Kişiselleştirilmiş Yabancı Dil Öğrenimi İçin Makine Öğrenmesi Yöntemleriyle İlgi Alanı Tahmini

Year 2022, Volume: 12 Issue: 1, 111 - 121, 30.04.2022

Kübra Okumuş Dağdeler Yasin Görmez Merve Kavuklu

Abstract

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.

Keywords

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

Supporting Institution

Detay Teknoloji A. Ş. Araştırma Merkezi

Project Number

4005

Thanks

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

Abstract

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.

Keywords

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

Project Number

4005

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