Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking

Alison Superfine; John Bragelman

doi:10.29333/ejmste/99501

Full Text (PDF)

Alison Superfine ¹ ^* , John Bragelman ¹

More Detail

¹ University of Illinois at Chicago, Chicago, Illinois, USA^* Corresponding Author

Abstract

Despite the growing research base on preservice teacher noticing of children’s mathematical thinking in video, few, if any studies consider the complex nature of the video representations themselves. Drawing from cognitive load theory, we developed a rubric to code the complexity of the salient teaching and learning events captured in video, and analyzed the relationship between video complexity and preservice teacher (n = 233) noticing. Results indicate that two categories significantly highlight children’s mathematical thinking and two categories significantly mask children’s mathematical thinking for preservice teachers. We discuss the implications of these results for the design of our instructional platform and other video-based learning environments used in preservice teacher education settings.

Keywords

cognitive load
preservice teacher education
teacher noticing
video

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2018, Volume 14, Issue 11, Article No: em1650

https://doi.org/10.29333/ejmste/99501

Publication date: 31 Oct 2018

Article Views: 2370

Article Downloads: 1090

Open Access References How to cite this article

References

Alsawaie, O. N., & Alghazo, I. M. (2010). The effect of video-based approach on prospective teachers’ ability to analyze mathematics teaching. Journal of Mathematics Teacher Education, 13(3), 223-241. https://doi.org/10.1007/s10857-009-9138-8.
American Statistical Association. (2007). Using statistics effectively in mathematics education research. Washington, DC: American Statistical Association.
Ball, D., & Cohen, D. (1999). Developing practice, developing practitioners. In L. Darling-Hammond & G. Sykes (Eds.), Teaching as the learning profession (pp. 3-32). San Francisco, CA: Jossey-Bass Publishers.
Ball, D., Thames, M., & Phelps, G. (2008). Content knowledge for teaching: What makes is so special? Journal of Teacher Education, 59, 389-407. https://doi.org/10.1177/0022487108324554.
Blomberg, G., Sturmer, K., & Seidel, T. (2011). How pre-service teachers observe teaching on video: Effects of viewers’ teaching subjects and the subject of the video. Teaching and Teacher Education, 27, 1131-1140. https://doi.org/10.1016/j.tate.2011.04.008.
Brophy, J. (2004). Using video in teacher education. New York, NY: Emerald Group Publishing.
Castro Superfine, A., Fisher, A., Bragelman, J., & Amador, J. (2017). Shifting perspectives on preservice teachers’ noticing of children’s thinking. In E. Schack, M. Fisher, & J. Wilhem (Eds.), Teacher Noticing: Bridging and Broadening Perspectives, Contexts and Frameworks (pp. 409-426). Springer International Publishing.
Castro Superfine, A., Li, W., Bragelman, J., & Fisher, A. (2015). Examining the use of video to support preservice elementary teachers’ noticing of children’s thinking. Journal of Technology and Teacher Education, 23(2), 137-157.
Chamberlin, M. (2005). Teacher discussions of students’ thinking: Meeting the challenge of attending to students’ thinking. Journal of Mathematics Teacher Education, 8(2), 141-170. https://doi.org/10.1007/s10857-005-4770-4.
Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology, 62(2), 233-246. https://doi.org/10.1111/j.2044-8279.1992.tb01017.x.
Fitzmaurice, G. M., Laird, N. M., & Rotnitzky, A. G. (1993). Regression models for discrete longitudinal responses. Statistical Science, 8(3), 284-309. https://doi.org/10.1214/ss/1177010903.
Hatch, T. & Grossman, P. (2009). Learning to look beyond the boundaries of representations: Using technology to examine teaching. Journal of Teacher Education, 60, 70-85. https://doi.org/10.1177/0022487108328533.
Homer, B. D., Plass, J. L., & Blake, L. (2008). The effects of video on cognitive load and social presence in multimedia-learning. Computers in Human Behavior, 24, 787-797. https://doi.org/10.1016/j.chb.2007.02.009.
Inan, G., & Wang, L. (2017). PGEE: An R package for analysis of longitudinal data with high-dimensional covariates. The R Journal, 9(1), 393-402.
Jacobs, V. R., Lamb, L. L. C., & Philipp, R. A. (2010). Professional noticing of children’s mathematical thinking. Journal for Research in Mathematics Education, 41(2), 169-202.
Kazemi, E., & Stipek, D. (2001). Promoting conceptual thinking in four upper-elementary mathematics classrooms. The Elementary School Journal, 102(1), 59-80. https://doi.org/10.1086/499693.
Kenward, M. G., Lesaffre, E., & Molenberghs, G. (1994). An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random. Biometrics, 50(4), 945-953. https://doi.org/10.2307/2533434.
Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Educational Psychologist, 902-913.
Masats, D. & Dooly, M. (2011). Rethinking the use of video in teacher education: A holistic approach. Teaching and Teacher Education, 27, 1151-1162. https://doi.org/10.1016/j.tate.2011.04.004.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52. https://doi.org/10.1207/S15326985EP3801_6.
Paa, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1-4. https://doi.org/10.1207/S15326985EP3801_1.
Pang, J. (2011). Case-based pedagogy for prospective teachers to learn how to teach elementary mathematics in Korea. ZDM Mathematics Education, 43, 777-789. https://doi.org/10.1007/s11858-011-0352-3.
Philipp (Eds.), Mathematics teacher noticing (pp. 117-133). New York: Routledge.
Santagata, R., Zannoni, C., & Stigler, J. W. (2007). The role of lesson analysis in pre-service teacher education: An empirical investigation of teacher learning from a virtual video-based field experience. Journal of mathematics teacher education, 10(2), 123-140. https://doi.org/10.1007/s10857-007-9029-9.
Sharpe, L., Hu, C., Crawford, L., Gopinathan, S., Khine, M., Moo, S., & Wong, A. (2003). Enhancing multipoint desktop video conferencing (MDVC) with lesson video clips: Recent developments in pre-service teachers practice in Singapore. Teaching and Teacher Education, 19, 529-541. https://doi.org/10.1016/S0742-051X(03)00050-7.
Sherin, M. G., & van Es, E. (2009). Effects of video club participation on teachers’ professional vision. Journal of Teacher Education, 60(1), 20-37. https://doi.org/10.1177/0022487108328155.
Sherin, M. G., & van Es, E. A. (2005). Using video to support teachers’ ability to notice classroom interactions. Journal of Technology and Teacher Education, 13, 475-491.
Sherin, M. G., Jacobs, V. R., & Phillip, R. (2011). Mathematics teacher noticing: Seeing through teachers’ eyes. New York, NY: Routledge. https://doi.org/10.4324/9780203832714.
Sherin, M. G., Linsenmeier, K., van Es., E. (2009). Selecting video clips to promote mathematics teachers’ discussion of student thinking. Journal of Teacher Education, 60(3), 213-230. https://doi.org/10.1177/0022487109336967.
Star, J. R., & Strickland, S. K. (2008). Learning to observe: Using video to improve preservice teachers’ ability to notice. Journal of Mathematics Teacher Education, 11, 107-125. https://doi.org/10.1007/s10857-007-9063-7.
Star, J., Lynch, K., & Perova, N. (2011). Using video to improve preservice mathematics teachers’ abilities to attend to classroom features. In M. G. Sherin, V. Jacobs, & R.
Stein, M. K., Engle, R., Smith, M., & Hughes, E. (2015). Orchestrating productive mathematics discussions: Helping teachers learn to better incorporate student thinking (pp. 375-388). In L. Resnick, C. Asterhan, & S. Clarke (Eds.), Socializing intelligence through academic talk and dialogue. Washington, DC: American Educational Research Association. https://doi.org/10.3102/978-0-935302-43-1_29.
Stein, M., Smith, M., Henningsen, M., & Silver, E. (2000). Implementing standards-based mathematics instruction. New York, NY: Teachers College Press.
Sweller, J. (2003). Evolution of human cognitive architecture. In B. H. Hoss (Ed.), The psychology of learning and motivation: Advances in research and theory (pp. 216-266). San Diego, CA: Academic Press.
Tekkumru Kisa, M., & Stein, M. K. (2015). Learning to see teaching in new ways: A foundation for maintaining cognitive demand. American Educational Research Journal, 52(1), 105-136. https://doi.org/10.3102/0002831214549452.
van Es, E. & Sherin, M. (2008). Mathematics teachers’ “learning to notice” in the context of a video club. Teaching and Teacher Education, 24, 244-276. https://doi.org/10.1016/j.tate.2006.11.005.
van Es, E. (2011). A framework for learning to notice student thinking. In M. G. Sherin, V. R. Jacobs, & R. Phillip (Eds.), Mathematics teacher noticing: Seeing through teachers’ eyes (pp. 165-180). New York, NY: Routledge.
van Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5-13. https://doi.org/10.1207/S15326985EP3801_2.
Zeger, S. L., & Liang, K. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121-130. https://doi.org/10.2307/2531248.
Zorn, C. J. (2001). Generalized estimating equation models for correlated data: A review with applications. American Journal of Political Science, 45(2), 470-490. https://doi.org/10.2307/2669353.

How to cite this article

APA

Superfine, A., & Bragelman, J. (2018). Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking. Eurasia Journal of Mathematics, Science and Technology Education, 14(11), em1650. https://doi.org/10.29333/ejmste/99501

Vancouver

Superfine A, Bragelman J. Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking. EURASIA J Math Sci Tech Ed. 2018;14(11):em1650. https://doi.org/10.29333/ejmste/99501

AMA

Superfine A, Bragelman J. Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking. EURASIA J Math Sci Tech Ed. 2018;14(11), em1650. https://doi.org/10.29333/ejmste/99501

Chicago

Superfine, Alison, and John Bragelman. "Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking". Eurasia Journal of Mathematics, Science and Technology Education 2018 14 no. 11 (2018): em1650. https://doi.org/10.29333/ejmste/99501

Harvard

Superfine, A., and Bragelman, J. (2018). Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking. Eurasia Journal of Mathematics, Science and Technology Education, 14(11), em1650. https://doi.org/10.29333/ejmste/99501

MLA

Superfine, Alison et al. "Analyzing the Impact of Video Representation Complexity on Preservice Teacher Noticing of Children’s Thinking". Eurasia Journal of Mathematics, Science and Technology Education, vol. 14, no. 11, 2018, em1650. https://doi.org/10.29333/ejmste/99501