How Students Use Statistical Computing in Problem Solving

References

• Baglin, J. (2013), “Applying a Theoretical Model for Explaining the Development of Technological Skills in Statistics Education,” Technology Innovations in Statistics Education, 7.
   Google Scholar
• Bargagliotti, A., Binder, W., Blakesley, L., Eusufzai, Z., Fitzpatrick, B., Ford, M., Huchting, K., Larson, S., Miric, N., Rovetti, R., Seal, K., and Zachariah, T. (2020), “Undergraduate Learning Outcomes for Achieving Data Acumen,” Journal of Statistics Education, 28, 197–211. DOI: 10.1080/10691898.2020.1776653.
   Web of Science ®Google Scholar
• Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., and Horton, N. J. (2014), “R Markdown: Integrating a Reproducible Analysis Tool Into Introductory Statistics,” Technology Innovations in Statistics Education, arXiv:1402.1894.
   Google Scholar
• Ben-Zvi, D. (2000), “Toward Understanding the Role of Technological Tools in Statistical Learning,” Mathematical Thinking and Learning, 2, 127–155. DOI: 10.1207/S15327833MTL0202_6.
   Google Scholar
• Ben-Zvi, D. (2006), “Scaffolding Students’ Informal Inference and Argumentation,” in Proceedings of the Seventh International Conference on Teaching Statistics, pp. 1–6.
   Google Scholar
• Biehler, R., Ben-Zvi, D., Bakker, A., and Makar, K. (2012), “Technology for Enhancing Statistical Reasoning at the School Level,” in Third International Handbook of Mathematics Education, New York: Springer, pp. 643–689.
   Google Scholar
• Brown, E. N., and Kass, R. E. (2009), “Statistical Training and Curricular Revision,” The American Statistician, 63, 105–110. DOI: 10.1198/tast.2009.0019.
   Web of Science ®Google Scholar
• Chance, B. (1997), “Experiences With Authentic Assessment Techniques in an Introductory Statistics Course,” Journal of Statistics Education, 5. DOI: 10.1080/10691898.1997.11910596.
   Google Scholar
• Chance, B., Ben-Zvi, D., Garfield, J., and Medina, E. (2007), “The Role of Technology in Improving Student Learning of Statistics,” Technology Innovations in Statistics Education, 1, 1–26.
   Google Scholar
• Chance, B., Cohen, S., Grimshaw, S., Hardin, J., Hesterberg, T., Hoerl, R., Horton, N., Malone, C., Nichols, R., and Nolan, D. (2014), Curriculum Guidelines for Undergraduate Programs in Statistical Science, Alexandria, VA: American Statistical Association.
   Google Scholar
• delMas, R. C., Garfield, J., and Chance, B. L. (1999), “A Model of Classroom Research in Action: Developing Simulation Activities to Improve Student’s Statistical Reasoning,” Journal of Statistics Education, 7.
   Google Scholar
• delMas, R. C., Garfield, J., and Zieffler, A. (2014), “Using TinkerPlots to Develop Tertiary Students’ Statistical Thinking in a Modeling-Based Introductory Statistics Class,” in Mit Werkzeugen Mathematik und Stochastik lernen—Using Tools for Learning Mathematics and Statistics, eds. T. Wassong, D. Frischemeier, P. R. Fischer, R. Hochmuth, and P. Bender, Wiesbaden: Springer Spektrum, pp. 405–420.
   Google Scholar
• Garfield, J., delMas, R., and Zieffler, A. (2012), “Developing Statistical Modelers and Thinkers in an Introductory, Tertiary-Level Statistics Course,” ZDM, 44, 883–898. DOI: 10.1007/s11858-012-0447-5.
   Google Scholar
• Holcomb, J., Chance, B., Tietjen, A. R., and Cobb, G. (2010), “Introducing Concepts of Statistical Inference via Randomization Tests,” in Proceedings of the Eighth International Conference on Teaching Statistics, Voorberg: International Statistical Institute.
   Google Scholar
• Horton, N. J., Baumer, B. S., and Wickham, H. (2014), “Teaching Precursors to Data Science in Introductory and Secondary Courses in Statistics,” in Proceedings of the Ninth International Conference on Teaching Statistics, Flagstaff, AZ.
   Google Scholar
• International Society for Technology in Education; Computer Science Teachers Association (2011), Operational Definition of Computational Thinking, National Science Foundation, available at http://csta.acm.org.
   Google Scholar
• Kalyuga, S., and Liu, T.-C. (2015), “Managing Cognitive Load in Technology-Based Learning Environments,” Journal of Educational Technology and Society, 18, 1–8.
   Google Scholar
• Lane-Getaz, S. J. (2010), “Linking the Randomization Test to Reasoning About p-Values and Statistical Significance,” in Eighth International Conference on Teaching Statistics, Ljubjana, Slovenia; Voorburg, The Netherlands.
   Google Scholar
• Lee, H. S., and Hollebrands, K. F. (2006), “Students’ Use of Technological Features While Solving a Mathematics Problem,” The Journal of Mathematical Behavior, 25, 252–266. DOI: 10.1016/j.jmathb.2006.09.005.
   Google Scholar
• Lee, H. S., Kersaint, G., Harper, S. R., Driskell, S. O., Jones, D. L., Leatham, K. R., Angotti, R. L., and Adu-Gyamfi, K. (2014), “Teachers’ Use of Transnumeration in Solving Statistical Tasks With Dynamic Statistical Software,” Statistics Education Research Journal, 13, 25–52.
   Google Scholar
• McNamara, A. (2016), “On the State of Computing in Statistics Education: Tools for Learning and Doing,” arXiv no. 1610.00984.
   Google Scholar
• Moore, D. S. (1997), “New Pedagogy and New Content: The Case of Statistics,” International Statistical Review, 65, 123–137. DOI: 10.1111/j.1751-5823.1997.tb00390.x.
   Web of Science ®Google Scholar
• Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107. DOI: 10.1198/tast.2010.09132.
   Web of Science ®Google Scholar
• Peltsverger, S., and Debnath, S. (2019), “Instructional Pseudocode Guide to Teach Problem-Solving,” in Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, p. 319.
   Google Scholar
• Pruim, R., Kaplan, D., and Horton, N. (2014), “Mosaic: Project MOSAIC (mosaic-web.org) Statistics and Mathematics Teaching Utilities,” R Package Version 0.9-1-3, available at http://CRAN.R-project.org/package=mosaic.
   Google Scholar
• RStudio Team. (2015), RStudio: Integrated Development for R, Boston, MA: RStudio, Inc., available at http://www.rstudio.com/.
   Google Scholar
• Schoenfeld, A. H. (1985), Mathematical Problem Solving, Orlando, FL: Academic Press.
   Google Scholar
• Tintle, N., VanderStoep, J., Holmes, V.-L., Quisenberry, B., and Swanson, T. (2011), “Development and Assessment of a Preliminary Randomization-Based Introductory Statistics Curriculum,” Journal of Statistics Education, 19. DOI: 10.1080/10691898.2011.11889599.
   Google Scholar
• Wing, J. M. (2006), “Computational Thinking,” Communications of the ACM, 49, 33–35. DOI: 10.1145/1118178.1118215.
   Web of Science ®Google Scholar
• Witmer, J. A. (2016), “Concept Maps in Introductory Statistics,” Teaching Statistics, 38, 4–7. DOI: 10.1111/test.12083.
   Web of Science ®Google Scholar
• Wolfram, S. (2016), “How to Teach Computational Thinking,” available at http://blog.stephenwolfram.com/2016/09/how-to-teach-computational-thinking/.
   Google Scholar