Anadolu University Open Education Faculty Students’ Opinions on the Use of Artificial Intelligence Based Systems and Applications

Yıl 2021, Cilt: 18 Sayı: 43, 6119 - 6142, 07.11.2021

Nil Göksel

https://doi.org/10.26466/opus.937331

Öz

This study aims to examine to what extent are the students aware of the AI based systems used in language learning. The design of the study was determined as a quantitative research method. For this purpose, a web-based questionnaire was applied to 204 students with A1 and A2 English levels enrolled in the Open Education Faculty, and thus data were collected about the students' views on Artificial Intelligence-based systems that they use to improve their English speaking and writing skills. The data obtained from the students were analyzed using descriptive statistical procedures such as frequency and percentage analysis. The findings of the study revealed that many of the distance education students do not have an idea whether AI-based applications have an effect on improving their English 'speaking' skills. It also showed that most students who participated in this study had no idea whether these practices had an effect on improving their English ‘writing’ skills. In this context, the study proves that students are limitedly aware of Artificial Intelligence-based systems used while learning foreign languages, and therefore, more studies on the subject should be conducted and these systems should be introduced to students.

Anahtar Kelimeler

Artificial Intelligence Based Systems, Distance Education, Foreign Language Learning, Open Education Faculty Students

Destekleyen Kurum

Anadolu Üniversitesi

Proje Numarası

--

Teşekkür

--

Anadolu Üniversitesi Açıköğretim Fakültesi Öğrencilerinin Yapay Zekâ Tabanlı Sistemlerin Kullanımı ve Uygulamalarına İlişkin Görüşleri

Öz

Bu çalışma, Açıköğretim Fakültesi öğrencilerinin yabacı dil öğrenirken kullandıkları Yapay Zekâ tabanlı sistemler hakkında ne kadar haberdar olduklarını incelemeyi amaçlamaktadır. Çalışmanın deseni nicel araştırma yöntemi olarak belirlenmiştir. Bu amaçla, Açıköğretim Fakültesine kayıtlı A1 ve A2 İngilizce seviyelerine sahip 204 öğrenciye web tabanlı bir anket uygulanmış ve böylece öğrencilerin İngilizce konuşma ve yazma becerilerini geliştirmek için kullanıldıkları Yapay Zekâ tabanlı sistemlere ilişkin görüşleri hakkında veriler toplanmıştır. Öğrencilerden elde edilen veriler, frekans ve yüzde analizi gibi betimsel istatistik prosedürleri kullanılarak analiz edilmiştir. Araştırmanın bulguları, uzaktan eğitim öğrencilerinin birçoğunun Yapay Zekâ temelli uygulamaların İngilizce 'konuşma' becerilerini geliştir-mede etkisi olup olmadığı konusunda fikir sahibi olmadıklarını ortaya koymuştur. Ayrıca, bu çalışmaya katılan çoğu öğrencinin, bu uygulamaların İngilizce ‘yazma’ becerilerini geliştirmede bir etkisi olup olmadığı konusunda bir fikri olmadığını göstermiştir. Bu bağlamda çalışma, öğrencilerin yabancı dil öğrenirken kullanılan Yapay Zekâ temelli sistemlerden sınırlı ölçüde haberdar olduklarını ve bu nedenle konu ile ilgili daha fazla çalışma yapılması ve bu sistemlerin öğrencilere tanıtılması gerektiğini kanıtlamaktadır.

Anahtar Kelimeler

Yapay Zekâ Tabanlı Sistemler, Uzaktan Eğitim, Yabancı Dil Öğrenimi, Açıköğretim Fakültesi Öğrencileri

Proje Numarası

--

Kaynakça

• Abad, J. V. (2013). Pedagogical factors that influence EFL teaching: Some considerations for teachers' professional development. Profile Issues in Teachers Professional Development, 15(1), 97-108.
• Adams, R.L. (2017). 10 Powerful Examples of Artificial Intelligence in Use Today. Forbes.
• Al Emran, M. and Shaalan, K. (2014). A survey of intelligent language tutoring systems. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (p.393-399). IEEE.
• Alhabbash, M. I., Mahdi, A. O., and Abu Naser, S. S. (2016). An Intelligent Tutoring System for Teaching Grammar English Tenses. European Academic Research, 6(9), 7743-7757.
• Andersen, Q. E., Yannakoudakis, H., Barker, F. and Parish, T. (2013). Developing and testing a self-assessment and tutoring system. In Proceedings of the eighth workshop on innovative use of NLP for building educational applications (p.32-41).
• Berdasco, A., López, G., Diaz, I., Quesada, L. and Guerrero, L. A. (2019). User experience comparison of intelligent personal assistants: Alexa, Google Assistant, Siri and Cortana. In Multidisciplinary Digital Publishing Institute Proceedings 31(1), 51. https://doi.org/10.3390/proceedings2019031051
• Borup, J., West, R. E. and Thomas, R. (2015). The impact of text versus video communication on instructor feedback in blended courses. Educational Technology Research and Development, 63(2), 161-184.
• Botsify. (n.d.). Enhance Learning with Education Chatbot. Retrieved from https://botsify.com/education-chatbot.
• Boud, D. and Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment and Evaluation in Higher Education, 38(6), 698-712.
• Brill, T. M., Munoz, L. and Miller, R. J. (2019). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15-16), 1401-1436.
• Brusilovsky, P. and Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13, 159-172.
• Cotos, E. (2011). Potential of automated writing evaluation feedback. Calico Journal, 28(2), 420-459.
• Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Boston, MA: Pearson.
• Daneshvar, E. and Rahimi, A. (2014). Written corrective feedback and teaching grammar. Procedia-Social and Behavioral Sciences, 136, 217-221.
• Dizon, G. (2017). Using intelligent personal assistants for second language learning: A case study of alexa. TESOL Journal, 8(4), 811-830.
• Fadzil, M. and Munira, T. A. (2008). Applications of Artificial Intelligence in an Open and Distance Learning institution. In 2008 International Symposium on Information Technology, IEEE, 2008 , 1- 7, August, 2008.
• Goldberg, Y. (2016). A primer on neural network models for natural language processing. Journal of Artificial Intelligence Research, 57, 345-420.
• Goldsmith, L. (2014). Digital feedback: An integral part of the online classroom. Distance Learning, 11(2), 33.
• Hamuddin, B. (2018, May). Blogs as powerful learning tools: the perception from efl students in riau main island Indonesia. In IOP Conference Series: Earth and Environmental Science, 156 (1), 1-6. IOP Publishing.
• Heift, T. and Vyatkina, N. (2017). Technologies for teaching and learning L2 grammar. The handbook of technology and second language teaching and learning, 26-44.
• Heift, T. and Nicholson, D. (2001). Web delivery of adaptive and interactive language tutoring. International Journal of Artificial Intelligence in Education, 12(4), 310–325.
• Heift, T. (1998) Designed intelligence: A language teacher model. PhD Dissertation. Simon Fraser University, Canada.
• Işik, A. H., and Aslan, G. (2018). Review and Proposal for Intelligent Distance Education. In I. Management Association (Ed.), Intelligent Systems: Concepts, Methodologies, Tools, and Applications (p.73-88). IGI Global. http://doi:10.4018/978-1-5225-5643-5.ch004
• Jyothirmayi, N., Soniya, A., Grace, Y., Reddy, C. K. K. and Murthy, B. R. (2019). Survey on Chatbot conversational system. Journal of Applied Science and Computations, 6(1), January 2019.
• Kannan, J. and Munday, P. (2018). New trends in second language learning and teaching through the lens of ICT, networked learning, and artificial intelligence. In Fernández Juncal, C. and Hernández Muñoz, N. (Eds.), Vías de transformación en la enseñanza de lenguas con mediación tecnológica. Círculo de Lingüística Aplicada a la Comunicación, 76, 13-30. Retrieved from: http://dx.doi.org/10.5209/CLAC.62495
• Kessler, G., and Plakans, L. (2008). Does teachers’ confidence with CALL equal innovative and integrated use? Computer Assisted Language Learning, 21(3), 269-282.
• Dolenc, K., and Aberšek, B. (2015). TECH8 intelligent and adaptive e-learning system: Integration into technology and science classrooms in lower secondary schools. Computers and Education, 82, 354-365.
• Kumar, V. (2016). Introduction: is customer satisfaction (ir) relevant as a metric? Journal of Marketing, 80(5), 108-109.
• Link, S., Dursun, A., Karakaya, K., and Hegelheimer, V. (2014). Towards better ESL practices for implementing automated writing evaluation. Calico Journal, 31(3),323-344, http://dx.doi. org/10.11139/cj.31.3.323-344
• Looi, C.K., McCalla, G., Bredeweg, B. and Breuker, J. (2005), Artificial intelligence in education: Supporting learning through intelligent and socially informed technology, IOS Press, Amsterdam, NLD.
• Lopatovska, I., Rink, K., Knight, I., Raines, K., Cosenza, K., Williams, H., ... and Martinez, A. (2019). Talk to me: Exploring user interactions with the Amazon Alexa. Journal of Librarianship and Information Science, 51(4), 984-997.
• Lu, X. (2018). Natural language processing and intelligent computer-assisted language learning (ICALL). In Liontas, J.I. (Ed.), The TESOL Encyclopedia of English Language Teaching. Chichester: Wiley Blackwell. Electronic publication. http://doi: 10.1002/9781118784235.eelt0422
• MATHiaU. (n.d.). Take your math classroom to the next level. Retrieved from https://www.carnegielearning.com/products/software-platform/mathiau-learning-software/
• McCalla, G. (2000). The fragmentation of culture, learning, teaching and technology: implications for the artificial intelligence in education research agenda in 2010. International Journal of Artificial Intelligence in Education, 11(2), 177-196.
• Mills, G. E., and Gay, L. R. (2019). Educational research: Competencies for analysis and applications. Pearson. One Lake Street, Upper Saddle River, New Jersey 07458.
• Moore, M. G., and Kearsley, G. (2012). Distance education: A systems view of online learning. Cengage Learning. Moussalli, S., and Cardoso, W. (2020). Intelligent personal assistants: can they understand and be understood by accented L2 learners?. Computer Assisted Language Learning, 33(8), 865-890.
• Nenkov, N., Dimitrov, G., Dyachenko, Y., and Koeva, K. (2016, September). Artificial Intelligence Technologies for Personnel Learning Management Systems. 2016 IEEE 8th International Conference on Intelligent Systems. Proceedings, Sofia, Bulgaria, 189-194.
• Nordrum, A. (2017). CES 2017: the year of voice recognition. IEEE Spectrum. Retrieved from http://spectrum.ieee.org/techtalk/consumerelectronics/gadgets/ces-2017-the-year-of-voice-recognition Ozobot. (n.d.). STEAM made simple. Retrieved from https://ozobot.com/stem-education
• Paul, A., Haque-Latif, A., Amin-Adnan, F., and Rahman, R. M. (2019). Focused domain contextual AI chatbot framework for resource poor languages. Journal of Information and Telecommunication, 3(2), 248-269.
• Pokulevska, A. I. (2018). Experience of using skype as a means of foreign languages teaching efficiency improving. Information Technologies and Learning Tools, 68(6), 155-165.
• Puspitasari, K. A. (2010). Student assessment. Policy and Practice. In T. Belawati and J. Baggaley (Eds.). Asian Distance Education (pp.60-65). New Delhi: SAGE.
• Roll, I., and Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599.
• Schulze, M. and T. Heift (2013). Intelligent CALL. In M. Thomas, H. Reinders and M. Warschauer (eds.), Contemporary computer-assisted language learning. London: Bloomsbury, p.249–265.
• Simonson, M., Smaldino, S., Albright, M. and Zvacek, S. (2006). Teaching and learning at a distance: Foundations of distance education (3rd ed.). Upper Saddle River, NJ: Pearson.
• Sivakumar, S., Venkataraman, S. and Gombiro, C. (2015). A user-intelligent adaptive learning model for learning management system using data mining and artificial intelligence. International Journal for Innovative Research in Science and Technology, 1(10), 78-81.
• Snatchbot. (n.d.). Enhancing the Classroom with Chatbots. Retrieved from https://snatchbot.me/education
• Tafazoli, D., María, E. G. and Abril, C. A. H. (2019). Intelligent Language Tutoring System: Integrating Intelligent Computer-Assisted Language Learning into Language Education. International Journal of Information and Communication Technology Education (IJICTE), 15(3), 60-74.
• Taskiran, A., and Yazici, M. (2021). Formative Feedback in Online Distance Language Learning: Boosting Motivation With Automated Feedback. In Motivation, Volition, and Engagement in Online Distance Learning (p. 100-125). IGI Global. http://dx.doi.org/10.4018/978-1-7998-7681-6.ch005
• Timms, M. J. (2016). Letting artificial intelligence in education out of the box: educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712.
• Underwood, J. (2017). Exploring AI language assistants with primary EFL students. In K. Borthwick, L. Bradley and S. Thouesny (Eds.), CALL in a climate of change: Adapting to turbulent global conditions – Short papers from EUROCALL, 317-321.
• Wang, F. and Wang, S. (2012). A comparative study on the influence of automated evaluation system and teacher grading on students’ English writing. Procedia Engineering, 29(2012), 993-997. http://doi:10.1016/j.proeng.2012.01.077
• Wang, L., Qian, Y., Scott, M., Chen, G. and Soong, F. (2012). Computer-assisted audiovisual language learning. Computer, 45(6), 38-47.
• Webber, B. L., and Nilsson, N. J. (Eds.). (2014). Readings in artificial intelligence. Morgan Kaufmann.
• Wiranti, N. A. (2013). Improving students’ speaking competence by using jazz chants (A Classroom Action Research at the Third Semester of English Department, Teacher Training and Education Faculty of Purwokerto Muhammadiyah University in the Academic Year of 2012-2013) (Doctoral dissertation, UNS (Sebelas Maret University)).