Physics Pre-service Teachers’ Approaches to Scientific Investigations by Data Exploration
More details
Hide details
University of Graz, AUSTRIA
Publication date: 2020-09-08
EURASIA J. Math., Sci Tech. Ed 2020;16(11):em1893
This article reports how physics pre-service teachers (PSTs) organize their investigations during an exploratory data analysis scenario, which we call scientific investigations by data exploration. In order to analyze the PSTs’ investigations, we developed a learning environment in which learners investigate aspects influencing the particulate matter concentration in an Austrian city. Audio documentation and written learner protocols were analyzed using qualitative content analyses, resulting in flowcharts describing the different types of investigations the PSTs conducted. In this analysis, we differentiate between investigations on a micro-level (a single investigation), and investigations on a macro-level. Findings show that the pre-service teachers follow three different approaches: some always start their investigations with a research question, some switch between exploratory and targeted investigations and a few conducted only exploratory investigations. In this article we provide exploratory insights into the strategies students use.
Arnold, J., Boone, W. J., Kremer, K., & Mayer, J. (2018). Assessment of competencies in scientific inquiry through the application of Rasch measurement techniques. Education Sciences, 8(4), 184.
Ben-Zvi, D. (2005). Reasoning about Data Analysis. In D. Ben-Zvi & J. Garfield (Eds.), The Challenge of Developing Statistical Literacy, Reasoning and Thinking (Vol. 15, pp. 121–145). Dordrecht: Springer Science + Business Media Inc.
Ben-Zvi, D., & Friedlander, A. (1997). Statistical thinking in a technological environment. In J. Garfield & G. Burrill (Eds.), Research on the Role of Technology in Teaching and Learning Statistics (pp. 45–55). Voorburg: ISI.
Chan, S. W., & Ismail, Z. (2012). The Role of Information Technology in Developing Students’ Statistical Reasoning. Procedia - Social and Behavioral Sciences, 46, 3660–3664.
Confrey, J., & Makar, K. (2002). Developing secondary teachers’ statistical inquiry through immersion in high-stakes accountability data. Proceedings of the Twenty-Fourth Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education PME-NA24, 3.
delMas, R., & Liu, Y. (2005). Exploring students’ conceptions of the standard deviation. Statistics Education Research Journal, 4(1), 55–82.
Garfield, J. B., Ben-Zvi, D., Chance, B., Medina, E., Roseth, C., & Zieffler, A. (2008). Developing Students’ Statistical Reasoning: Connecting Research and Teaching Practice. Dordrecht: Springer Science+Business Media B.V.
Hammerman, J. K., & Rubin, A. (2004). Strategies for managing statistical complexity with new software tools. Statistics Education Research Journal, 3(2), 17–41.
Irish, T., Berkowitz, A., & Harris, C. (2019). Data Explorations: Secondary Students’ Knowledge, Skills and Attitudes Toward Working with Data. Eurasia Journal of Mathematics, Science and Technology Education, 15(6).
Konold, C., & Miller, C. D. (2005). TinkerPlots: Dynamic data exploration: Computer Software. Emeryville, CA: Key Curriculum Press.
Lederman, N. G., Lederman, J. S., & Antink, A. (2013). Nature of Science and Scientific Inquiry as Contexts for the Learning of Science and Achievement of Scientific Literacy. International Journal of Education in Mathematics, Science and Technology, 1(3), 138–147.
Makar, K., & Confrey, J. (2014). Wondering, Wandering or Unwavering?: Learners’ statistical investigations with fathom. In Thomas Wassong (Ed.), Mit Werkzeugen Mathematik und Stochastik lernen. Using tools for learning mathematics and statistics (pp. 351–362). Wiesbaden: Springer Spektrum.
Makar, K., Bakker, A., & Ben-Zvi, D. (2011). The Reasoning behind informal statistical inference. Mathematical Thinking and Learning, 13(1-2), 152–173.
Mayring, P. (2014). Qualitative Content Analysis: theoretical foundation, basic procedures and software solution. Klagenfurt. Retrieved from
Pfannkuch, M. (1999). Statistical Thinking in Empirical Enquiry. International Statistical Review, 67(3), 223–265.
Reinert, D., Prill, F., Frank, H., Zängl, G., & Denhard, M. (2018). ICON Database Reference Manual: Version 1.2.6. Retrieved from
Shaman, J., Karspeck, A., Yang, W., Tamerius, J., & Lipsitch, M. (2013). Real-time influenza forecasts during the 2012–2013 season. Nature Communications, 4(1), 2837.
Schubatzky, T., & Haagen-Schützenhöfer, C. (2019). Online data repositories as educational resources? A learning environment covering formal and informal inferential statistics ideas in scientific inquiry. European Journal of Physics, 40(4), 45802.
Tukey, J. W. (1980). We Need Both Exploratory and Confirmatory. The American Statistician, 34(1), 23-25.
Utts, J. M., & Horton, N. J. (2018). What is Statistics? In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International Handbook of Research in Statistics Education. Springer.
Journals System - logo
Scroll to top