Kişiselleştirilmiş Yabancı Dil Öğrenimi İçin Makine Öğrenmesi Yöntemleriyle İlgi Alanı Tahmini

Yıl 2022, Cilt: 12 Sayı: 1, 111 - 121, 30.04.2022

Kübra Okumuş Dağdeler , Yasin Görmez , Merve Kavuklu

Öz

Küreselleşen dünyada yabancı dil bilmenin önemi giderek artmaktadır. Dil öğretim zorluklarını azaltmak için önemli yöntemlerden biri de, teknoloji dünyasındaki gelişmeler ile birlikte daha kolay yönetebilir hale gelen kişiselleştirilmiş öğrenim yaklaşımlardır. Kişiselleştirilmiş öğrenim sayesinde aynı sınıf ortamında bile, her bireyin istek ve ihtiyaçlarına göre yöntem ve materyal sunulabilmektedir. Dil öğretiminde, içeriklerin kişilerin ilgi alanlarına uygun olarak sunulmasının öğrenimin verimini artıracağı düşünülmektedir. Bu kapsamda çalışmada, kişiselleştirilmiş İngilizce öğretiminde alt yapı olarak kullanılmak üzere makine öğrenmesi yöntemleri ile bireylerin ilgi alanı tahmini yapılmıştır. Çalışmada öncelikli olarak bir anket tasarlanarak farklı sektörlerden 164 kişiye uygulanmıştır. Tasarlanan ankette kişilerin istedikleri kadar seçim yapacakları seçeneklerden oluşan 11 soru ve ilgi alanını seçebilecekleri bölüm bulunmaktadır. Birey en az biri zorunlu olmak üzere teknoloji, sağlık, iş yaşamı, farklı kültürler, spor ve güzel sanatlar ilgi alanlarından dilediği kadarını seçebilmektedir. Toplanan bu veriler matematiksel hale dönüştürülerek k-en yakın komşu, rastgele orman ve yapay sinir ağı yöntemleri ile analizler yapılmıştır. Kullanılan yöntemlerim parametre optimizasyonu için geleneksel ızgara arama yönteminden daha kısa sürede daha iyi sonuçlar üreten Bayesian optimizasyon yönteminden faydalanılmıştır. Bir kullanıcı birden fazla ilgi alanı seçebildiği için tüm makine öğrenmesi modelleri çoklu etiket tahmini yaklaşımı ile oluşturulmuştur. Bu bağlamda her bir kişi için ilgi duyuyor ve duymuyor olacak şekilde 6 ilgi alanı için ayrı ayrı tahmin yapılmış ve başarı oranı da bu durum göze alınarak hesaplanmıştır. Analiz sonuçları incelendiğinde en iyi başarı oranı %78.12 ile rastgele orman algoritması ile elde edilmiştir. Bu sonucun tasarlanacak sistem için yeterli olduğu, veri sayısının artırılması ile birlikte de daha iyi sonuçlar elde edileceği öngörülmektedir.

Anahtar Kelimeler

Kişiselleştirilmiş Öğrenim, Makine Öğrenmesi, Optimizasyon, Yabancı Dil Eğitimi, İlgi Alanı Tahmini

Destekleyen Kurum

Detay Teknoloji A. Ş. Araştırma Merkezi

Proje Numarası

4005

Teşekkür

Bu çalışma, Detay Teknoloji A. Ş. Ar-Ge Merkezi bünyesinde yürütülen çalışmaların bir sonucudur. Desteklerinden ötürü teşekkür ederiz

Prediction of Interest Through Machine Learning Methods for Personalized Foreign Language Learning

Öz

In the globalizing world, the importance of knowing a foreign language is increasing. One of the important methods to reduce language teaching difficulties is personalized learning approaches, which have become easier to manage with the developments in the world of technology. Thanks to personalized learning, educational methods and materials can be presented based on the interests and needs of each individual, even in the same classroom environment. It is known that presenting the learning contents in accordance with the interests of the learners will increase the efficiency of learning. Thus, in this study, the individuals’ areas of interest were estimated through machine learning methods in order to use it as an infrastructure for personalized foreign language learning. A questionnaire was designed and applied to 164 people from different sectors. In the questionnaire, there were 11 questions consisting of options that people could choose as much as they want, and a section where they could choose their area of interest. Individuals could choose areas of interest as much as they want from technology, health, business life, different cultures, sports and fine arts, at least one of which is compulsory. The collected data were transformed into mathematical form and analyzed with k-nearest neighbor, random forest and artificial neural network methods. The Bayesian optimization method, which produces better results in a shorter time than the traditional grid search method, was used for parameter optimization of methods used. Since a user could select more than one area of interest, all machine learning models were built with a multi-label prediction approach. In this context, separate estimations were made for 6 areas of interest for each person, whether they were interested or not, and the success rate was calculated considering this situation. When the analysis results were examined, the best success rate was obtained with the random forest algorithm with 78.12%. It was foreseen that this result would be sufficient for the system to be designed and better results would be obtained with the increasing number of data.

Anahtar Kelimeler

Personalized Learning, Machine Learning, Optimization, Foreign Language Education, Interest Prediction

Proje Numarası

4005

Kaynakça

• Ankit, & Saleena, N. (2018). An Ensemble Classification System for Twitter Sentiment Analysis. Procedia Computer Science, 132, 937-946. https://doi.org/10.1016/j.procs.2018.05.109
• 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
• Bulger, M. (t.y.). Personalized Learning: The Conversations We’re Not Having. 29.
• Catal, C., & Nangir, M. (2017). A sentiment classification model based on multiple classifiers. Applied Soft Computing, 50, 135-141. https://doi.org/10.1016/j.asoc.2016.11.022
• 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
• Chen, C.-M., & Hsu, S.-H. (2008). Personalized Intelligent Mobile Learning System for Supporting Effective English Learning. Educational Technology & Society, 11(3), 153-180.
• Chen, C.-M., Hsu, S.-H., Li, Y.-L., & Peng, C.-J. (2006). Personalized Intelligent M-learning System for Supporting Effective English Learning. 2006 IEEE International Conference on Systems, Man and Cybernetics, 6, 4898-4903. https://doi.org/10.1109/ICSMC.2006.385081
• Chen, C.-M., & Li, Y.-L. (2010). Personalised context-aware ubiquitous learning system for supporting effective English vocabulary learning. Interactive Learning Environments, 18(4), 341-364. https://doi.org/10.1080/10494820802602329
• Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty Years of Personalized Language Learning: Topic Modeling and Knowledge Mapping. Educational Technology & Society, 24(1), 205-222.
• EF EPI 2020 – Turkey. (t.y.). Geliş tarihi 13 Temmuz 2021, gönderen https://www.ef.com/ca/epi/regions/europe/turkey/
• Gardezi, S. J. S., Faye, I., Bornot, J. M. S., Kamel, N., & Hussain, M. (2017). Mammogram classification using dynamic time warping. Multimedia Tools and Applications, 1-22. https://doi.org/10.1007/s11042-016-4328-8
• Griol, D., Baena, I., Molina, J. M., & de Miguel, A. S. (2014). A Multimodal Conversational Agent for Personalized Language Learning. Içinde C. Ramos, P. Novais, C. E. Nihan, & J. M. Corchado Rodríguez (Ed.), Ambient Intelligence—Software and Applications (ss. 13-21). Springer International Publishing. https://doi.org/10.1007/978-3-319-07596-9_2
• Hill, J. R., & Jolly, N. A. (2012). Salary Distribution and Collective Bargaining Agreements: A Case Study of the NBA. Industrial Relations: A Journal of Economy and Society, 51(2), 342-363. https://doi.org/10.1111/j.1468-232X.2012.00680.x
• Hsieh, T.-C., Wang, T.-I., Su, C.-Y., & Lee, M.-C. (2012). A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning. Journal of Educational Technology & Society, 15(1), 273-288.
• 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
• Janssens, A. C. J. W., Duijn, V., & M, C. (2008). Genome-based prediction of common diseases: Advances and prospects. Human Molecular Genetics, 17(R2), R166-R173. https://doi.org/10.1093/hmg/ddn250
• Keras: The Python deep learning API. (t.y.). Geliş tarihi 10 Temmuz 2021, gönderen https://keras.io/
• Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. 2016 International Conference on Platform Technology and Service (PlatCon), 1-5. https://doi.org/10.1109/PlatCon.2016.7456805
• Koesdwiady, A., Soua, R., & Karray, F. (2016). Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, 65(12), 9508-9517. https://doi.org/10.1109/TVT.2016.2585575
• Kruppa, J., Ziegler, A., & König, I. R. (2012). Risk estimation and risk prediction using machine-learning methods. Human Genetics, 131(10), 1639-1654. https://doi.org/10.1007/s00439-012-1194-y
• Kumar, K., Parida, M., & Katiyar, V. K. (2013). Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network. Procedia - Social and Behavioral Sciences, 104, 755-764. https://doi.org/10.1016/j.sbspro.2013.11.170
• li, W., & Liu, Z. (2011). A method of SVM with Normalization in Intrusion Detection. Procedia Environmental Sciences, 11, 256-262. https://doi.org/10.1016/j.proenv.2011.12.040 Mesleki Yönelim Envanteri (Prof. Dr. Muharrem KEPÇEOĞLU). (2019, Aralık 4). Eduolog.com. https://www.eduolog.com/mesleki-yonelim-envanteri-prof-dr-muharrem-kepceoglu/
• Oktay, A. (2015). Foreign Language Teaching: A Problem in Turkish Education. Procedia - Social and Behavioral Sciences, 174, 584-593. https://doi.org/10.1016/j.sbspro.2015.01.587
• 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), 9. https://doi.org/10.1186/s40561-019-0089-y
• 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, 04(01), 33-60. https://doi.org/10.1142/S1793206809000635
• Prabusankarlal, K. M., Thirumoorthy, P., & Manavalan, R. (2015). Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound. Human-Centric Computing and Information Sciences, 5(1), 12. https://doi.org/10.1186/s13673-015-0029-y
• Qoussini, A. E. M., & Bt Jusoh, Y. Y. (2014). A Review on Personalization and Agents Technology in Mobile Learning. 2014 International Conference on Intelligent Environments, 260-264. https://doi.org/10.1109/IE.2014.49
• Scikit-learn: Machine learning in Python—Scikit-learn 0.24.2 documentation. (t.y.). Geliş tarihi 10 Temmuz 2021, gönderen https://scikit-learn.org/stable/
• Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A Deep Learning Approach to Network Intrusion Detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50. https://doi.org/10.1109/TETCI.2017.2772792
• Shuib, M., Abdullah, A., Azizan, S. N., & Gunasegaran, T. (2015). Designing an Intelligent Mobile Learning Tool for Grammar Learning (i-MoL). International Journal of Interactive Mobile Technologies (IJIM), 9(1), 41. https://doi.org/10.3991/ijim.v9i1.4238
• Skopt module. (2019). skopt module. https://scikit-optimize.github.io/
• Stergiou, C., & Psannis, K. E. (2017). Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: A survey. International Journal of Network Management, 27(3), e1930. https://doi.org/10.1002/nem.1930
• Thacker, R. A. (1995). Gender, influence tactics, and job characteristics preferences: New insights into salary determination. Sex Roles, 32(9), 617-638. https://doi.org/10.1007/BF01544215
• Traxler, J. (2007). Current State of Mobile Learning. Mobile Learning: Transforming the Delivery of Education and Training.
• Troussas, C., Chrysafiadi, K., & Virvou, M. (2018). Machine Learning and Fuzzy Logic Techniques for Personalized Tutoring of Foreign Languages. Içinde C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Ed.), Artificial Intelligence in Education (ss. 358-362). Springer
• International Publishing. https://doi.org/10.1007/978-3-319-93846-2_67
• Wei Yang, Kuanquan Wang, & Wangmeng Zuo. (2011). A fast and efficient nearest neighbor method for protein secondary structure prediction. 2011 3rd International Conference on Advanced Computer Control, 224-227. https://doi.org/10.1109/ICACC.2011.6016402
• Wu, T.-T., Huang, Y.-M., Chao, H.-C., & Park, J. H. (2014). Personlized English reading sequencing based on learning portfolio analysis. Information Sciences, 257, 248-263. https://doi.org/10.1016/j.ins.2011.07.021
• Xu, B., Zhu, X., & Zhu, H. (2019). An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network. IEEE Access, 7, 123912-123921. https://doi.org/10.1109/ACCESS.2019.2937831
• Yarandi, M., Jahankhani, H., Dastbaz, M., & Tawil, A. R. (2011). PERSONALISED MOBILE LEARNING SYSTEM BASED ON ITEM RESPONSE THEORY. 8.
• 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. https://aclanthology.org/C18-1292
• Zhou, J., & Wang, J. (2016). Unsupervised fabric defect segmentation using local patch approximation. The Journal of The Textile Institute, 107(6), 800-809. https://doi.org/10.1080/00405000.2015.1131440