Öğretim sürecini tasarlayarak öğrenenleri anlama ve tekno-pedagojik yeterliğin etkili öğrenme ortamları üzerinde etkisi

Year 2020, , 246 - 259, 31.07.2020

Ragıp Terzi

https://doi.org/10.19128/turje.746953

Abstract

Bu çalışmanın amacı, öğretmen adaylarının öğretmenlik mesleğine başlamadan önce öğretmeye hazır olmalarını anlamaktır. Özellikle, “öğretim sürecini tasarlamak” aracılığıyla “öğreneni anlama” ve “tekno-pedagojik yeterliğin” “etkili öğrenme ortamları oluşturma” üzerindeki nedensel etkileri araştırılmıştır. Bu değişkenlerin etkili öğrenme ortamları üzerindeki etkilerini yordamak için yapısal eşitlik modellemesi uygulanmıştır. Türkiye’deki bir devlet üniversitesinde 2019-2020 bahar döneminde eğitim gören 314 öğretmen adayı ile kesitsel ölçek tasarımı kullanılmıştır. Bu çalışmanın amacı doğrultusunda, Öğretmenliğe Hazır Olma ölçeği kullanılarak veriler toplanmış olup, ölçeğin geçerlik ve güvenirlik özellikleri incelenmiştir. Sonuçlar öğreneni anlama’nın etkili öğrenme ortamları oluşturma üzerinde hem doğrudan hem de dolaylı etkileri olduğunu tavsiye etmektedir. Yani, öğretmen adaylarının öğreneni ne kadar iyi anlarsa, öğretim sürecini o kadar uygun bir şekilde tasarlayabilecek ve nihayetinde etkili bir öğrenme ortamı oluşturabilecektir. Ancak, tekno-pedagojik yeterliğin etkili öğrenme ortamları oluşturma üzerinde sadece dolaylı etkileri olduğu saptanmıştır. Bu bulgu, tekno-pedagojide daha yüksek yetkinliğe sahip öğretmen adaylarının ÖST tarafından daha etkili bir öğrenme ortamı oluşturabileceklerini göstermektedir.

Keywords

Eğitimde ölçme ve değerlendirme, Etkili öğrenme ortamı, Öğreneni anlama, Öğretim süreci tasarımı, Tekno-pedagojik yeterlik, Yapısal eşitlik modellemesi

The impact of understanding learners and techno-pedagogical competency on effective learning environments by designing the instructional process

Abstract

This study aims to understand prospective teachers’ (PTs) preparedness to teach before they actually start working in the profession. In particular, the causal effects of “understanding the learner” and “techno-pedagogical competency” on “forming effective learning environments” by “designing the instructional process” were investigated. Structural equation modeling was carried out to estimate the effects of these variables on effective learning environments. A cross-sectional survey design was used with 314 PTs who were studying in a state university in Turkey in the 2019-2020 spring semester. For the purpose of this study, Preparedness to Teach scale was used to obtain the data after investigating the scale for validity and reliability properties. The results suggested that understanding the learner had both direct and indirect effects on forming effective learning environments. That is, the better PTs could understand the learner, the more appropriately they could design the instructional process and, ultimately, form an effective learning environment. However, techno-pedagogical competency had only indirect impacts on forming an effective learning environment. This finding suggests that the higher-competency PTs had in techno-pedagogy, the more effectively they could establish a learning environment by properly designing the instructional process.

Keywords

Measurement and evaluation in education, Designing instructional process, Effective learning environments, Structural equation modeling, Techno-pedagogical competency, Understanding learners

References

• Bang, E., & Luft, J. (2013). Secondary science teachers’ use of technology in the classroom during their first 5 years. Journal of Digital Learning in Teacher Education, 29(4), 118-126. DOI: 10.1080/21532974.2013.10784715
• Bandura, A. (1969). Principles of behavior modification. New York: Holt, Rinehart, and Winston.
• Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246. DOI: 10.1037/0033-2909.107.2.238
• Bentler, P. M., & Hu, P. (1995). EQS: Structural equations program manual. Los Angeles, CA: BMPD Statistical Software.
• Brown, C. P., & Englehardt, J. (2017). A case study of how a sample of preservice teachers made sense of incorporating iPads into their instruction with children. Journal of Early Childhood Teacher Education, 38(1), 19–38. DOI: 10.1080/10901027.2016.1274695
• Browne, M. W. & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230-258. DOI: 10.1177/0049124192021002005
• Bush, T. (2008). Leadership and management development in education. London: Sage.
• Chai, C. S., Ng, E. M. W., Li, W., Hong, H., & Koh, J. H. L. (2013). Validating and modelling technological pedagogical content knowledge framework among Asian preservice teachers. Australasian Journal of Educational Technology, 29, 41–53. DOI: 10.14742/ajet.174
• Chen, F.-H., Looi, C.-K., & Chen, W. (2009). Integrating technology in the classroom: a visual conceptualization of teachers’ knowledge, goals and beliefs. Journal of Computer Assisted Learning, 25(5), 470–488. DOI: 10.1111/j.1365-2729.2009.00323.x
• Compton, V., & Harwood, C. (2003). Enhancing technological practice: An assessment framework for technology education in New Zealand. International Journal of Technology and Design Education, 13(1), 1-26. DOI: 10.1023/A:1022318118467
• Cook-Sather, A. (2003, March/April). Listening to students about learning differences. Teaching Exceptional Children, 22–26.
• Darling-Hammond, L. (2006). Constructing 21st-century teacher education. Journal of Teacher Education, 57(3), 300-314. DOI: 10.1177/0022487105285962
• Darling-Hammond, L., Chung, R., & Frelow, F. (2002). Variation in teacher preparation: How well do different pathways prepare teachers to teach? Journal of Teacher Education, 53(4), 286-302. DOI: 10.1177/0022487102053004002
• Demetriou, C., Uzun Ozer, B., & Essau, C. A. (2015). Self-report questionnaires. In R. L. Cautin, and S. O. Lilienfeld (Eds.), The encyclopedia of clinical psychology (pp. 1–6). New York: JohnWiley & Sons. DOI: 10.1002/9781118625392.wbecp50
• Doyle, W. (1977). Paradigms for research on teacher effectiveness. In L. S. Schulman (Ed.), Review of research in education (pp. 163–197). Itasca: F.E. Peacock.
• Eret, E. (2013). An assessment of pre-service teacher education in terms of preparing teacher candidates for teaching (Doctoral dissertation). Middle East Technical University, Ankara.
• Hagger, H., Mutton, T., & Bird, K. (2011). Surprising but not shocking: The reality of the first year of teaching. Cambridge Journal of Education, 41, 387–405. DOI: 10.1080/0305764X.2011.624999
• Hanushek, E. A. (2011). The economic value of higher teacher quality. Economics of Education Review, 30(3), 466-479. DOI: 10.3386/w16606
• Harris, J., Mishra, P., & Koehler, M. (2009). Teachers’ technological pedagogical content knowledge and learning activity types: curriculum-based technology integration reframed. Journal of Research on Technology in Education, 41(4), 393–417. DOI: 10.1080/15391523.2009.10782536
• Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London, UK: Routledge. DOI: 10.4324/9780203887332
• Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology, 50(3), 956–971. DOI: 10.1111/bjet.12770
• Hofer, M., & Swan, K. O. (2008). Technological pedagogical content knowledge in action: A case study of a middle school digital documentary project. Journal of Research on Technology in Education, 41(2), 179-200. DOI: 10.1080/15391523.2008.10782528
• IBM Corp. (2013). IBM SPSS Statistics for Windows (Version 22.0). Armonk, NY: IBM Corp.
• Imbimbo, J., & Silvernail, D. (1999). Prepared to teach? Key findings of the New York City teacher survey. New York: New Visions for Public Schools.
• Inan, F. A., & Lowther, D. (2010). Laptops in the K-12 classrooms: exploring factors impacting instructional use. Computers & Education, 55(3), 937–944. DOI: 10.1016/j.compedu.2010.04.004
• Joiner, S., & Edwards, J. (2008). Novice teachers: Where are they going and why don’t they stay? Journal of Cross Disciplinary Perspectives in Education, 1, 36–43.
• Jones, A., Olds, A., & Lisciandro, J. (2016). Understanding the learner: Effective course design in the changing higher education space. International Studies in Widening Participation, 3(1), 19-35.
• Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford press.
• Knight, B. A., & Moore, T. (2012). Supporting beginning male teachers as they transform to skilled professionals. Improving Schools, 15, 61–72.
• Koehler, M. J., Mishra, P., & Yahya, K. (2007). Tracing the development of teacher knowledge on a design seminar: Integrating content, pedagogy and technology. Computers & Education, 49(3), 740-762. DOI: 10.1016/j.compedu.2005.11.012
• Koh, J. L., Chai, C. S., & Tsai, C. C. (2013). Examining practicing teachers’ perceptions of technological pedagogical content knowledge (TPACK) pathways: A structural equation modeling approach. Instructional Science, 41, 793–809. DOI: 10.1007/s11251-012-9249-y
• Kumar, N., Rose, R. C., & D’Silva, J. L. (2008). Teachers’ readiness to use technology in the classroom: An empirical study. European Journal of Scientific Research, 21(4), 603-616.
• Kushner Benson, S. N., Ward, C. L., & Liang, X. (2015). The essential role of pedagogical knowledge in technology integration for transformative teaching and learning, in Angeli C. and Valanides N. (Ed.), Technological pedagogical content knowledge exploring, developing, and assessing TPCK (pp. 3–18). New York: Springer Science + Business Media. DOI: 10.1007/978-1-4899-8080-9_1
• Lane, J., & Sharp, S. (2014). Pathways to success: Evaluating the use of “enabling pedagogies” in a university transition course. Journal on Education, 2(1), 66-73.
• Liao, Y. C. (2007). Effects of computer-assisted instruction on students’ achievement in Taiwan: A meta-analysis. Computers & Education, 48(2), 216–233. DOI: 10.1016/j.compedu.2004.12.005
• Lin, T., Tsai, C., Chai, C. S., & Lee, M. (2013) Identifying science teachers’ perceptions of technological pedagogical and content knowledge (TPACK). Journal of Science and Educational Technology, 22, 325–336.
• Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. DOI: 10.1111/j.1467-9620.2006.00684.x
• Muthén, L. K., & Muthén, B. O. (2007). Mplus user's guide (Sixth Edition). Los Angeles, CA:Muthén & Muthén.
• Morrison, G. R., Ross, S. J., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction. John Wiley & Sons.
• National Council for Accreditation of Teacher Education (NCATE). (2008). Professional standards for the accreditation of schools, colleges, and departments of education. Washington, DC: Author.
• Niess, M. L. (2005). Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge. Teaching and Teacher Education, 21(5), 509–523. DOI: 10.1016/j.tate.2005.03.006
• Nye, B., Konstantopoulos, S., & Hedges, L. V. (2004). How large are teacher effects? Educational Evaluation and Policy Analysis, 26, 237-257. DOI: 10.3102/01623737026003237
• Pillen, M., Beijaard, D., & den Brok, P. (2013). Professional identity tensions of beginning teachers. Teachers and Teaching: Theory and Practice, 19, 660–678. DOI: 10.1080/13540602.2013.827455
• Revell, A., & Wainwright, E. (2009). What makes lectures ‘unmissable’? Insights into teaching excellence and active learning. Journal of Geography in Higher Education, 33(2), 209-223. DOI: 10.1080/03098260802276771
• Rowan, B., Correnti, R., & Miller, R. J. (2002). What large-scale, survey research tells us about teacher effects on student achievement: Insights from the prospects study of elementary schools. Teachers College Record, 104, 1525-1567.
• Schmidt, D. A., Baran, E., Thompson, A. D., Mishra, P., Koehler, M. J., & Shin, T. S. (2009) Technological pedagogical content knowledge (TPACK): The development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42, 123–149. DOI: 10.1080/15391523.2009.10782544
• Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
• Senior, R. M. (2006). The experience of language teaching. Cambridge, England: Cambridge University Press.
• Silvernail, D. L. (1998). Findings from an initial analysis of the New York City Teacher Survey. New York: New Visions for Public Schools.
• Suhr, D. (2006). Exploratory or confirmatory factor analysis. SAS Users Group International Conference (pp. 1 - 17). Cary: SAS Institute, Inc. Retrieved from http://www2.sas.com/proceedings/sugi31/200-31.pdf
• Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th Ed.). Needham Heights, MA: Allyn & Bacon.
• Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2013). Technological pedagogical content knowledge–a review of the literature. Journal of Computer Assisted Learning, 29(2), 109–121. DOI: 10.1111/j.1365-2729.2012.00487.x
• West, S.G., Finch, J.F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: problems and remedies. In R.H. Hoyle (Ed.), Structural equation modeling: Concepts, issues and applications (pp. 56-75). Newbery Park, CA: Sage.
• Wolff, C., van den Bogert, N., Jarodzka, H., & Boshuizen, H. (2015). Keeping an eye on learning: Differences between expert and novice teachers’ representations of classroom management events. Journal of Teacher Education, 66, 68-85. DOI: 10.1177/0022487114549810
• Yildirim, I., & Kalman, M. (2017). Öğretmenliğe hazır olma ölçeğinin Türkçe formunun geçerlik ve güvenirlik çalışması [The validity and reliability study of the Turkish version of the preparedness to teach scale]. Kastamonu Eğitim Dergisi, 25(6), 2311-2326.