Research Article
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Year 2022, Volume 23, Issue 2, 120 - 139, 30.03.2022
https://doi.org/10.17718/tojde.1096260

Abstract

References

  • Al Emran, M., & Shaalan, K. (2014). A survey of intelligent language tutoring systems. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 393-399). IEEE.
  • Alger, C., & Kopcha, T. J. (2009). eSupervision: A technology framework for the 21st century field experience in teacher education. Issues in Teacher Education, 18, 31-46.
  • Alharbi, S. (2016). Effect of teachers’ written corrective feedback on Saudi EFL university students’ writing achievements. International Journal of Linguistics, 8(5), 15-29.
  • Andersen, Q. E., Yannakoudakis, H., Barker, F., & Parish, T. (2013). Developing and testing a selfassessment and tutoring system. In Proceedings of the eighth workshop on innovative use of NLP for building educational applications (pp. 32-41).
  • Boling, E. C., Hough, M., Krinsky, H., Saleem, H., & Stevens, M. (2012). Cutting the distance in distance education: Perspectives on what promotes positive, online learning experiences. The Internet and Higher Education, 15(2), 118-126.
  • Bonnel, W. (2008). Improving feedback to students in online courses. Nursing Education Perspectives, 29(5), 290-294.
  • Borup, J., West, R. E., & 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.
  • Boud, D. & Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment & Evaluation in Higher Education, 38(6), 698-712. Retrieved from https://doi.org/10. 1080/02602938.2012.691462
  • Bozkurt, S., & Acar, Z. C. (2017). EFL students’ reflections on explicit and implicit written corrective feedback. The Eurasia Proceedings of Educational & Social Sciences, 7, pp. 98-102.
  • Caccamise, D., Franzke, M., Eckhoff, A., Kintsch, E., & Kintsch, W. (2007). Guided practice in technologybased summary writing. In D. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 375–396). Mahwah, NJ: Erlbaum.Cambridge English (2018). Write and improve. Retrieved from https://writeandimprove.com/
  • Cambridge English (2018). Write and improve. Retrieved from https://writeandimprove.com/
  • Carter, J. (1984). Instructional learner feedback: A literature review with implications for software development. The Computing Teacher, 12(2), 53–55.

AUTOMATED FEEDBACK AND TEACHER FEEDBACK: WRITING ACHIEVEMENT IN LEARNING ENGLISH AS A FOREIGN LANGUAGE AT A DISTANCE

Year 2022, Volume 23, Issue 2, 120 - 139, 30.03.2022
https://doi.org/10.17718/tojde.1096260

Abstract

Information and communication technologies have been transforming the way we teach and learn. Either for facilitating teaching practices or for making learning more interesting and joyful for the learners, artificial intelligence-based applications are utilized in recent years. In this connection, this study intends to examine if automated feedback and teacher feedback contribute to academic writing achievement and whether they differ in their effect on achievement in learning English as a foreign language in an open and distant learning context. The participants of the study were open education faculty students in a higher education institution in Turkey. In this quasi-experimental quantitative study repeated measures design was adopted. the participants were given writing tasks each week in a nine-week writing activity and they received feedback from their English language teachers for the first three tasks, and they received feedback from the software for the last three tasks. All participants wrote an English text as a diagnostic test at the beginning of the process. At the end of the teacher and software feedback phases, they took post-tests. All grades were statistically analyzed in order to find any effect of regular feedback either from a language teacher or from an online software on academic writing achievement. Results revealed significant differences between the diagnostic test and two achievement tests. Participants tended to improve their academic writing skills by taking regular feedback, and it was observed that the writing scores increased slightly more when receiving feedback from teachers compared to automated feedback software.

References

  • Al Emran, M., & Shaalan, K. (2014). A survey of intelligent language tutoring systems. In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 393-399). IEEE.
  • Alger, C., & Kopcha, T. J. (2009). eSupervision: A technology framework for the 21st century field experience in teacher education. Issues in Teacher Education, 18, 31-46.
  • Alharbi, S. (2016). Effect of teachers’ written corrective feedback on Saudi EFL university students’ writing achievements. International Journal of Linguistics, 8(5), 15-29.
  • Andersen, Q. E., Yannakoudakis, H., Barker, F., & Parish, T. (2013). Developing and testing a selfassessment and tutoring system. In Proceedings of the eighth workshop on innovative use of NLP for building educational applications (pp. 32-41).
  • Boling, E. C., Hough, M., Krinsky, H., Saleem, H., & Stevens, M. (2012). Cutting the distance in distance education: Perspectives on what promotes positive, online learning experiences. The Internet and Higher Education, 15(2), 118-126.
  • Bonnel, W. (2008). Improving feedback to students in online courses. Nursing Education Perspectives, 29(5), 290-294.
  • Borup, J., West, R. E., & 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.
  • Boud, D. & Molloy, E. (2013). Rethinking models of feedback for learning: the challenge of design. Assessment & Evaluation in Higher Education, 38(6), 698-712. Retrieved from https://doi.org/10. 1080/02602938.2012.691462
  • Bozkurt, S., & Acar, Z. C. (2017). EFL students’ reflections on explicit and implicit written corrective feedback. The Eurasia Proceedings of Educational & Social Sciences, 7, pp. 98-102.
  • Caccamise, D., Franzke, M., Eckhoff, A., Kintsch, E., & Kintsch, W. (2007). Guided practice in technologybased summary writing. In D. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 375–396). Mahwah, NJ: Erlbaum.Cambridge English (2018). Write and improve. Retrieved from https://writeandimprove.com/
  • Cambridge English (2018). Write and improve. Retrieved from https://writeandimprove.com/
  • Carter, J. (1984). Instructional learner feedback: A literature review with implications for software development. The Computing Teacher, 12(2), 53–55.

Details

Primary Language English
Subjects Social
Journal Section Articles
Authors

Ayse TASKIRAN This is me (Primary Author)
ANADOLU UNIVERSITY
Türkiye


Nil GOKSEL
ANADOLU UNIVERSITY
Türkiye

Publication Date March 30, 2022
Application Date September 3, 2020
Acceptance Date
Published in Issue Year 2022, Volume 23, Issue 2

Cite

APA Taskıran, A. & Goksel, N. (2022). AUTOMATED FEEDBACK AND TEACHER FEEDBACK: WRITING ACHIEVEMENT IN LEARNING ENGLISH AS A FOREIGN LANGUAGE AT A DISTANCE . Turkish Online Journal of Distance Education , 23 (2) , 120-139 . DOI: 10.17718/tojde.1096260