3,518
Views
4
CrossRef citations to date
0
Altmetric
Articles

Teaching Statistical Concepts and Modern Data Analysis With a Computing-Integrated Learning Environment

, &
Pages S61-S73 | Published online: 22 Mar 2021

References

  • ACM Data Science Task Force (2019), “Computing Competencies for Undergraduate Data Science Curricula,” available at http://www.cs.williams.edu/∼andrea/DSTF/DSReportInitialFull.pdf.
  • Anderson, R. J., Anderson, R., Vandegrift, T., Wolfman, S., and Yasuhara, K. (2003), “Promoting Interaction in Large Classes With Computer-Mediated Feedback,” in Designing for Change in Networked Learning Environments, eds. B. Wasson, S. Ludvigsen, and U. Hoppe, Dordrecht: Springer, pp. 119–123.
  • Barry, D. (2005), “A Conversation With John Hartigan,” Statistical Science, 20, 418–430, DOI: 10.1214/088342304000000242.
  • Baumer, B., Çetinkaya-Rundel, M., Bray, A., Loi, L., and Horton, N. J. (2014), “R Markdown: Integrating a Reproducible Analysis Tool,” Technology Innovations in Statistics Education, 8, available at https://escholarship.org/uc/item/90b2f5xh.
  • Cobb, G. (2015), “Mere Renovation Is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum From the Ground Up,” American Statistician, 69, 266–282, DOI: 10.1080/00031305.2015.1093029.
  • Crouch, C. H., and Mazur, E. (2001), “Peer Instruction: Ten Years of Experience and Results,” American Journal of Physics, 69, 970–977, DOI: 10.1119/1.1374249.
  • De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., Bryant, L., Cheng, L. Z., Francis, A., Gould, R., Kim, A. Y., Kretchmar, M., Lu, Q., Moskol, A., Nolan, D., Pelayo, R., Raleigh, S., Sethi, R. J., Sondjaja, M., Tiruviluamala, N., Uhlig, P. X., Washington, T. M., Wesley, C. L., White, D., and Ye, P. (2017), “Curriculum Guidelines for Undergraduate Programs in Data Science,” The Annual Review of Statistics and Its Application, 4, 15–30, DOI: 10.1146/annurev-statistics-060116-053930.
  • Dillenbourg, P., Schneider, D., and Synteta, P. (2002), “Virtual Learning Environments,” in 3rd Hellenic Conference “Information & Communication Technologies in Education”, ed. A. Dimitracopoulou, Rhodes: Kastaniotis Editions, pp. 3–18.
  • Erickson, T., Wilkerson, M., Finzer, W., and Reichsman, F. (2019), “Data Moves,” Technology Innovations in Statistics Education, 12, available at https://escholarship.org/uc/item/0mg8m7g6.
  • Finzer, W. F. (2000), “Design of FathomTM, a Dynamic StatisticsTM Environment, for the Teaching of Mathematics,” in International Conference on Mathematics Education.
  • Forbes, S., Chapman, J., Harraway, J., Stirling, D., and Wild, C. (2014), “Use of Data Visualisation in the Teaching of Statistics: A New Zealand Perspective,” Statistics Education Research Journal, 13, 187–201.
  • GAISE College Report ASA Revision Committee (2016), “Guidelines for Assessment and Instruction in Statistics Education College Report,” available at http://www.amstat.org/education/gaise.
  • Gelman, A., and Nolan, D. (2002), Teaching Statistics: A Bag of Tricks, Oxford: OUP.
  • Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Lang, D. T., and Ward, M. D. (2015), “Data Science in Statistics Curricula: Preparing Students to ‘Think With Data’,” The American Statistician, 69, 343–353, DOI: 10.1080/00031305.2015.1077729.
  • Horton, N. J. (2015), “Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward,” American Statistician, 69, 138–145, DOI: 10.1080/00031305.2015.1032435.
  • Khachatryan, D., and Karst, N. (2017), “V for Voice: Strategies for Bolstering Communication Skills in Statistics,” Journal of Statistics Education, 25, 68–78, DOI: 10.1080/10691898.2017.1305261.
  • Kiewra, K. A., DuBois, N. F., Christian, D., and McShane, A. (1988), “Providing Study Notes: Comparison of Three Types of Notes for Review,” Journal of Educational Psychology, 80, 595–597, DOI: 10.1037/0022-0663.80.4.595.
  • MacQueen, J. (1967), “Some Methods for Classification and Analysis of Multivariate Observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability.
  • McLuhan, M. (1960), “Classroom Without Walls,” in Explorations in Communication: An Anthology, eds. E. Carpenter and M. McLuhan, Boston: Beacon Press, pp. 1–3.
  • McNamara, A. (2019), “Key Attributes of a Modern Statistical Computing Tool,” American Statistician, 73, 375–384, DOI: 10.1080/00031305.2018.1482784.
  • National Academies of Sciences, Engineering, and Medicine (2018), Data Science for Undergraduates: Opportunities and Options, Washington, DC: The National Academies Press.
  • Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107, DOI: 10.1198/tast.2010.09132.
  • Ooms, J. (2014), “The OpenCPU System: Towards a Universal Interface for Scientific Computing Through Separation of Concerns,” arXiv no. 1406.4806.
  • Pfannkuch, M., Regan, M., Wild, C., and Horton, N. J. (2010), “Telling Data Stories: Essential Dialogues for Comparative Reasoning,” Journal of Statistics Education, 18, DOI: 10.1080/10691898.2010.11889479.
  • Pruim, R., Kaplan, D. T., and Horton, N. J. (2017), “The Mosaic Package: Helping Students to ‘Think With Data’ Using R,” R Journal, 9, 77, DOI: 10.32614/RJ-2017-024.
  • Roschelle, J., Penuel, W. R., and Abrahamson, L. (2004), “Classroom Response and Communication Systems: Research Review and Theory,” in Annual Meeting of the American Educational Research Association, pp. 1–8, available at http://humansphere.com.sg/pdf/an/ClassroomResponseandCommunicationSystems.pdf.
  • Roseth, C. J., Garfield, J. B., and Ben-Zvi, D. (2008), “Collaboration in Learning and Teaching Statistics,” Journal of Statistics Education, 16, DOI: 10.1080/10691898.2008.11889557.
  • RStudio Team (2020), RStudio: Integrated Development Environment for R, Boston, MA: RStudio, PBC.
  • Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016), “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, 3, 637–646, DOI: 10.1109/JIOT.2016.2579198.
  • Silberzahn, R., Uhlmann, E. L., Martin, D. P., Anselmi, P., Aust, F., Awtrey, E., Bahník, Š., Bai, F., Bannard, C., Bonnier, E., Carlsson, R., Cheung, F., Christensen, G., Clay, R., Craig, M. A., Dalla Rosa, A., Dam, L., Evans, M. H., Flores Cervantes, I., Fong, N., Gamez-Djokic, M., Glenz, A., Gordon-McKeon, S., Heaton, T. J., Hederos, K., Heene, M., Hofelich Mohr, A. J., Högden, F., Hui, K., Johannesson, M., Kalodimos, J., Kaszubowski, E., Kennedy, D. M., Lei, R., Lindsay, T. A., Liverani, S., Madan, C. R., Molden, D., Molleman, E., Morey, R. D., Mulder, L. B., Nijstad, B. R., Pope, N. G., Pope, B., Prenoveau, J. M., Rink, F., Robusto, E., Roderique, H., Sandberg, A., Schlüter, E., Schönbrodt, F. D., Sherman, M. F., Sommer, S. A., Sotak, K., Spain, S., Spörlein, C., Stafford, T., Stefanutti, L., Tauber, S., Ullrich, J., Vianello, M., Wagenmakers, E.-J., Witkowiak, M., Yoon, S., and Nosek, B. A. (2018), “Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results,” Advances in Methods and Practices in Psychological Science, 1, 337–356, DOI: 10.1177/2515245917747646.
  • Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H. (2019), “Welcome to the Tidyverse,” Journal of Open Source Software, 4, 1686, DOI: 10.21105/joss.01686.