Adaptive Wireless Network Model with Reinforcement Learning for Language Proficiency Development
Nazmiya MukhitdinovaProfessor, Samarkand State University named after Sharof Rashidov Uzbekistan. muxitdinovanazmiya@gmail.com0000-0001-7108-7244
Manzura ShamsitdinovaAssociate Professor, Department of Foreign Languages, Tashkent State University of Law, Tashkent, Uzbekistan. m.shamsitdinova@tsul.uz0000-0001-8078-3559
Ugiloy BolbekovaLecturer of department of "Language Teaching", Samarkand Institute of Economy and Service, Samarkand Institute of Economics and Service, Uzbekistan. ugiloybolbekova@gmail.com0009-0009-1739-744X
Obidjon OtamuratovAssociate Professor, Department of "Exact Sciences", Kimyo International University in Tashkent, Uzbekistan. obidjon.otamuratov@gmail.com0009-0003-2460-0879
Dildora BuranovaLecturer of Department of Uzbek Linguistics, Termez State University, Uzbekistan. mrsdildora.buranova@gmail.com0009-0001-4880-0758
Mukhayyo KambarovaPhD in Philological Science, Associate Professor, Department of Foreign Languages, Tashkent University of Architecture and Civil Engineering, Uzbekistan. mukhayyo.kambarova@gmail.com0000-0002-6067-2234
Dilfuza AzimovaPhD Researcher, Assistant Lecturer of Practical English Department, National University of Uzbekistan. dilfuzazimova89@gmail.0000-0001-9015-4801
Ibrokhim SapaevHead of the Department Physics and Chemistry, “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, Tashkent, Uzbekistan sapaevibrokhim@gmail.com0000-0003-2365-1554
This study gives a new way to learn English, showing its importance in today's globalized world. The study recognizes how difficult it is to learn a language. It offers a new way to use wireless networks to create a flexible and adaptable learning environment by combining the latest developments in Reinforcement Learning (RL) algorithms with ideas from the field of education. This method produces a personalized learning path by thoroughly testing students' reading, listening, and observation skills. An application using the suggested method was carefully built by fifty people, who used RL techniques to check students' progress and identify their strengths. The model's fantastic success is shown by the fact that it passes 96% of tests and is accurate 95% of the time.479 Cross-linguistic skills include analytical thinking, good communication, and standard language skills that the system aims to improve. Students worldwide can benefit from this method, which combines cutting-edge technology with traditional teaching methods to create a personalized and adaptable learning environment that helps students succeed in today's changing educational environment.