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Çevrim içi dil öğrenimi için yapay zeka tabanlı otomatik yazma değerlendirmesi: Uzaktan öğrenenlerin algıları

Yıl 2022, , 111 - 129, 31.05.2022
https://doi.org/10.33400/kuje.1053862

Öz

Bilgi ve iletişim teknolojilerinin yaygınlaşması, öğrenenlerin zamandan ve mekandan bağımsız öğrenme taleplerinin artması ve öğrenen profilindeki hızlı değişimlerle birlikte açık ve uzaktan öğrenme yükseköğretim kurumları tarafından giderek daha fazla benimsenir hale gelmiştir. Giderek artan öğrenci sayısı karşısında açık ve uzaktan öğrenme hizmeti veren yükseköğretim kurumları çevrim içi öğrenmenin etkinliğini ve verimliliğini en üst düzeye çıkarmak için yapay zeka tabanlı teknolojilerden yararlanmaya başlamıştır. Bu teknolojiler arasında yer alan otomatik yazma değerlendirme (OYD) araçları özellikle yabancı dilde yazma etkinliklerinde etkili ve verimli biçimlendirici geri bildirim sağlama potansiyeline sahiptir. Konuyla ilgili alanyazın incelendiğinde, OYD araçlarının yabancı dilde yazma becerisinin geliştirilmesindeki etkililiğinin çoğunlukla öğretmenlerin desteğiyle yüz yüze öğrenme bağlamlarında incelendiği görülmektedir. Ancak, bu OYD araçlarının açık ve uzaktan öğrenme bağlamlarında yabancı dil olarak İngilizce yazma becerisinin geliştirilmesine yönelik kullanımına ilişkin yeterli çalışma olmadığı dikkati çekmektedir. Bu çalışma, dört hafta süren yabancı dil yazma etkinliğini gönüllü olarak tamamlamış yetişkin uzaktan İngilizce öğrenenlerin OYD aracı deneyimlerine ilişkin görüşlerini ortaya çıkarmayı amaçlamaktadır. Çevrim içi açık uçlu anket yoluyla toplanan veriler, öğrencilerin sürece ilişkin değerlendirmelerini, aldıkları otomatik geri bildirimin yazma becerilerine nasıl katkıda bulunduğunu ve OYD'nin uzaktan dil öğreniminde kullanımına yönelik önerilerini ortaya çıkarmıştır. Elde edilen verilerin uzaktan dil öğrenme süreçlerinde OYD araçlarının kullanımının etkinliği konusunda literatüre katkı sağlaması beklenmektedir.

Kaynakça

  • Ai, H. (2017). Providing graduated corrective feedback in an intelligent computer assisted language learning environment. ReCALL, 29(3), 313–334. https://doi.org/10.1017/S095834401700012X
  • Alvarez, L., Ananda, S., Walqui, A., Sato, E., & Rabinowitz, S. (2014). Focusing formative assessment on the needs of English language learners. WestEd.
  • Andersen, Q. E., Yannakoudakis, H., Barker, F., & 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 (pp. 32-41).
  • Ariyanto, M. S. A., Mukminatien, N., & Tresnadewi, S. (2021). College Students' Perceptions of an Automated Writing Evaluation as a Supplementary Feedback Tool in a Writing Class. Jurnal Ilmu Pendidikan, 27(1), 41-51. http://dx.doi.org/10.17977/um048v27i1p41-51
  • Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment & Evaluation in higher education, 38(6), 698-712. https://doi.org/10.1080/02602938.2012.691462
  • Chaudhary, S., & Dey, N. (2013). Assessment in open and distance learning system (ODL): A challenge. Open Praxis, 5(3), 207–216. https://doi.org/10.5944/openpraxis.5.3.65
  • Cotos, E. (2015). AWE for writing pedagogy: From healthy tension to tangible prospects. Writing and Pedagogy, 7 (2-3), 197–231. https://dr.lib.iastate.edu/handle/20.500.12876/23641
  • Cotos, E., Huffman, S., & Link, S. (2020). Understanding Graduate Writers' Interaction with and Impact of the Research Writing Tutor during Revision. Journal of Writing Research, 12(1). https://doi.org/10.17239/jowr-2020.12.01.07
  • Curry, N., & Riordan, E. (2021). Intelligent CALL Systems for Writing Development: Investigating the Use of Write & Improve for Developing Written Language and Writing Skills. In CALL Theory Applications for Online TESOL Education (pp. 252-273). IGI Global.
  • Daniel, J. S. (2019). Open Universities: Old concepts and contemporary challenges. International Review of Research in Open and Distributed Learning, 20(4), 195-211. https://doi.org/10.19173/irrodl.v20i3.4035
  • Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & De Weerdt, J. (2020). A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education, 162, 104094. https://doi. org/10.1016/j.compedu.2020.104094
  • Elliot, S., & Mikulua, C. (2004). The impact of MyAccess! Use on student writing performance: A tech- nology overview and four studies. Paper presented at the Annual Meeting of the American Educational Research Association, San Diego, CA.
  • Guri-Rosenblit, S. (2018). E-Teaching in Higher Education: An Essential Prerequisite for E-Learning. Journal of New Approaches in Educational Research, 7(2), PP. 93-97. https://doi.org/10.7821/naer.2018.7.298
  • Guri-Rosenblit, S. (2019). Open Universities: Innovative past, challenging present, and prospective future. International Review of Research in Open and Distributed Learning, 20(4), 179–194. https://doi. org/10.19173/irrodl.v20i4.4034
  • Hockly, N. (2019). Automated writing evaluation. ELT Journal, 73(1), 82–88. https://doi.org/10.1093/elt/-ccy044
  • Hyland, K., & Hyland, F. (2006). Feedback on second language students' writing. Language Teaching, 39(2), 83-101. https://doi.org/10.1017/S0261444806003399
  • Karadağ, N., Akyildiz, M., Kumtepe, A. T., & Akgün, H. R. (2017). Anadolu Üniversitesi Açıköğretim Sistemi soru yazarlarının ölçme ve değerlendirme seminerlerine ilişkin görüşleri. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 3(3), 9-46.
  • Kellogg, R. T., Whiteford, A. P., & Quinlan, T. (2010). Does automated feedback help students to write?
  • Journal of Educational Computing Research, 42(2), 173–196. https://doi.org/10.2190/EC.42.2.c
  • Link, S., Mehrzad, M., & Rahimi, M. (2020). Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement. Computer Assisted Language Learning, 33, 1–30. https://doi.org/10.1080/09588221.2020.1743323
  • Ogawa, R. T., & Malen, B. (1991). Towards rigor in reviews of multivocal literatures: Applying the exploratory case study method. Review of educational research, 61(3), 265-286.
  • Puspitasari, K. A. (2010). Student assessment. In Policy and Practice in Asian Distance Education. New Delhi: SAGE.
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.100740593-016-0110-3
  • Shermis, M.D., Garvan, C.W., & Diao, Y. (2008, March). The Impact of Automated Essay Scoring on Writing Outcomes. [Paper presentation]. In the Annual Meetings of the National Council on Measurement in Education. New York, NY.
  • Simonson, M., Smaldino, S., & Zvacek, S. (2015). Teaching and learning at a distance: Foundations of distance education (6th ed.). IAP.
  • Stevenson, M. (2016). A critical interpretative synthesis: The integration of Automated Writing Evaluation into classroom writing instruction. Computers and Composition, 42, 1-16. https://doi.org/10.1016/j.compcom.2016.05.001
  • Stevenson, M., & Phakiti, A. (2014). The effects of computer-generated feedback on the quality ofwriting. Assessing Writing, 19, 51-65. https://doi.org/10.1016/j.asw.2013.11.007
  • Stevenson, M., & Phakiti, A. (2019). Automated feedback and second language writing. In Feedback in second language writing: Contexts and issues (pp. 125-142). Cambridge University Press.
  • Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques. Thousand oaks, CA: Sage publications.
  • Strøm, A., & Fagermoen, M. S. (2012). Systematic data integration—A method for combined analyses of field notes and interview texts. International Journal of Qualitative Methods, 11(5), 534-546. https://doi.org/10.1177%2F160940691201100502
  • Wang, F., & Wang, S. (2012). A comparative study on the influence of automated evaluation system and teacher grading on students’ English writing. Procedia Engineering, 29, 993-997. https://doi.org/10.1016/j.proeng.2012.01.077
  • Wang, P., & Wang, P. (2015). Effects of an automated writing evaluation program: Student experiences and perceptions. Electronic Journal of Foreign Language Teaching, 12(1), 79-100.
  • Weld, D. S., Adar, E., Chilton, L., Hoffmann, R., Horvitz, E., Koch, M., . . . Mausam, M. (2012, July). Personalized online education—a crowdsourcing challenge. Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.
  • Wilson, J., & Czik, A. (2016). Automated essay evaluation software in English Language Arts classrooms: Effects on teacher feedback, student motivation, and writing quality. Computers & Education, 100, 94-109. https://doi.org/10.1016/j.compedu.2016.05.004
  • Woodworth, J., & Barkaoui, K. (2020). Perspectives on Using Automated Writing Evaluation Systems to Provide Written Corrective Feedback in the ESL Classroom. TESL Canada Journal, 37(2), 234-247. https://doi.org/10.18806/tesl.v37i2.1340
  • Zhang, Z. V., & Hyland, K. (2018). Student engagement with teacher and automated feedback on L2 writing. Assessing Writing, 36, 90-102. https://doi.org/10.1016/j.asw.2018.02.004
  • Zhu, M., Liu, O. L., & Lee, H. S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, 103668. https://doi.org/10.1016/j.compedu.2019.103668

AI-based automated writing evaluation for online language learning: Perceptions of distance learners

Yıl 2022, , 111 - 129, 31.05.2022
https://doi.org/10.33400/kuje.1053862

Öz

With the spread of information and communication technologies, increasing demands for learning independent of time and place, and rapid changes in the learner profile, open and distance learning has been increasingly adopted by higher education institutions. In the face of the increasing number of students, these institutions have started to make use of artificial intelligence-based technologies to maximize the effectiveness and efficiency of online learning. Automated writing evaluation (AWE) tools, which are among these technologies, have the potential to provide effective and efficient formative feedback, especially in foreign language writing activities. Based on the relevant literature, it is seen that the effectiveness AWE tools has been examined in face-to-face learning contexts with the support of teachers. However, there are not enough studies on the use of these AWE tools in open and distance learning contexts. This study aims to reveal the views of adult distance English language learners regarding their AWE tool experiences following a four-week writing activity. Data gathered through online open-ended questionnaire revealed learners' evaluation of the process, how the feedback they received contributed to their writing skills, and their suggestions for the use of AWE in distance language learning. It is expected that the obtained data will contribute to the literature on the effectiveness of the use of AWE tools in distance language learning processes.

Kaynakça

  • Ai, H. (2017). Providing graduated corrective feedback in an intelligent computer assisted language learning environment. ReCALL, 29(3), 313–334. https://doi.org/10.1017/S095834401700012X
  • Alvarez, L., Ananda, S., Walqui, A., Sato, E., & Rabinowitz, S. (2014). Focusing formative assessment on the needs of English language learners. WestEd.
  • Andersen, Q. E., Yannakoudakis, H., Barker, F., & 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 (pp. 32-41).
  • Ariyanto, M. S. A., Mukminatien, N., & Tresnadewi, S. (2021). College Students' Perceptions of an Automated Writing Evaluation as a Supplementary Feedback Tool in a Writing Class. Jurnal Ilmu Pendidikan, 27(1), 41-51. http://dx.doi.org/10.17977/um048v27i1p41-51
  • Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment & Evaluation in higher education, 38(6), 698-712. https://doi.org/10.1080/02602938.2012.691462
  • Chaudhary, S., & Dey, N. (2013). Assessment in open and distance learning system (ODL): A challenge. Open Praxis, 5(3), 207–216. https://doi.org/10.5944/openpraxis.5.3.65
  • Cotos, E. (2015). AWE for writing pedagogy: From healthy tension to tangible prospects. Writing and Pedagogy, 7 (2-3), 197–231. https://dr.lib.iastate.edu/handle/20.500.12876/23641
  • Cotos, E., Huffman, S., & Link, S. (2020). Understanding Graduate Writers' Interaction with and Impact of the Research Writing Tutor during Revision. Journal of Writing Research, 12(1). https://doi.org/10.17239/jowr-2020.12.01.07
  • Curry, N., & Riordan, E. (2021). Intelligent CALL Systems for Writing Development: Investigating the Use of Write & Improve for Developing Written Language and Writing Skills. In CALL Theory Applications for Online TESOL Education (pp. 252-273). IGI Global.
  • Daniel, J. S. (2019). Open Universities: Old concepts and contemporary challenges. International Review of Research in Open and Distributed Learning, 20(4), 195-211. https://doi.org/10.19173/irrodl.v20i3.4035
  • Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & De Weerdt, J. (2020). A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education, 162, 104094. https://doi. org/10.1016/j.compedu.2020.104094
  • Elliot, S., & Mikulua, C. (2004). The impact of MyAccess! Use on student writing performance: A tech- nology overview and four studies. Paper presented at the Annual Meeting of the American Educational Research Association, San Diego, CA.
  • Guri-Rosenblit, S. (2018). E-Teaching in Higher Education: An Essential Prerequisite for E-Learning. Journal of New Approaches in Educational Research, 7(2), PP. 93-97. https://doi.org/10.7821/naer.2018.7.298
  • Guri-Rosenblit, S. (2019). Open Universities: Innovative past, challenging present, and prospective future. International Review of Research in Open and Distributed Learning, 20(4), 179–194. https://doi. org/10.19173/irrodl.v20i4.4034
  • Hockly, N. (2019). Automated writing evaluation. ELT Journal, 73(1), 82–88. https://doi.org/10.1093/elt/-ccy044
  • Hyland, K., & Hyland, F. (2006). Feedback on second language students' writing. Language Teaching, 39(2), 83-101. https://doi.org/10.1017/S0261444806003399
  • Karadağ, N., Akyildiz, M., Kumtepe, A. T., & Akgün, H. R. (2017). Anadolu Üniversitesi Açıköğretim Sistemi soru yazarlarının ölçme ve değerlendirme seminerlerine ilişkin görüşleri. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 3(3), 9-46.
  • Kellogg, R. T., Whiteford, A. P., & Quinlan, T. (2010). Does automated feedback help students to write?
  • Journal of Educational Computing Research, 42(2), 173–196. https://doi.org/10.2190/EC.42.2.c
  • Link, S., Mehrzad, M., & Rahimi, M. (2020). Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement. Computer Assisted Language Learning, 33, 1–30. https://doi.org/10.1080/09588221.2020.1743323
  • Ogawa, R. T., & Malen, B. (1991). Towards rigor in reviews of multivocal literatures: Applying the exploratory case study method. Review of educational research, 61(3), 265-286.
  • Puspitasari, K. A. (2010). Student assessment. In Policy and Practice in Asian Distance Education. New Delhi: SAGE.
  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.100740593-016-0110-3
  • Shermis, M.D., Garvan, C.W., & Diao, Y. (2008, March). The Impact of Automated Essay Scoring on Writing Outcomes. [Paper presentation]. In the Annual Meetings of the National Council on Measurement in Education. New York, NY.
  • Simonson, M., Smaldino, S., & Zvacek, S. (2015). Teaching and learning at a distance: Foundations of distance education (6th ed.). IAP.
  • Stevenson, M. (2016). A critical interpretative synthesis: The integration of Automated Writing Evaluation into classroom writing instruction. Computers and Composition, 42, 1-16. https://doi.org/10.1016/j.compcom.2016.05.001
  • Stevenson, M., & Phakiti, A. (2014). The effects of computer-generated feedback on the quality ofwriting. Assessing Writing, 19, 51-65. https://doi.org/10.1016/j.asw.2013.11.007
  • Stevenson, M., & Phakiti, A. (2019). Automated feedback and second language writing. In Feedback in second language writing: Contexts and issues (pp. 125-142). Cambridge University Press.
  • Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques. Thousand oaks, CA: Sage publications.
  • Strøm, A., & Fagermoen, M. S. (2012). Systematic data integration—A method for combined analyses of field notes and interview texts. International Journal of Qualitative Methods, 11(5), 534-546. https://doi.org/10.1177%2F160940691201100502
  • Wang, F., & Wang, S. (2012). A comparative study on the influence of automated evaluation system and teacher grading on students’ English writing. Procedia Engineering, 29, 993-997. https://doi.org/10.1016/j.proeng.2012.01.077
  • Wang, P., & Wang, P. (2015). Effects of an automated writing evaluation program: Student experiences and perceptions. Electronic Journal of Foreign Language Teaching, 12(1), 79-100.
  • Weld, D. S., Adar, E., Chilton, L., Hoffmann, R., Horvitz, E., Koch, M., . . . Mausam, M. (2012, July). Personalized online education—a crowdsourcing challenge. Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence.
  • Wilson, J., & Czik, A. (2016). Automated essay evaluation software in English Language Arts classrooms: Effects on teacher feedback, student motivation, and writing quality. Computers & Education, 100, 94-109. https://doi.org/10.1016/j.compedu.2016.05.004
  • Woodworth, J., & Barkaoui, K. (2020). Perspectives on Using Automated Writing Evaluation Systems to Provide Written Corrective Feedback in the ESL Classroom. TESL Canada Journal, 37(2), 234-247. https://doi.org/10.18806/tesl.v37i2.1340
  • Zhang, Z. V., & Hyland, K. (2018). Student engagement with teacher and automated feedback on L2 writing. Assessing Writing, 36, 90-102. https://doi.org/10.1016/j.asw.2018.02.004
  • Zhu, M., Liu, O. L., & Lee, H. S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, 103668. https://doi.org/10.1016/j.compedu.2019.103668
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Araştırma Makaleleri
Yazarlar

Ayşe Taşkıran 0000-0003-1913-7296

Yayımlanma Tarihi 31 Mayıs 2022
Gönderilme Tarihi 5 Ocak 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Taşkıran, A. (2022). AI-based automated writing evaluation for online language learning: Perceptions of distance learners. Kocaeli Üniversitesi Eğitim Dergisi, 5(1), 111-129. https://doi.org/10.33400/kuje.1053862



22176

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