Introducing teachers who use GUI-driven tools for the randomization test to code-driven tools

References

• Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., & Chang, W. (2018). rmarkdown: Dynamic documents for R. https://CRAN.R-project.org/package=rmarkdown
   Google Scholar
• Bakker, A., & Van Eerde, D. (2015). An introduction to design-based research with an example from statistics education. In A. Bikner-Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Approaches to qualitative research in mathematics education (pp. 429–466). Springer.
   Google Scholar
• Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20–29.
   Google Scholar
• Bell, T., & Roberts, J. (2016). Computational thinking is more about humans than computers. Set: Research Information for Teachers, 1(1), 3–7. https://doi.org/10.18296/set.0030
   Google Scholar
• Ben-Zvi, D. (2000). Toward understanding the role of technological tools in statistical learning. Mathematical Thinking and Learning, 2(1–2), 127–155. https://doi.org/10.1207/S15327833MTL0202_6
   Google Scholar
• Biehler, R. (2018). Design principles, realizations and uses of software supporting the learning and the doing of statistics - a reflection on developments since the late 1990s. In M. A. Sorto, A. White, & L. Guyot (Eds.), Looking back, looking forward. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan. International Statistical Institute.
   Google Scholar
• Bray, A., Ismay, C., Baumer, B., & Cetinkaya-Rundel, M. (2018). Infer: Tidy statistical inference. https://CRAN.R-project.org/package=infer
   Google Scholar
• Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at Annual American Educational Research Association meeting, Vancouver, BC, Canada.http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf.
   Google Scholar
• Case, C., & Jacobbe, T. (2018). A framework to characterize student difficulties in learning inference from a simulation-based approach. Statistics Education Research Journal, 17(2), 9–29.https://iase-web.org/documents/SERJ/SERJ17(2)_Case.pdf
   Google Scholar
• Casey, S. A., & Wasserman, N. H. (2015). Teachers’ knowledges about informal line of best fit. Statistics Education Research Journal, 14(1), 8–35. https://iase-web.org/documents/SERJ/SERJ14(1)_Casey.pdf
   Google Scholar
• Cetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and tools for teaching computing throughout the statistical curriculum. The American Statistician, 72(1), 58–65. https://doi.org/10.1080/00031305.2017.1397549
   Web of Science ®Google Scholar
• Chance, B., delMas, R., & Garfield, J. (2004). Reasoning about sampling distributions. In D. Ben-Zvi & J. B. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 295–323). Springer.
   Google Scholar
• Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210. https://doi.org/10.1007/BF01320076
   Web of Science ®Google Scholar
• Cobb, G. W.(1997). Mere literacy is not enough. In L. Steen (Ed.), Why numbers count: Quantitative literacy for tomorrow’s America (pp. 75–90). College Entrance Examination Board.
   Google Scholar
• Cobb, G. W. (2007). The introductory statistics course: A ptolemaic curriculum? Technology Innovations in Statistics Education, 1(1). https://escholarship.org/uc/item/6hb3k0nz
   Google Scholar
• Cook, S. W., & Goldin-Meadow, S. (2006). The role of gesture in learning: Do children use their hands to change their minds? Journal of Cognition and Development, 7(2), 211–232. https://doi.org/10.1207/s15327647jcd0702_4
   Web of Science ®Google Scholar
• Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.
   Google Scholar
• Erickson, T., Wilkerson, M., Finzer, B., & Reichsman, F. (2019). Data moves. Technology Innovations in Statistics Education, 12(1). https://escholarship.org/uc/item/0mg8m7g6
   Google Scholar
• Fergusson, A., & Pfannkuch, M. (2020). Development of an informal test for the fit of a probability distribution model for teaching. Journal of Statistics Education, 28(3), 344-357. https://doi.org/10.1080/10691898.2020.1837039
   Google Scholar
• Ferreira, R. D. S., Kataoka, V. Y., & Karrer, M. (2014). Teaching probability with the support of the R statistical software. Statistics Education Research Journal, 13(2), 132–147. https://iase-web.org/documents/SERJ/SERJ13(2)_Ferreira.pdf
   Google Scholar
• Finzer, W. (2013). The data science education dilemma. Technology Innovations in Statistics Education, 7(2). https://escholarship.org/uc/item/7gv0q9dc
   Google Scholar
• Forbes, S., Chapman, J., Harraway, J., Stirling, D., & Wild, C. J. (2014). Use of data visualization in the teaching of statistics: A New Zealand perspective. Statistics Education Research Journal, 13(2), 187-201. https://iase-web.org/documents/SERJ/SERJ13(2)_Forbes.pdf
   Google Scholar
• Garfield, J., delMas, R., & Zieffler, A. (2012). Developing statistical modelers and thinkers in an introductory, tertiary-level statistics course. ZDM, 44(7), 883–898. https://doi.org/10.1007/s11858-012-0447-5
   Google Scholar
• Gelman, A., & Nolan, D. (2017). Teaching statistics: A bag of tricks. Oxford University Press.
   Google Scholar
• Gould, R. (2010). Statistics and the modern student. International Statistical Review, 78(2), 297–315. https://doi.org/10.1111/j.1751-5823.2010.00117.x
   Web of Science ®Google Scholar
• Grolemund, G., & Wickham, H. (2014). A cognitive interpretation of data analysis. International Statistical Review, 82(2), 184–204. https://doi.org/10.1111/insr.12028
   Web of Science ®Google Scholar
• Hesterberg, T. C. (1998). Simulation and bootstrapping for teaching statistics. American Statistical Association proceedings of the section on Statistical Education, Dallas, Texas, USA(pp. 44-52). ASA.
   Google Scholar
• Kaplan, D. (2007). Computing and introductory statistics. Technology Innovations in Statistics Education, 1(1). https://escholarship.org/uc/item/3088k195
   Google Scholar
• Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., Malyn-Smith, J., & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37. https://doi.org/10.1145/1929887.1929902
   Google Scholar
• Magana, A. J., Vasileska, D., & Ahmed, S. (2011). Work in progress—a transparency and scaffolding framework for computational simulation tools. 2011 Frontiers in Education conference (FIE), Rapid City, South Dakota, USA, S4G–1. IEEE.
   Google Scholar
• McNamara, A. A. (2015). Bridging the gap between tools for learning and for doing statistics. [PhD dissertation, University of California].
   Google Scholar
• Ministry of Education. (2007). The New Zealand Curriculum. Learning Media Limited.
   Google Scholar
• Molyneux, J., McNamara, A., Johnson, T., & Nolen, S. (2017). mobilizr: Functions for the mobilize introduction to data science course. R package version 0.3.12.
   Google Scholar
• Nardelli, E. (2019). Do we really need computational thinking? Communications of the ACM, 62(2), 32–35. https://doi.org/10.1145/3231587
   Web of Science ®Google Scholar
• National Academies of Sciences, Engineering, and Medicine. (2018). Data science for undergraduates: Opportunities and options. NASEM. https://www.nap.edu/catalog/25104/data-science-for-undergraduates-opportunities-and-options
   Google Scholar
• Nolan, D., & Temple Lang, D. (2010). Computing in the statistics curricula. The American Statistician, 64(2), 97–107. https://doi.org/10.1198/tast.2010.09132
   Web of Science ®Google Scholar
• Pruim, R., Kaplan, D. T., & Horton, N. J. (2017). The mosaic package: Helping students to ’think with data’ using R. The R Journal, 9(1), 77–102. https://journal.r-project.org/archive/2017/RJ-2017-024/index.html
   Google Scholar
• R Core Team. (2017). R: A language and environment for statistical computing. https://www.R-project.org/
   Google Scholar
• realtime. (n.d.). JamaicaVM 3.4 — User documentation: The virtual machine for realtime and embedded systems. http://aicas.com/jamaica/3.4/doc/html/x3718.html
   Google Scholar
• RStudio Team. (2018). RStudio: Integrated development environment for R. http://www.rstudio.com/
   Google Scholar
• Schloerke, B., Allaire, J., & Borges, B. (2018). Learnr: Interactive tutorials for R. https://CRAN.R-project.org/package=learnr
   Google Scholar
• Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495. https://doi.org/10.1007/s10639-016-9482-0
   Web of Science ®Google Scholar
• Sweller, J., Van Merriënboer, J. J. G., & Paas, F. G. W. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
   Web of Science ®Google Scholar
• Weiland, T. (2017). The importance of context in task selection. Teaching Statistics, 39(1), 20–25. https://doi.org/10.1111/test.12116
   Web of Science ®Google Scholar
• Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
   Web of Science ®Google Scholar
• Wickham, H. (2017). Tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse
   Google Scholar
• Wild, C. J., & Halstead, B. (n.d.). VIT online. https://www.stat.auckland.ac.nz/~wild/VITonline/
   Google Scholar
• Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x
   Web of Science ®Google Scholar
• Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
   Web of Science ®Google Scholar
• Wood, M. (2005). The role of simulation approaches in statistics. Journal of Statistics Education, 13(3). https://doi.org/10.1080/10691898.2005.11910562
   Google Scholar
• Wouters, P., Paas, F., & Van Merriënboer, J. J. (2008). How to optimize learning from animated models: A review of guidelines based on cognitive load. Review of Educational Research, 78(3), 645–675. https://doi.org/10.3102/0034654308320320
   Web of Science ®Google Scholar