Dergiler / Erzincan Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Cilt: 27 Sayı: 2

Çevrimiçi Senkron Uzaktan Eğitimde Etkileşim ve Başarı: Daha Fazlası Her Zaman Daha İyi Mi?

Interaction and Achievement in Online Synchronous Distance Education: Is More Always Better?

Adem Mehmet Yıldız(KIRKLARELİ ÜNİVERSİTESİ)0000-0002-4033-0122
Sayfa
267–277
Yayın Tarihi
2025-06-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuçlar, düşük etkileşim grubundaki öğrencilerin akademik başarılarının anlamlı ölçüde daha düşük olduğunu göstermektedir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:COVID-19 pandemisi, yükseköğretimde uzaktan eğitimin yaygınlaşmasına neden olmuş ve çevrimiçi senkron derslerde öğrencilerin sosyal bulunuşluğu ile etkileşim durumları önemli bir araştırma alanı hâline gelmiştir.
📊
Temel Bulgular & Veri:Bu çalışma, 2020-2021 akademik yılında bir devlet üniversitesinde Microsoft Teams platformunda yürütülen çevrimiçi senkron derslere katılım sayısı, kamera ve mikrofon kullanım sürelerine ait öğrenme analitiklerini incelemektedir. 2020-2021 güz döneminde 16.304, bahar döneminde ise 14.042 öğrencinin verileri analiz edilmiştir.
💡
Sonuç & Katkı:Sonuçlar, düşük etkileşim grubundaki öğrencilerin akademik başarılarının anlamlı ölçüde daha düşük olduğunu göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2020-2021 Verileri
⚙️Değişkenler:Uzaktan Eğitim · Etkileşim · Öğrenme Analitikleri

Özet

COVID-19 pandemisi, yükseköğretimde uzaktan eğitimin yaygınlaşmasına neden olmuş ve çevrimiçi senkron derslerde öğrencilerin sosyal bulunuşluğu ile etkileşim durumları önemli bir araştırma alanı hâline gelmiştir. Bu çalışma, 2020-2021 akademik yılında bir devlet üniversitesinde Microsoft Teams platformunda yürütülen çevrimiçi senkron derslere katılım sayısı, kamera ve mikrofon kullanım sürelerine ait öğrenme analitiklerini incelemektedir. 2020-2021 güz döneminde 16.304, bahar döneminde ise 14.042 öğrencinin verileri analiz edilmiştir. Öğrenciler, K-Ortalamalar algoritması ile yüksek, orta ve düşük etkileşim düzeylerine göre kümelendirilmiştir. Sonuçlar, düşük etkileşim grubundaki öğrencilerin akademik başarılarının anlamlı ölçüde daha düşük olduğunu göstermektedir. Güz döneminde orta düzeyde etkileşim gösteren öğrenciler, yüksek etkileşim grubundakilerden daha başarılı bulunurken, bahar döneminde orta ve yüksek etkileşim grupları arasında anlamlı bir fark görülmemiştir. Lisans öğrencileri arasında orta düzey etkileşim gösterenler daha başarılı olurken, ön lisans öğrencileri arasında en yüksek başarıya sahip grup, yüksek etkileşim gösterenler olmuştur. Bu farklılık, lisans öğrencilerinin bağımsız çalışma alışkanlıklarına, ön lisans öğrencilerinin ise öğretmen yönlendirmesine daha fazla ihtiyaç duymasına bağlanabilir. Sonuçlar, yüksek etkileşim ve sosyal bulunuşluğun her zaman akademik başarıyı artırmadığını, belirli öğrenci gruplarında orta düzey etkileşimin daha verimli öğrenme çıktıları sağlayabileceğini göstermektedir. Bu nedenle, etkileşim stratejilerinin öğrenci gruplarının ihtiyaçlarına göre uyarlanması gerekmektedir.

Abstract

The COVID-19 pandemic accelerated the adoption of distance learning in higher education, making students’ social presence and interaction in online synchronous classes an important research focus. This study analyses learning analytics on participation, camera use, and microphone use during online classes via Microsoft Teams at a public university in the 2020–2021 academic year. Data from 16,304 students in the autumn term and 14,042 in the spring term were examined. Using the K-Means algorithm, students were clustered into high, medium, and low interaction groups. The results show that students in the low interaction group had significantly lower academic performance. In the autumn term, medium interaction students outperformed those with high interaction, while in the spring term, no significant difference emerged between the medium and high groups. Among undergraduates, medium interaction students were more successful, whereas among associate degree students, those in the high interaction group achieved the best results. This may be linked to undergraduates’ stronger independent study habits, while associate degree students tend to rely more on instructor guidance. The findings suggest that high interaction and social presence do not always enhance academic success, and interaction strategies should be tailored to the specific needs of student groups.

🔖

Bu Makaleye Atıf Yap

Yıldız, A. M. (2025). Çevrimiçi Senkron Uzaktan Eğitimde Etkileşim ve Başarı: Daha Fazlası Her Zaman Daha İyi Mi?. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, Cilt: 27 Sayı: 2, 267–277. https://doi.org/10.17556/erziefd.1641065

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (64)

  1. Al-Samarraie, H. (2019). A Scoping Review of Videoconferencing Systems in Higher Education: Learning Paradigms, Opportunities, and Challenges. The International Review of Research in Open and Distributed Learning, 20(3). https://doi.org/10.19173/IRRODL.V20I4.4037
  2. Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398. https://doi.org/10.1007/s11423-012-9235-8
  3. Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learning, 20(2), 13-29. https://eric.ed.gov/?id=EJ1105911
  4. Battaglia, O. R., Di Paola, B., & Fazio, C. (2017). A quantitative analysis of Educational Data through the Comparison between Hierarchical and Not-Hierarchical Clustering. EURASIA Journal of Mathematics, Science and Technology Education, 13(8). https://doi.org/10.12973/eurasia.2017.00943a
  5. Berkhin, P. (2006). A Survey of Clustering Data Mining Techniques. Içinde J. Kogan, C. Nicholas, & M. Teboulle (Ed.), Grouping Multidimensional Data (25-71). Springer. https://doi.org/https://doi.org/10.1007/3-540-28349-8_2
  6. Bharara, S., Sabitha, S., & Bansal, A. (2018). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 23(2), 957-984. https://doi.org/10.1007/s10639-017-9645-7
  7. Bogarín, A., Romero, C., Cerezo, R., & Sánchez-Santillán, M. (2014). Clustering for improving Educational Process Mining. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 11-15. https://doi.org/https://doi.org/10.1145/2567574.256760
  8. Bonk, C. J. (2020). Pandemic ponderings, 30 years to today: synchronous signals, saviors, or survivors? Distance Education, 41(4), 589-599. https://doi.org/10.1080/01587919.2020.1821610
  9. Burnham, B. R., & Walden, B. (1997). Interactions in distance education: A report from the other side. Annual Adult Education Research Conference Proceedings, 49-54. https://newprairiepress.org/aerc/1997/papers/9
  10. Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students’ LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, 42-54. https://doi.org/10.1016/J.COMPEDU.2016.02.006
  11. Chen, J., Huang, K., Wang, F., & Wang, H. (2009). E-learning Behavior Analysis Based on Fuzzy Clustering. 2009 Third International Conference on Genetic and Evolutionary Computing, 863-866. https://doi.org/10.1109/WGEC.2009.214
  12. Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695. https://doi.org/10.1080/13562517.2013.827653
  13. Creswell, J. W., & Creswell, J. D. (2022). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition. Journal of Electronic Resources in Medical Libraries, 19(1-2), 54-55. https://doi.org/10.1080/15424065.2022.2046231
  14. DeFreitas, K., & Bernard, M. (2015). Comparative Performance Analysis of Clustering Techniques in Educational Data Mining. International journal on computer science & Information systems, 10(2), 14.
  15. Dessì, D., Fenu, G., Marras, M., & Reforgiato Recupero, D. (2019). Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections. Computers in Human Behavior, 92, 468-477. https://doi.org/10.1016/j.chb.2018.03.004
  16. Elton, L. (1996). Strategies to enhance student motivation: A conceptual analysis. Studies in Higher Education, 21(1), 57-68. https://doi.org/10.1080/03075079612331381457
  17. Eryilmaz, M. (2019). Sanal Öğrenme Ortamlarındaki Öğrenci Davranışlarının Kümeleme Yöntemi ile Analiz Edilmesi. Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 16(1), 725-743. https://doi.org/10.23891/efdyyu.2019.139
  18. Fang, Y., Shubeck, K., Lippert, A., Cheng, Q., Shi, G., Feng, S., Chen, S., Cai, Z., Pavlik, P., Frijters, J., Greenberg, D., & Graesser, A. (2018). Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System. International Conference on Educational Data Mining, 7.
  19. Garrison, D. R. (2007). Online Community of Inquiry Review: Social, Cognitive, and Teaching Presence Issues. Journal of Asynchronous Learning Networks, 11(1), 61-72.
  20. Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education, 2(2-3), 87-105. https://doi.org/10.1016/S1096-7516(00)00016-6
  21. Ghorbani, F., & Montazer, G. A. (2012). Learners grouping improvement in e-learning environment using fuzzy inspired PSO method. 6Th National And 3Rd International Conference Of E -Learning And E -Teaching, 65-70. https://doi.org/10.1109/ICELET.2012.6333367
  22. Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285-292. https://doi.org/10.1016/J.CHB.2012.09.005
  23. Gillies, D. (2008). Student perspectives on videoconferencing in teacher education at a distance. Distance Education, 29(1), 107-118. https://doi.org/10.1080/01587910802004878
  24. Han, J., Kamber, M., & Pei, J. (2012). Cluster Analysis: Basic Concepts and Methods. Içinde Data Mining Concepts and Techniques (s. 443). Elsevier Inc.
  25. Händel, M., Bedenlier, S., Kopp, B., Gläser-Zikuda, M., Kammerl, R., & Ziegler, A. (2022). The webcam and student engagement in synchronous online learning: visually or verbally? Education and Information Technologies, 27(7), 10405-10428. https://doi.org/10.1007/S10639-022-11050-3/FIGURES/1
  26. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Applied Statistics, 28(1), 100. https://doi.org/10.2307/2346830
  27. Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/J.COMPEDU.2019.103724
  28. Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206-230. https://doi.org/10.1080/10494820.2019.1636086
  29. Joksimović, S., Gašević, D., Kovanović, V., Riecke, B. E., & Hatala, M. (2015). Social presence in online discussions as a process predictor of academic performance. Journal of Computer Assisted Learning, 31(6), 638-654. https://doi.org/10.1111/JCAL.12107
  30. Khalil, M., & Ebner, M. (2016). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 1-19. https://doi.org/10.1007/S12528-016-9126-9
  31. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Computers in Human Behavior, 19(3), 335-353. https://doi.org/10.1016/S0747-5632(02)00057-2
  32. Kreijns, K., Kirschner, P. A., & Vermeulen, M. (2013). Social aspects of CSCL environments: A research framework. Educational Psychologist, 48(4), 229-242. https://doi.org/10.1080/00461520.2012.750225
  33. Kreijns, K., Xu, K., & Weidlich, J. (2022). Social Presence: Conceptualization and Measurement. Educational Psychology Review, 34(1), 139-170. https://doi.org/10.1007/S10648-021-09623-8/TABLES/2
  34. Lowenthal, P. R., & Snelson, C. (2017). In search of a better understanding of social presence: an investigation into how researchers define social presence. Distance Education, 38(2), 141-159. https://doi.org/10.1080/01587919.2017.1324727
  35. Luis Cavalcanti Ramos, J., e Silva, R., Carlos Sedraz Silva, J., Lins Rodrigues, R., & Sandro Gomes, A. (2016). A Comparative Study between Clustering Methods in Educational Data Mining. IEEE Latin America Transactions, 14(8), 3755-3761. https://doi.org/10.1109/TLA.2016.7786360
  36. Mirza, E., & Samen, K. (2022). Web Kamerayı Açmak ya da Açmamak: Uzaktan Senkron Eğitimde Derse Giren Lisans Öğrencileri Web Kameraya Nasıl Bir Anlam Yüklüyorlar? Uluslararası Medya ve İletişim Araştırmaları Hakemli Dergisi, 5(2), 206-235. https://doi.org/10.33464/MEDIAJ.1130565
  37. Moore, M. G. (1991). Editorial: Distance Education Theory. American Journal of Distance Education, 5(3), 1-6. https://doi.org/10.1080/08923649109526758/ASSET//CMS/ASSET/F4D64AE0-1D1D-4FF7-A974-9931E3498F02/08923649109526758.FP.PNG
  38. Moore, M. G. (2013). The Theory of Transactional Distance. Içinde Handbook of Distance Education (C. 14, Sayı 304, ss. 66-85). Routledge. https://doi.org/10.4324/9780203803738-10 Moore, M. G., & Kearsley, G. (2012). Distance Education: A Systems View of Online Learning. Wadsworth Cengage Learning.
  39. Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2020). Student Engagement Level in an e-Learning Environment: Clustering Using K-means. American Journal of Distance Education, 34(2), 137-156. https://doi.org/10.1080/08923647.2020.1696140
  40. Orhan Göksün, D. (2020). Predictors of perceived learning in a distance learning environment from the perspective of SIPS model. International Journal of Human–Computer Interaction, 36(10), 941-952. https://doi.org/10.1080/10447318.2019.1700643
  41. Oviedo, B., Moral, S., & Puris, A. (2016). A hierarchical clustering method: Applications to educational data. Intelligent Data Analysis, 20(4), 933-951. https://doi.org/10.3233/IDA-160839
  42. Pahl, C., & Donnellan, D. (2002). Data Mining Technology for the Evaluation of Web-based Teaching and Learning Systems. ELearn: World Conference on EdTech, 6.
  43. Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards Networked Learning Analytics – A concept and a tool. Networked Learning, 8.
  44. Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402-417. https://doi.org/10.1016/J.CHB.2017.02.001
  45. Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), e0210236. https://doi.org/10.1371/journal.pone.0210236
  46. Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472. https://doi.org/10.1016/j.compedu.2013.06.009
  47. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. https://doi.org/10.1016/j.eswa.2006.04.005
  48. Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. https://doi.org/10.1109/TSMCC.2010.2053532
  49. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/WIDM.1355
  50. Roski, M., Sebastian, R., Ewerth, R., Hoppe, A., & Nehring, A. (2024). Learning analytics and the Universal Design for Learning (UDL): A clustering approach. Computers & Education, 214, 105028. https://doi.org/10.1016/J.COMPEDU.2024.105028
  51. Schenker, J. D., & Rumrill, Jr., P. D. (2004). Causal-comparative research designs. Journal of Vocational Rehabilitation, 21(3), 117-121.
  52. Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  53. Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30. https://eric.ed.gov/?id=EJ950794
  54. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. (6. ed). Pearson.
  55. Tie, Z., Jin, R., Zhuang, H., & Wang, Z. (2010). The Research on Teaching Method of Basics Course of Computer based on Cluster Analysis. 2010 10th IEEE International Conference on Computer and Information Technology, 2001-2004. https://doi.org/10.1109/CIT.2010.338
  56. Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., & Karakos, A. (2012). A Clustering Methodology of Web Log Data for Learning Management Systems. Educational Technology & Society, 15(2), 154-167. https://eric.ed.gov/?id=EJ988458
  57. Weidlich, J., & Bastiaens, T. J. (2017). Explaining social presence and the quality of online learning with the SIPS model. Computers in Human Behavior, 72, 479-487. https://doi.org/10.1016/j.chb.2017.03.016
  58. Weidlich, J., Göksün, D. O., & Kreijns, K. (2023). Extending social presence theory: Social presence divergence and interaction integration in online distance learning. Journal of Computing in Higher Education, 35(3), 391-412. https://doi.org/10.1007/s12528-022-09325-2
  59. Wut, T. M., & Xu, J. (2021). Person-to-person interactions in online classroom settings under the impact of COVID-19: a social presence theory perspective. Asia Pacific Education Review, 22(3), 371-383. https://doi.org/10.1007/s12564-021-09673-1
  60. YÖK. (2020). Koronavirüs (Covid-19) Bilgilendirme Notu: 1. https://covid19.yok.gov.tr/Documents/alinan-kararlar/02-coronavirus-bilgilendirme-notu-1.pdf Accessed on 03/02/2025.
  61. Yudhanegara, M. R., & Lestari, K. E. (2019). Clustering for multi-dimensional data set: a case study on educational data. Journal of Physics: Conference Series, 1280(4), 42025. https://doi.org/10.1088/1742-6596/1280/4/042025
  62. Zaiane, O. R., & Luo, J. (2001). Towards evaluating learners’ behaviour in a Web-based distance learning environment. Proceedings IEEE International Conference on Advanced Learning Technologies, 357-360. https://doi.org/10.1109/ICALT.2001.943944
  63. Khalil, M., & Ebner, M. (2016). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 1-19. https://doi.org/10.1007/S12528-016-9126-9
  64. Schenker, J. D., & Rumrill, Jr., P. D. (2004). Causal-comparative research designs. Journal of Vocational Rehabilitation, 21(3), 117-121.