, V. (2025). The role of large language models in personalized learning: a systematic review of educational impact. Discover Sustainability, 6(243). https://doi.org/10.1007/s43621-025-01094-z
  32. Traxler, J. (2009). Current state of mobile learning: Transforming the delivery of education and training. Accessed on http://www.aupress.ca/index.php/books/120155
  33. Troussas, C., Chrysafiadi, K., & Virvou, M. (2018). Machine learning and fuzzy logic techniques for personalized tutoring of foreign languages. In C. Penstein Rosé et al. (Eds.), Artificial Intelligence in Education (pp. 358-362). Springer International Publishing. https://doi.org/10.1007/978-3-319-93846-2_67
  34. Troussas, C., Chrysafiadi, K., & Virvou, M. (2019). An intelligent adaptive fuzzy-based inference system for computer-assisted language learning. Expert Systems with Applications, 127, 85–96. https://doi.org/10.1016/j.eswa.2019.03.003
  35. Troussas, C., Virvou, M., & Espinosa, K. J. (2015). Using visualization algorithms for discovering patterns in groups of users for tutoring multiple languages through social networking. Journal of Networks, 10(12), 668-674. https://doi.org/10.4304/jnw.10.12.668-674
  36. U.S. Department of Education, Office of Educational Technology. (2017). Reimagining the role of technology in education: 2017 National Education Plan update. U.S. Department of Education. Washington, DC Virvou, M., Troussas, C., & Alepis, E. (2012). Machine learning for user modelling in a multilingual learning system. In International Conference on Information Society (pp. 292-297).
  37. Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945.
  38. Wang, Z. (2023). Online language education recommendation based on personalized learning and edge computing. Internet Technology Letters, 408. https://doi.org/10.1002/itl2.408
  39. Wu, J., Chen, X.-Y., Zhang, H., Xiong, D.-L., Lei, H., & Deng, S.-H. (2019). Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120
  40. Wu, T.-T., Huang, Y.-M., Chao, H.-C., & Park, J. H. (2014). Personalized English reading sequencing based on learning portfolio analysis. Information Sciences, 257, 248-263. https://doi.org/10.1016/j.ins.2011.07.021
  41. Xu, Y., Zhang, M., & Gao, Z. (2020). The construction of distance education personalized learning platform based on educational data mining. In J. Abawajy, K.K. Choo, R. Islam, Z. Xu, & M. Atiquzzaman (Eds.), ATCI 2019. Advances in Intelligent Systems and Computing, 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_134
  42. Yang, C. C. Y., & Ogata, H. (2023). Personalized review learning approach for improving behavioural engagement and academic achievement in language learning through e-books. Education and Information Technologies, 28, 1491–1508. https://doi.org/10.1007/s10639-022-11245-8
  43. Yeung, C. Y., & Lee, J. (2018). Personalized text retrieval for learners of Chinese as a foreign language. Proceedings of the 27th International Conference on Computational Linguistics, 3448-3455. Accessed on https://aclanthology.org/C18-1292
  44. Zhang, L. (2021). A new machine learning framework for effective evaluation of English education. International Journal of Emerging Technologies in Learning, 16(12), 142–154. https://doi.org/10.3991/ijet.v16i12.23323 Zou, Y. (2011, December). Personalized automatic recommendations for the web-based autonomous language learning system based on data mining technology. In IEEE International Symposium on IT in Medicine and Education (Vol. 2, pp. 326-329). IEEE.