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Video Analysis As A Tool For Understanding Science Instruction

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Science Education Research and Practice in Europe

Part of the book series: Cultural Perpectives in Science Education ((CHPS,volume 5))

Abstract

Research on science instruction has revealed complex and nontrivial relations between instructional variables – including school system characteristics, teacher cognition and beliefs, teachers’ and students’ activities during instruction and last but not least, learning outcomes. To further investigate these relations, the development of respective models as well as appropriate research designs and methodologies are required. This will allow for tracing effects to the instructional level, shedding light on the well-known gap between teachers’ demands and students’ efforts as well as for the creation of interventions to overcome this gap. To this end, variables of teaching and learning have to be investigated using low- and high-inferent video analyses. Students’ and teachers’ behaviour holds valuable information for identifying cause-effect relations between what happens in the classroom and targeted outcomes.

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References

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. Standards for educational and psychological testing. Washington, DC: American Educational Research Association; 2004.

    Google Scholar 

  • American Psychological Association. (51). Technical recommendations for psychological tests and diagnostic techniques. Psychological Bulletin, (2).

    Google Scholar 

  • Anderson L. Instruction and Time-on- Task: a Review. Journal of Curriculum Studies. 1981;13(4):289–303. doi:10.1080/0022027810130402.

    Article  Google Scholar 

  • Anderson RD. A Consolidation and Appraisal of Science Meta-Analyses. Journal of Research in Science Teaching. 1983;20(5):497–509.

    Article  Google Scholar 

  • von Aufschnaiter C, Erduran S, Osborne J, Simon S. Arguing to learn and learning to argue: Case studies of how students’ argumentation relates to their scientific knowledge. Journal of Research in Science Teaching. 2008;45(1):101–131. doi:10.1002/tea.20213.

    Article  Google Scholar 

  • Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2006). Multivariate Analysemethoden: Eine anwendungsorientierte Einführung (Vol. 11). [Multivariate analysis: an applied oriented introduction] Berlin: Springer.

    Google Scholar 

  • Baumert J, Kunter M, Blum W, Brunner M, Voss T, Jordan A. Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress. American Educational Research Journal. 2010;47(1):133–180. doi:10.3102/0002831209345157.

    Article  Google Scholar 

  • Berg, B. L. (2004). Qualitative research methods for the social sciences (5th ed.). Boston: Pearson. Retrieved from http://www.worldcat.org/oclc/51223512

  • Bromme, R. (2008). Lehrerexpertise. In W. Schneider & M. Hasselhorn (Eds.), Handbuch der pädagogischen Psychologie (pp. 159–167). [Handbook of pedagogical psychology] Göttingen ;, Bern, Wien, Paris, Oxford, Prag, Toronto, Cambridge, MA, Amsterdam, Kopenhagen: Hogrefe.

    Google Scholar 

  • Bromme R. Kompetenzen, Funktionen und unterrichtliches Handeln der Lehrer. In: Weinert FE, editor. Enzyklopädie der Psychologie, Psychologie des Unterrichts und der Schule, vol. 3. Göttingen: Hogrefe; 1997. p. 177–212.

    Google Scholar 

  • Brophy JE, Good TL. Teacher behaviour and student achievement. In: Wittrock MC, editor. Handbook of research on teaching. New York: Macmillan; 1986. p. 328–375.

    Google Scholar 

  • Chao I, Jen T. Policy Evaluation on Technology Integrated Instructions of Junior High Math and Science Teaching in Taiwan. In: Richards G, editor. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2007. Chesapeake, VA: AACE; 2007. p. 2204–2211.

    Google Scholar 

  • Clark CM, Peterson PLM. Teachers’ Tought Processes. In: Wittrock MC, editor. Handbook of Research on Teaching. New York: Macmillan Publishing Company; 1986. p. 255–296.

    Google Scholar 

  • Clarke DJ, Hollingsworth H. Elaborating a model of teacher professional growth. Teaching and Teacher Education. 2002;18(8):947–967.

    Article  Google Scholar 

  • Clausen, M. (2000). Wahrnehmung von Unterricht, Übereinstimmung, Konstruktvalidität und Kriteriumsvalidität in der Forschung zur Unterrichtsqualität (Dissertation). [Perceiving lessons, accordance, construct validity and criterion validity in research on quality of instruction] Freie Universität Berlin, Berlin.

    Google Scholar 

  • Duschl R. Science Education in Three-Part Harmony: Balancing Conceptual, Epistemic, and Social Learning Goals. Review of Research in Education. 2008;32(1):268–291. doi:10.3102/ 0091732X07309371.

    Article  Google Scholar 

  • Einsiedler W. Unterrichtsqualität und Leistungsentwicklung: Literaturüberblick. In: Weinert FE, Helmke A, editors. Entwicklung im Grundschulalter. Weinheim: PVU; 1997. p. 225–240.

    Google Scholar 

  • Ennis RH. Critical thinking assessment. Theory into Practice. 1993;32(3):179–186.

    Article  Google Scholar 

  • Erickson F. Audiovisual Records as a Primary Data Source. Sociological Methods & Research. 1982;11(2):213–232. doi:10.1177/0049124182011002008.

    Article  Google Scholar 

  • Erickson F. Qualitative methods research on teaching. In: Wittrock MC, editor. Handbook of research on teaching. New York: Macmillan; 1986. p. 119–161.

    Google Scholar 

  • Fraser BJ, Walberg HJ, Welch WW, Hattie JA. Syntheses of Educational Productivity Research. International Journal of Educational Research. 1987;11:145–252.

    Article  Google Scholar 

  • Gage NL. Teaching methods. In: Ebel RL, Noll VH, Bauer RM, editors. Encyclopedia of educational research. 4th ed. New York: Macmillan; 1969. p. 1446–1458.

    Google Scholar 

  • Getzels JW, Jackson PW. Merkmale der Lehrerpersönlichkeit. In: Ingenkamp K, editor. Handbuch der Unterrichtsforschung. Weinheim: Beltz; 1970. p. 1353–1526.

    Google Scholar 

  • Glaser, B. G. (1992). Basics of grounded theory analysis. [S.l.]: Sociology Press. Retrieved from http://www.worldcat.org/oclc/72762268

  • Goodman, L. A., & Kruskal, W. (1979). Measures of association for cross classification. New York, NY: Springer-Verlag. Retrieved from http://www.worldcat.org/oclc/503191003

  • Grimshaw AD. Sound-Image Data Records for Research on Social Interaction: Some Questions and Answers. Sociological Methods & Research. 1982;11(2):121–144. doi:10.1177/ 0049124182011002002.

    Article  Google Scholar 

  • Hair, J, F., Black, W. C. Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006), Multivariate data analysis (6th ed.). Upper Saddle River, N.J.: Prentice-Hall.

    Google Scholar 

  • Hardy, M., & Bryman, A. (2004). Handbook of data analysis: Sage. Retrieved from http://books.google.de/books?id=kV0aK9DcuukC

  • Helmke A. Unterrichtsqualität: Erfassen, Bewerten, Verbessern. 4th ed. Seelze: Kallmeyersche Verlagsbuchhandlung GmbH; 2006.

    Google Scholar 

  • Hiebert, J. (2003). Teaching mathematics in seven countries: Results from the TIMSS 1999 video study. Washington, DC: U.S. Gov. Print. Off. Retrieved from http://purl.access.gpo.gov/GPO/LPS31989

  • Hox, J. J. (2002). Multilevel analysis: techniques and applications: Lawrence Erlbaum Associates. Retrieved from http://books.google.de/books?id=LEfj0Nu2D8UC

  • Hugener, I., Pauli, C., Reusser, K., Lipowsky, F., Rakoczy, K., & Klieme, E. (2009). Teaching patterns and learning quality in Swiss and German mathematics lessons. Learning and Instruction, 19(1), 66–78. doi:10.1016/j.learninstruc.2008.02.001

  • Jacobs, J., Garnier, H., Gallimore, R., Hollingsworth, H., Givvin, K. B., Rust, K., … (2003). TIMSS 1999 Video Study Technical Report: Volume 1: Mathematics Study. Waschington, DC: National Center of Education Statistics.

    Google Scholar 

  • Jacobs JK, Kawanaka T, Stigler JW. Integrating qualitative and quantitative approaches to the analysis of video data on classroom teaching. International Journal of Educational Research. 1999;31:717–724.

    Article  Google Scholar 

  • Jong, O. de, & van Driel, J. (2004). Exploring the developement of student teachers‘ PCK of the multiple meanings of chemistry topics. International Journal of Science and Mathematics Education, 2 (4), 477–491.

    Google Scholar 

  • Kaplan, D. (2004). The Sage handbook of quantitative methodology for the social sciences. Thousand Oaks, Calif: Sage. Retrieved from http://www.worldcat.org/oclc/53970601

  • Krauss S, Brunner M, Kunter M, Baumert J, Blum W, Neubrand M, Jordan A. Pedagogical content knowledge and content knowledge of secondary mathematics teachers. Journal of Educational Psychology. 2008;100:716–725.

    Article  Google Scholar 

  • Krippendorf K. Content analysis. An Introduction to its Methodology. Beverly Hills: Sage; 1980.

    Google Scholar 

  • Lederman NG, Latz MS. Knowledge structures in the preservice teacher: Sources, development, interactions, and relationships to teaching. Journal of Science Teacher Education. 1995;6(1):1–19.

    Article  Google Scholar 

  • Lipowsky, F., Rakoczy, K., Vetter, B., Klieme, E., Reusser, K., & Pauli, C. (2005). Quality of geometry instruction and its impact on the achievement of students with different characteristics. American Educational Research Association

    Google Scholar 

  • Lipowsky, F., Rakoczy, K., Pauli, C., Drollinger-Vetter, B., Klieme, E., & Reusser, K. (2009). Quality of geometry instruction and its short-term impact on students’ understanding of the Pythagorean Theorem. Learning and Instruction, 19(6), 527–537. Retrieved from http://www.sciencedirect.com/science/article/B6VFW-4V5GCR7-1/2/8e40bb2c0c91432167cec213c5b9a8c7

  • Lubienski ST, Lubienski C. School Sector and Academic Achievement: A Multilevel Analysis of NAEP Mathematics Data: American Educational Research Journal. American Educational Research Journal. 2006;43(4):651–698.

    Article  Google Scholar 

  • Maas CJM, Hox JJ. Robustness issues in multilevel regression analysis. Statistica Neerlandica. 2004;58:127–137.

    Article  Google Scholar 

  • Mayring P. Qualitative Inhaltsanalyse: Grundlagen und Techniken. 5th ed. Weinheim: Dt. Studien-Verl; 1995.

    Google Scholar 

  • Mayring, P. (2007). Qualitative Inhaltsanalyse. Grundlagen und Techniken (Vol. 9). Basel: Beltz.

    Google Scholar 

  • McCurry, D. (2000). Technology for Critical Pedagogy: Beyond Self-Reflection with Video. In D. A. Willis, J. D. Price, & J. Willis (Eds.), Proceedings of SITE 2000. Abstracts (pp. 6–11). Charlottesville, VA: Association for the Advancement of Computer in Education (AACE).

    Google Scholar 

  • Messick S. Meaning and Values in Test Validation: The Science and Ethics of Assessment. Educational Researcher. 1989;18(2):5–11. doi:10.3102/0013189X018002005.

    Google Scholar 

  • Mortimer, E. F., & Scott, P. (2003). Meaning making in secondary science classrooms. Buckingham: Open University Press. Retrieved from http://www.worldcat.org/oclc/182530398

  • Norman K. Denzin, & Yvonna S. Lincoln. (2005). The SAGE handbook of qualitative research: Sage Publications. Retrieved from http://books.google.de/books?id=X85J8ipMpZEC

  • Oevermann U, Allert T, Konau E, Krambeck T. Die Methodologie einer „objektiven Hermeneutik" und ihre allgemeine forschungslogische Bedeutung in den Sozialwissenschaften. In: Soeffner H-G, editor. Interpretative Verfahren in den Sozial- und Textwissenschaften. Stuttgart: Metzler; 1979. p. 353–434.

    Google Scholar 

  • Organisation for Economic Co-operation and Development. (2007). Programme for International Student Assessment (PISA) 2006: Science Competencies for Tomorrow’s World. Paris: Organisation for Economic Co-operation and Development. Retrieved from http://public.eblib.com/EBLPublic/ PublicView.do?ptiID = 359823

  • Oser F, Dick A, Patry J. Effective and responsible teaching: The new synthesis. San Francisco, CA: Jossey-Bass; 1992.

    Google Scholar 

  • Peterson, K., Kauchak, D., & Yaakobi, D. (1980). Science students’ role-specific self-concept: Course, success, and gender. Science Education, 64 (2), 169–174. doi:10.1002/sce.3730640206

    Google Scholar 

  • Peterson PL, Fennema E, Carpenter TP, Loef M. Teachers’ pedagogical content beliefs in mathematics. Cognition and Instruction. 1989;6(1):1–40.

    Article  Google Scholar 

  • Prenzel, M. (Ed.). (2004). PISA 2003: Der Bildungsstand der Jugendlichen in Deutschland - Ergebnisse des zweiten internationalen Vergleichs. Münster, München, Berlin: Waxmann. Retrieved from http://swbplus.bsz-bw.de/bsz114861056kla.htm

  • Rakoczy, K., Klieme, E., Drollinger-Vetter, B., Lipowsky, F., Pauli, C., & Reusser, K. (2007). Structure as a quality feature of instruction. In M. Prenzel (Ed.), Studies in the educational quality of schools. The final report on the DFG Priority Programme (pp. 101–120). Münster: Waxmann.

    Google Scholar 

  • Reyer, T. (2005). Qualitative Video-Analysis Applied to Classroom Studies - A First-Steps Workshop. In H. E. Fischer (Ed.), Developing Standards in Research on Science Education. The ESERA Summer School 2004. Proceedings of the Conference on Developing Standards in Research on Science Education - the 7th ESERA Summer School, Mülheim, Germany, 28 August - 4 September 2004 (pp. 39–45). London: Taylor&Francis.

    Google Scholar 

  • Roth, K. J., Druker, S. L., Garnier, H. E., Lemmens, M., Chen, C., Kawanaka, T., … (2006). Teaching science in five countries: Results from the TIMSS 1999 video study statistical analysis report. (NCES 2006–011). U.S. Department of Education,National Center for Education Statistics. Washington, DC: U.S. Government Printing Office. Retrieved from _http://nces.ed.gov._

  • Schön, D. A. (1987). Educating the reflective practitioner (1st). San Francisco: Jossey-Bass. Retrieved from http://www.worldcat.org/oclc/22142478

  • Seidel T, Prenzel M, Kobarg M, editors. How to run a video study. Münster: Waxmann; 2005.

    Google Scholar 

  • Seidel, T., Rimmele, R., & Prenzel, M. (2005). Clarity and coherence of lesson goals as a scaffold for student learning. Learning and Instruction. doi:10.1016/j.learninstruc.2005.08.004

  • Seidel T, Rimmele R, Prenzel M. Gelegenheitsstrukturen beim Klassengespräch und ihre Bedeutung für die Lernmotivation. Unterrichtswissenschaft. 2003;31(2):142–165.

    Google Scholar 

  • Seidel, T., Prenzel, M., Rimmele, R., Herweg, C., Kobarg, M., & Schwindt, K. (2007). Science teaching and learning in German physics classrooms. In M. Prenzel (Ed.), Studies in the educational quality of schools. The final report on the DFG Priority Programme (pp. 79–99). Münster: Waxmann.

    Google Scholar 

  • Seidel, T., & Prenzel, M. (2005). How to run a video study. Technical report of the IPN Video Study. (Kobarg, M., Ed.). Münster: Waxmann.

    Google Scholar 

  • Shulman LS. Knowledge and teaching of the new reform. Harvard Educational Review. 1987;57:1–22.

    Google Scholar 

  • Shulman L. Those who understand: Knowledge growth in teaching. Educational Researcher. 1986;15(2):4–14.

    Google Scholar 

  • Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publications Ltd. Retrieved from http://www.worldcat.org/oclc/474779852

  • Staub F, Stern E. The nature of teachers’ pedagogical content beliefs matters for students’ achievement gains: quasi-experimental evidence from elementary mathematics. Journal of Educational Psychology. 2002;94(2):344–355.

    Article  Google Scholar 

  • Stigler JW, Hiebert J. Understanding and Improving Mathematics Instruction: An Overview of the TIMSS Video Study. Phi Delta Kappa. 1997;79(1):7–21.

    Google Scholar 

  • Stigler, J., Gonzales, P., Kawanaka, T., Knoll, S., & Serrano, A. (1999). The TIMSS videotape classroom study. Methods and findings from an exploratory research project on eighth-grade mathematics instruction in Germany, Japan and the United States. Washington D.C.: U.S. Department of Education.

    Google Scholar 

  • Strauss, A., & Corbin, J. (1998). Basics of qualitative research: techniques and procedures for developing grounded theory: Sage Publications. Retrieved from http://books.google.de/books?id=wTwYUnHYsmMC

  • Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Thousand Oaks, Calif: Sage. Retrieved from http://www.worldcat.org/oclc/38732106

  • Titscher, S., & Jenner, B. (2000). Methods of text and discourse analysis: Sage. Retrieved from http://books.google.de/books?id=qpaVYyn5Jj8C

  • Treumann, K. P. (1998). Triangulation als Kombination qualitativer und quantitativer Forschung. [Triangulation as combination of qualitative and quantitative research] In J. Abel, R. Möller, & K. P. Treumann (Eds.), Grundriß der Pädagogik: Vol. 2. Einführung in die Empirische Pädagogik. Stuttgart: W. Kohlhammer.

    Google Scholar 

  • Turner J, Meyer D. Studying and Understanding the Instructional Contexts of Classrooms: Using our Past to Forge our Future. Educational Psychologist. 2000;35(2):69–85. doi:10.1207/S15326985EP3502_2.

    Article  Google Scholar 

  • von Aufschnaiter, S., & Welzel, M. (Eds.). (2001). Nutzung von Videodaten zur Untersuchung von Lehr-Lern-Prozessen: aktuelle Methoden empirischer pädagogischer Forschung. [Using video data for analysing teaching and learning: recent methods of empirical pedagogical research] Münster: Waxmann.

    Google Scholar 

  • Wackermann, R., Trendel, G., & Fischer, H. E. (2010). Evaluation of a Theory of Instructional Sequences for Physics Instruction. International Journal of Science Education, 32(7), 963–985. doi:10.1080/09500690902984792

    Google Scholar 

  • Walberg, H. J. (1981). A psychological theory of educational productivity. In F. H. Farley & N. Gordon (Eds.), Psychology and education: The state of the union ( (pp. 81–108). Berkeley: McCutchan.

    Google Scholar 

  • Wang MC, Haertel GD, Walberg HJ. Toward a knowledge base for school learning. Review of Educational Research. 1993;63:249–294.

    Google Scholar 

  • Wever, B. de, Vankeer, H., Schellens, T., & Valcke, M. (2007). Applying multilevel modelling to content analysis data: Methodological issues in the study of role assignment in asynchronous discussion groups. Learning and Instruction, 17(4), 436–447. doi:10.1016/j.learninstruc.2007.04.001

  • Wong A, Young D, Fraser B. A Multilevel Analysis of Learning Environments and Student Attitudes. Educational Psychology. 1997;17(4):449–468. doi:10.1080/0144341970170406.

    Article  Google Scholar 

  • Xiaoxia A. Gender Differences in Growth in Mathematics Achievement: Three- Level Longitudinal and Multilevel Analyses of Individual, Home, and School Influences. Mathematical Thinking & Learning. 2002;4(1):1–22.

    Article  Google Scholar 

  • Yip DY, Tsang WK, Cheung SP. Evaluation of the Effects of Medium of Instruction on the Science Learning of Hong Kong Secondary Students: Performance on the Science Achievement Test. Bilingual Research Journal. 2003;27(2):295–331. doi:10.1080/15235882.2003.10162808.

    Article  Google Scholar 

  • Young, D. J. (1997). A Multilevel Analysis of Science and Mathematics Achievement. Paper presented at the Annual Meeting of the American Educational Research Association (Chicago, IL, March 24–26).

    Google Scholar 

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Fischer, H.E., Neumann, K. (2012). Video Analysis As A Tool For Understanding Science Instruction. In: Jorde, D., Dillon, J. (eds) Science Education Research and Practice in Europe. Cultural Perpectives in Science Education, vol 5. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6091-900-8_6

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