Research Article
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Year 2021, Volume: 22 Issue: 1, 19 - 32, 31.12.2020
https://doi.org/10.17718/tojde.849872

Abstract

References

  • Al-Rahmi, W., Aldraiweesh, A., Yahaya, N., Kamin, Y. Bin, & Zeki, A. M. (2019). Massive open online courses (MOOCs): Data on higher education. Data in Brief, 22, 118–125.
  • Alhabeeb, A., & Rowley, J. (2018). E-learning critical success factors: Comparing perspectives from academic staff and students. Computers & Education, 127, 1–12.
  • Anshari, M., Alas, Y., Hamid, M. H. S. A., & Smith, M. (2016). Learning management system 2.0: Higher education. In Handbook of research on engaging digital natives in higher education settings (pp. 265–279). IGI Global.
  • Arnab, C. (2018). The Absence of Longer Texts in Literature Classes in Some Open and Distance Education Courses in India: Learning Outcomes. International Linguistics Research, 1(1), p95–p95.
  • Battalio, J. (2009). Success in distance education: Do learning styles and multiple formats matter? The Amer. Jrnl. of Distance Education, 23(2), 71–87.
  • Bilgic, H. G., & Tuzun, H. (2015). Yuksekogretim kurumlari web tabanli uzaktan egitim programlarinda yasanan sorunlar. Acikogretim Uygulamalari ve Arastirmalari Dergisi, 1(3), 26–50.
  • Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., … Goksel-Canbek, N. (2015). Trends in distance education research: A content analysis of journals 2009-2013. International Review of Research in Open and Distributed Learning, 16(1), 330–363.

MODEL PROPOSAL ON THE DETERMINATION OF STUDENT ATTENDANCE IN DISTANCE EDUCATION WITH FACE RECOGNITION TECHNOLOGY

Year 2021, Volume: 22 Issue: 1, 19 - 32, 31.12.2020
https://doi.org/10.17718/tojde.849872

Abstract

The aim of this study is to present a model proposal on determining the student participation rate in synchronous courses given in Learning Management Systems (LMS). Especially in situations where equal opportunities cannot be provided or opportunities are limited, distance education provides benefits for learning anytime and anywhere (ubiquitous learning) with the support of educational technologies. When the literature is examined, thanks to distance education; It is seen that it offers a very advantageous teaching environment in terms of location, time, convenience in accessing the resources needed and cost-benefit. However, when the literature is analyzed, it is found that there is a problem in determining the participation levels and rates of students in the Learning Management Systems used in distance education. Students access the activity or course in LMS using text-based user information and passwords. Unfortunately, it is not possible to determine with the current LMS whether the participant is the real responsible person or he/she is actively/synchronously following the course. In this context, a design model has been presented using face recognition algorithms to determine attendance in distance education, to ensure more active participation and to increase success indirectly. In the proposed model, tests were made using special filters for image processing, and in cases where the number of samples was increased, more than 80% accuracy was provided. The proposed design model was developed in Visual Studio.Net platform and coded on C# programming language. SQL server is used as database management system and EmguCV library is used for the image processing stages.

References

  • Al-Rahmi, W., Aldraiweesh, A., Yahaya, N., Kamin, Y. Bin, & Zeki, A. M. (2019). Massive open online courses (MOOCs): Data on higher education. Data in Brief, 22, 118–125.
  • Alhabeeb, A., & Rowley, J. (2018). E-learning critical success factors: Comparing perspectives from academic staff and students. Computers & Education, 127, 1–12.
  • Anshari, M., Alas, Y., Hamid, M. H. S. A., & Smith, M. (2016). Learning management system 2.0: Higher education. In Handbook of research on engaging digital natives in higher education settings (pp. 265–279). IGI Global.
  • Arnab, C. (2018). The Absence of Longer Texts in Literature Classes in Some Open and Distance Education Courses in India: Learning Outcomes. International Linguistics Research, 1(1), p95–p95.
  • Battalio, J. (2009). Success in distance education: Do learning styles and multiple formats matter? The Amer. Jrnl. of Distance Education, 23(2), 71–87.
  • Bilgic, H. G., & Tuzun, H. (2015). Yuksekogretim kurumlari web tabanli uzaktan egitim programlarinda yasanan sorunlar. Acikogretim Uygulamalari ve Arastirmalari Dergisi, 1(3), 26–50.
  • Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., … Goksel-Canbek, N. (2015). Trends in distance education research: A content analysis of journals 2009-2013. International Review of Research in Open and Distributed Learning, 16(1), 330–363.
There are 7 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Durmus Ozdemır

Mehmet Emin Ugur

Publication Date December 31, 2020
Submission Date March 3, 2020
Published in Issue Year 2021 Volume: 22 Issue: 1

Cite

APA Ozdemır, D., & Ugur, M. E. (2020). MODEL PROPOSAL ON THE DETERMINATION OF STUDENT ATTENDANCE IN DISTANCE EDUCATION WITH FACE RECOGNITION TECHNOLOGY. Turkish Online Journal of Distance Education, 22(1), 19-32. https://doi.org/10.17718/tojde.849872