Akademik Veri Yönetim Sistemi
International Conference on Mathematics and Mathematics Education (ICMME), Nevşehir, Türkiye, 3 - 05 Ekim 2024, ss.221-222
Education has been an important element of civilizations with a great impact on individuals and societies. Improving existing educational activities, materials and systems has a significant impact on social development. Nowadays, artificial intelligence has started to be used extensively in educational activities as in every field of technology. Language learning is one of the fields affected by these developments. With personalized language learning applications with artificial intelligence, content can be produced according to the level, learning speed and preferences of the individual. With an AI-based language learning application, pronunciation errors can be detected with instant feedback and the development of the individual can be supported by making corrections. Artificial intelligence can produce personalized language learning materials, i.e. personalized tests, and study texts. Thus, the diversity and effectiveness of language learning resources can be increased. With the personalized learning method, methods and materials are designed for each individual. In foreign language teaching, the development of content and methods in accordance with the interests of individuals increases the effectiveness and success of the learning process. For this purpose, computer-based training models are developed using machine learning algorithms and machine learning methods are used to improve student performance. Personalized learning process: virtual reality, game-based learning using augmented reality, communities of practice, adaptive technologies, learning analytics and e-assessment techniques are used. One of the important processes of foreign language education is assessment and evaluation. The assessment and evaluation process provides continuous feedback to individuals about how they are learning, the support they need and the progress they are making towards their learning goals.
In this study, a model was designed for determining the interests of the individual (technology, sports, health, etc.) and developing learning materials suitable for these interests, determining topics for listening activities and assessment and evaluation processes with machine learning methods. For this purpose, algorithms using artificial neural networks, support vector machines, k-nearest neighbors and decision trees were developed, and their performances were compared. With the most successful model, it will be possible to develop applications that offer content and measurement questions suitable for the interests of individuals and evaluate the level of achievement.