Araştırma Makalesi
BibTex RIS Kaynak Göster

ÜNİVERSİTE ÖĞRENCİLERİNİN AKRAN GERİ BİLDİRİMİ VERME DAVRANIŞLARININ SÜREÇ MADENCİLİĞİ İLE İNCELENMESİ

Yıl 2022, Cilt: 12 Sayı: 2, 412 - 433, 30.07.2022
https://doi.org/10.17943/etku.1065546

Öz

Akran geri bildirimi öğrencilere birçok yönden fayda sağlayabilir. Ancak bu faydalar, geri bildirim tek yönlü ele alındığında asgari miktarda gerçekleşmektedir. Geri bildirim öncesinde, süresince ve sonrasında diyalog ve öğrenciler arasında iş birliği sürdürülebilirse bu faydalar katlanabilir. Bu noktada geri bildirimi veren öğrencinin aktif katılımı önemlidir. Ancak, öğrencilerin akran geri bildirimi verme davranışları literatürde çok fazla ilgi görmemiştir ve diyalog merkezli iş birlikçi uygulamalarda ise hiç incelenmemiştir. Bu boşluktan yola çıkılarak yapılan bu çalışmada çevrimiçi gerçekleştirilen bir akran geri bildirimi etkinliğindeki öğrencilerin geri bildirim verme davranışları incelenmiştir. Bu etkinlik, iş birlikçi geri bildirim teorisi çerçevesinde tasarlanmış olan Sinerji platformunda gerçekleştirilmiştir ve öğrencilerin bu platformla olan etkileşimleri sonucunda oluşan veri bu çalışmada süreç madenciliği uygulanarak incelenmiştir. Elde edilen büyük verinin ön işlemesi iş birlikçi geri bildirim teorisi temel alınarak gerçekleştirilmiştir. Araştırma sonuçlarına göre, yüksek performans grubundaki öğrencilerin davranışları iş birlikçi geri bildirim teorisiyle ile uyumluyken, orta performanslı öğrencilerin davranışlarında teoriden önemli ölçüde sapmalar olmuştur. Öğrenci geri bildirim verme davranışına dair bulgular geri bildirim öncesinde öğrenciler arasındaki diyalog ve ortaklaşa planlamanın önemini göstermektedir. Ayrıca, bu çalışma öğrenme analitikleri alanında teori eksikliğine yönelik tartışmalara, öğrenme analitiklerini teorik bir temele oturtmanın faydalarını göstererek katkı sunmuştur. Çalışma sonunda önemli öneriler paylaşılmıştır.

Destekleyen Kurum

Avrupa Birliği'nin Ufuk 2020 Araştırma ve Yenilik Programı

Proje Numarası

793317

Kaynakça

  • Çevik, Y. D. (2015). Assessor or assessee? Investigating the differential effects of online peer assessment roles in the development of students’ problem-solving skills. Computers in Human Behavior, 52(2015), 250–258. https://doi.org/10.1016/j.chb.2015.05.056
  • Dascalu, M. I., Bodea, C.-N., & Burlacu, A. (2013). Platform for creating collaborative e-learning communities based on automated composition of learning groups. 2013 IEEE 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems, ECBS-EERC 2013, 103–112. https://doi.org/10.1109/ECBS-EERC.2013.21
  • Er, E., Dimitriadis, Y., & Gašević, D. (2020). A collaborative learning approach to dialogic peer feedback: a theoretical framework. Assessment & Evaluation in Higher Education, Online.
  • Er, E., Dimitriadis, Y., & Gašević, D. (2019). Synergy: A Web-Based Tool to Facilitate Dialogic Peer Feedback. 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, 709–713.
  • Espasa, A., Guasch, T., Mayordomo, R. M., & Carless, D. (2018). A Dialogic Feedback Index measuring key aspects of feedback processes in online learning environments. Higher Education Research & Development, 37(3), 499–513. https://doi.org/10.1080/07294360.2018.1430125
  • Ferguson, P. (2011). Student perceptions of quality feedback in teacher education. Assessment & Evaluation in Higher Education, 36(1), 51–62. https://doi.org/10.1080/02602930903197883
  • Filius, R. M., de Kleijn, R. A. M., Uijl, S. G., Prins, F. J., van Rijen, H. V. M., & Grobbee, D. E. (2018). Strengthening dialogic peer feedback aiming for deep learning in SPOCs. Computers & Education, 125, 86–100. https://doi.org/10.1016/j.compedu.2018.06.004
  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
  • Kumaran, S. R. K., Mcdonagh, D. C., & Bailey, B. P. (2017). Increasing quality and involvement in online peer feedback exchange. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 1–18.
  • Mulder, R., Baik, C., Naylor, R., & Pearce, J. (2014). How does student peer review influence perceptions, engagement and academic outcomes? A case study. Assessment & Evaluation in Higher Education, 39(6), 657–677.
  • Nicol, D. (2010). From monologue to dialogue: Improving written feedback processes in mass higher education. Assessment & Evaluation in Higher Education, 35(5), 501–517. https://doi.org/10.1080/02602931003786559
  • Nicol, D., Thomson, A., & Breslin, C. (2013). Rethinking feedback practices in higher education: A peer review perspectivex. Assessment & Evaluation in Higher Education, 39(1), 102–122. https://doi.org/10.1080/02602938.2013.795518
  • Reimann, P. (2016). Connecting learning analytics with learning research: The role of design-based research. Learning: Research and Practice, 2(2), 130–142. https://doi.org/10.1080/23735082.2016.1210198
  • Rogers, T., Dawson, S., & Gasevic, D. (2016). Learning analytics and the imperative for theory driven research. In C. Haythornthwaite, R. Andrews, J. Fransma, & E. Meyers (Eds.), The SAGE Handbook of E-learning Research (2nd ed., Issue March, pp. 232–250).
  • Saint, J., A., W.-W., Gasevic, D., & Pardo, A. (2020). Trace-SRL: A framework for analysis of micro-level processes of self-regulated learning from trace data. IEEE Transactions on Learning Technologies, 13(4), 861–877. https://doi.org/https://doi.org/10.1109/TLT.2020.3027496
  • Steen-Utheim, A., & Wittek, A. L. (2017). Dialogic feedback and potentialities for student learning. Learning, Culture and Social Interaction, 15(December 2016), 18–30. https://doi.org/10.1016/j.lcsi.2017.06.002
  • Topping, K. (1998). Peer assessment between students in colleges and universities. Review of Educational Research, 68(3), 249–276. https://doi.org/10.3102/00346543068003249
  • Wen, M. L., Tsai, C. C., & Chang, C. Y. (2006). Attitudes towards peer assessment: a comparison of the perspectives of pre‐service and in‐service teachers. Innovations in Education and Teaching International, 43(1), 83–92. https://doi.org/10.1080/14703290500467640
  • Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13.

EXPLORING UNIVERSITY STUDENTS' PEER FEEDBACK BEHAVIORS USING PROCESS MINING

Yıl 2022, Cilt: 12 Sayı: 2, 412 - 433, 30.07.2022
https://doi.org/10.17943/etku.1065546

Öz

Peer feedback can benefit students in many ways. However, these benefits are minimal when the feedback is implemented as a one-way activity. These benefits can be multiplied if dialogue and collaboration among students are maintained before, during and after feedback. At this point, the active participation of the student giving the feedback is important. However, students' feedback-giving behaviour has not received much attention in the literature and has never been examined in dialogue-centred collaborative practices. In this study, attending this gap, the feedback behaviours of students in an online peer feedback activity were explored. This activity platform was carried out on the Synergy platform, which was designed within the framework of collaborative peer feedback theory, and data emerging from students’ interactions with this platform were examined by applying process mining. The pre-processing of the obtained big data were carried out on the basis of collaborative feedback theory. According to the results of the research, while the behaviours of the students in the high-performance group were compatible with the collaborative peer feedback theory, there were significant deviations from the theory in the behaviours of the middle-performing students. Findings indicate the importance of dialogue and collective planning between students prior to feedback. In addition, this study contributed to the debate on the lack of theory in the field of learning analytics by showing the benefits of grounding learning analytics in theory. Important recommendations were shared at the end of the study.

Proje Numarası

793317

Kaynakça

  • Çevik, Y. D. (2015). Assessor or assessee? Investigating the differential effects of online peer assessment roles in the development of students’ problem-solving skills. Computers in Human Behavior, 52(2015), 250–258. https://doi.org/10.1016/j.chb.2015.05.056
  • Dascalu, M. I., Bodea, C.-N., & Burlacu, A. (2013). Platform for creating collaborative e-learning communities based on automated composition of learning groups. 2013 IEEE 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems, ECBS-EERC 2013, 103–112. https://doi.org/10.1109/ECBS-EERC.2013.21
  • Er, E., Dimitriadis, Y., & Gašević, D. (2020). A collaborative learning approach to dialogic peer feedback: a theoretical framework. Assessment & Evaluation in Higher Education, Online.
  • Er, E., Dimitriadis, Y., & Gašević, D. (2019). Synergy: A Web-Based Tool to Facilitate Dialogic Peer Feedback. 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, 709–713.
  • Espasa, A., Guasch, T., Mayordomo, R. M., & Carless, D. (2018). A Dialogic Feedback Index measuring key aspects of feedback processes in online learning environments. Higher Education Research & Development, 37(3), 499–513. https://doi.org/10.1080/07294360.2018.1430125
  • Ferguson, P. (2011). Student perceptions of quality feedback in teacher education. Assessment & Evaluation in Higher Education, 36(1), 51–62. https://doi.org/10.1080/02602930903197883
  • Filius, R. M., de Kleijn, R. A. M., Uijl, S. G., Prins, F. J., van Rijen, H. V. M., & Grobbee, D. E. (2018). Strengthening dialogic peer feedback aiming for deep learning in SPOCs. Computers & Education, 125, 86–100. https://doi.org/10.1016/j.compedu.2018.06.004
  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
  • Kumaran, S. R. K., Mcdonagh, D. C., & Bailey, B. P. (2017). Increasing quality and involvement in online peer feedback exchange. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW), 1–18.
  • Mulder, R., Baik, C., Naylor, R., & Pearce, J. (2014). How does student peer review influence perceptions, engagement and academic outcomes? A case study. Assessment & Evaluation in Higher Education, 39(6), 657–677.
  • Nicol, D. (2010). From monologue to dialogue: Improving written feedback processes in mass higher education. Assessment & Evaluation in Higher Education, 35(5), 501–517. https://doi.org/10.1080/02602931003786559
  • Nicol, D., Thomson, A., & Breslin, C. (2013). Rethinking feedback practices in higher education: A peer review perspectivex. Assessment & Evaluation in Higher Education, 39(1), 102–122. https://doi.org/10.1080/02602938.2013.795518
  • Reimann, P. (2016). Connecting learning analytics with learning research: The role of design-based research. Learning: Research and Practice, 2(2), 130–142. https://doi.org/10.1080/23735082.2016.1210198
  • Rogers, T., Dawson, S., & Gasevic, D. (2016). Learning analytics and the imperative for theory driven research. In C. Haythornthwaite, R. Andrews, J. Fransma, & E. Meyers (Eds.), The SAGE Handbook of E-learning Research (2nd ed., Issue March, pp. 232–250).
  • Saint, J., A., W.-W., Gasevic, D., & Pardo, A. (2020). Trace-SRL: A framework for analysis of micro-level processes of self-regulated learning from trace data. IEEE Transactions on Learning Technologies, 13(4), 861–877. https://doi.org/https://doi.org/10.1109/TLT.2020.3027496
  • Steen-Utheim, A., & Wittek, A. L. (2017). Dialogic feedback and potentialities for student learning. Learning, Culture and Social Interaction, 15(December 2016), 18–30. https://doi.org/10.1016/j.lcsi.2017.06.002
  • Topping, K. (1998). Peer assessment between students in colleges and universities. Review of Educational Research, 68(3), 249–276. https://doi.org/10.3102/00346543068003249
  • Wen, M. L., Tsai, C. C., & Chang, C. Y. (2006). Attitudes towards peer assessment: a comparison of the perspectives of pre‐service and in‐service teachers. Innovations in Education and Teaching International, 43(1), 83–92. https://doi.org/10.1080/14703290500467640
  • Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Alan Eğitimleri
Bölüm Makaleler
Yazarlar

Erkan Er 0000-0002-9624-4055

Proje Numarası 793317
Erken Görünüm Tarihi 30 Temmuz 2022
Yayımlanma Tarihi 30 Temmuz 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 12 Sayı: 2

Kaynak Göster

APA Er, E. (2022). ÜNİVERSİTE ÖĞRENCİLERİNİN AKRAN GERİ BİLDİRİMİ VERME DAVRANIŞLARININ SÜREÇ MADENCİLİĞİ İLE İNCELENMESİ. Eğitim Teknolojisi Kuram Ve Uygulama, 12(2), 412-433. https://doi.org/10.17943/etku.1065546