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

Yıl 2025, Cilt: 27 Sayı: 2, 267 - 277, 30.06.2025
https://doi.org/10.17556/erziefd.1641065

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695. https://doi.org/10.1080/13562517.2013.827653
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • Garrison, D. R. (2007). Online Community of Inquiry Review: Social, Cognitive, and Teaching Presence Issues. Journal of Asynchronous Learning Networks, 11(1), 61-72.
  • 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
  • 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
  • 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
  • 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
  • Han, J., Kamber, M., & Pei, J. (2012). Cluster Analysis: Basic Concepts and Methods. Içinde Data Mining Concepts and Techniques (s. 443). Elsevier Inc.
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  • 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
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Çevrimiçi Senkron Uzaktan Eğitimde Etkileşim ve Başarı: Daha Fazlası Her Zaman Daha İyi Mi?

Yıl 2025, Cilt: 27 Sayı: 2, 267 - 277, 30.06.2025
https://doi.org/10.17556/erziefd.1641065

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695. https://doi.org/10.1080/13562517.2013.827653
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • Garrison, D. R. (2007). Online Community of Inquiry Review: Social, Cognitive, and Teaching Presence Issues. Journal of Asynchronous Learning Networks, 11(1), 61-72.
  • 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
  • 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
  • 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
  • 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
  • Han, J., Kamber, M., & Pei, J. (2012). Cluster Analysis: Basic Concepts and Methods. Içinde Data Mining Concepts and Techniques (s. 443). Elsevier Inc.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • Pahl, C., & Donnellan, D. (2002). Data Mining Technology for the Evaluation of Web-based Teaching and Learning Systems. ELearn: World Conference on EdTech, 6.
  • Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards Networked Learning Analytics – A concept and a tool. Networked Learning, 8.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
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  • 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
  • Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30. https://eric.ed.gov/?id=EJ950794
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. (6. ed). Pearson.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yükseköğretim Çalışmaları (Diğer), Eğitim Teknolojisi ve Bilgi İşlem, Öğrenme Analitiği
Bölüm Araştırma Makalesi
Yazarlar

Adem Mehmet Yıldız 0000-0002-4033-0122

Gönderilme Tarihi 17 Şubat 2025
Kabul Tarihi 27 Mart 2025
Erken Görünüm Tarihi 19 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 27 Sayı: 2

Kaynak Göster

APA Yıldız, A. M. (2025). Interaction and Achievement in Online Synchronous Distance Education: Is More Always Better? Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 27(2), 267-277. https://doi.org/10.17556/erziefd.1641065