Çizge Kuramının Sosyodinamik Kurama Adaptasyonu: Öğreşme Kavramı

Abstract

Bu çalışma, eğitimde etkileşim biçimlerine örnekler üzerinden odaklanmayı amaçlamaktadır. Örnekte öğrenciler, aileler, öğretmenler, okul müdürleri ve akademisyenler arasındaki olası doğrusal, yüzeysel ve üç boyutlu etkileşim modelleri mümkün olduğunca ele alınmıştır. Analiz edilen örnek, öğrencileri sınavla kabul eden bir lisedir. Bu lisede bazı öğrenciler dersten sonra evlerine giderken bazıları pansiyonlarda kalıyor. Etkileşim biçimleri, ilgili ilkeler ve gözlemlenen sonuçları ele alan bu çalışma, 'öğreşme' adı verilen yeni ama önemli bir kavramı ortaya çıkarmaktadır. Öğretmenin ve öğrenmenin aslında tek bir kavram olduğu fikri, bazı eğitimsel varsayımları ve ilkeleri de beraberinde getirir. Eğitim bileşenlerinin 'öğreşme' kavramı üzerinden etkileşim yollarının yer aldığı bu çalışmanın tüm bileşenlere katkı sağlayacağı düşünülmektedir. Bu durum araştırması/etnografik çalışma, esasen bileşenler ve bileşenler arası etkileşimi araştırmak için kullanılan çizge kuramı ile toplumdaki her bir bileşenin toplumun gelişiminin bir parçası olduğunu vurgulayan sosyodinamik kuramı birlikte ele almayı hedeflemektedir.

Keywords

• Öğreşme
• Çizge Kuramı
• Sosyodinamik Kuram
• Bilim Eğitimi

Adaptation of Graph Theory to Sociodynamic Theory: The Concept of Tearning

Abstract

This study aims to focus on the forms of interaction in education through examples. Possible linear, surface and three dimensional models of interactions among students, families, teachers, school principals and academics are handled on the example as far as possible. The sample analyzed is a high school that accepts students by exam. In this high school, some students go home after class, while others stay in the dormitories of the school. This study, which deals with forms of interaction, related principles and observed results, opens up a new but important concept called 'tearning'. The idea that teaching and learning are actually one concept brings along some educational assumptions and principles. It is thought that this study, in which the ways of interaction of the components of education through the concept of 'tearning', will contribute to all components. This case / ethnographic study aims to deal with graph theory, which is mainly used to investigate the components and the interaction between components, and sociodynamic theory, which emphasizes that each component in society is a part of the development of society.

Keywords

• Tearning
• Graph Theory
• Sociodynamic Theory
• Science Education

References

1. Adams, G. R., & Cohen, A. S. (1974). Children’s physical and interpersonal characteristics that effect student-teacher interactions. The Journal of Experimental Education, 43(1), 1-5.
2. Bloch, J. (2002). Student/teacher interaction via email: The social context of Internet discourse. Journal of Second Language Writing, 11(2), 117-134.
3. Bülbül, M. Ş. (2015). Öğreşme Kavramı ve Termodinamik. Eğitişim Dergisi, 12 (46).
4. Bülbül, M. Ş., & Karaman, C. (2014). Kaosun Keşfedilmesi ve Sorgulanması: Makaraların Hareketi. Journal of Inquiry Based Activities, 4, 18-23.
5. Chai, A., Le, J. P., Lee, A. S., & Lo, S. M. (2019). Applying Graph Theory to Examine the Dynamics of Student Discussions in Small-Group Learning. CBE—Life Sciences Education, 18(2), ar29.
6. Edgerson, D. E., & Kritsonis, W. A. (2006). Analysis of the influence of principal-teacher relationships on student academic achievement: A national focus. National Journal for Publishing and Mentoring Doctoral Student Research, 1(1), 1-5.
7. Farland-Smith, D. (2009). How does culture shape students’ perceptions of scientists? Cross-national comparative study of American and Chinese elementary students. Journal of Elementary Science Education, 21(4), 23-42.
8. Hamman, D., Fives, H., & Olivarez, A. (2007). Efficacy and pedagogical interaction in cooperating and student teacher dyads. The Journal of Classroom Interaction, 55-63.

9. Hudson, J. L., & Rapee, R. M. (2001). Parent–child interactions and anxiety disorders: An observational study. Behaviour research and therapy, 39(12), 1411-1427.
10. Lang, Q. C., Wong, A. F., & Fraser, B. J. (2005). Student perceptions of chemistry laboratory learning environments, student–teacher interactions and attitudes in secondary school gifted education classes in Singapore. Research in Science Education, 35(2-3), 299-321.
11. Lindblom-Ylänne, S., Pihlajamäki, H., & Kotkas, T. (2003). What makes a student group successful? Student-student and student-teacher interaction in a problem-based learning environment. Learning environments research, 6(1), 59-76.
12. Mesquita, B., & Boiger, M. (2014). Emotions in context: A sociodynamic model of emotions. Emotion Review, 6(4), 298-302.
13. Murphey, T., Falout, J., Fukuda, T., & Fukada, Y. (2014). Socio-dynamic motivating through idealizing classmates. System, 45, 242-253.
14. Niman, J. (1975). Graph theory in the elementary school. Educational studies in mathematics, 6(3), 351-373.
15. Peavy, R. V. (1997). SocioDynamic counselling. Victoria, BC. Trafford.
16. Peavy, R. V. (2000). A sociodynamic perspective for counselling. Australian Journal of Career Development, 9(1), 17-24.
17. She, H. C. (2000). The interplay of a biology teacher's beliefs, teaching practices and gender-based student-teacher classroom interaction. Educational Research, 42(1), 100-111.
18. Szeto, E., & Cheng, A. Y. N. (2018). Principal–teacher interactions and teacher leadership development: Beginning teachers’ perspectives. International journal of leadership in education, 21(3), 363-379.
19. Tatsuoka, M. M. (1986). Graph theory and its applications in educational research: A review and integration. Review of Educational Research, 56(3), 291-329.
20. Van Steen, M. (2010). Graph theory and complex networks. An introduction, 144.
21. Weidlich, W. (2005). Thirty years of sociodynamics.: An integrated strategy of modelling in the social sciences: applications to migration and urban evolution. Chaos, Solitons & Fractals, 24(1), 45-56.
22. Zhang, Y., Xiang, T., Hospedales, T. M., & Lu, H. (2018). Deep mutual learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4320-4328).
23. Zhan, K., & Niu, C. (2020). Mutual teaching for graph convolutional networks. Future Generation Computer Systems, 115, 837-843.