References
- Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., and Iannone, R. (2021), rmarkdown: Dynamic Documents for R.
- Burgess, A. W., McGregor, D. M., and Mellis, C. M. (2014), “Applying Established Guidelines to Team-Based Learning Programs in Medical Schools: A Systematic Review,” Academic Medicine, 89, 678–688. DOI: https://doi.org/10.1097/ACM.0000000000000162.
- Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., Rossman, A., Roswell, G., Velleman, P., Witmer, J., and Wood, B. (2016), “Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016,” Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016.
- Çetinkaya-Rundel, M. (2021), “Data Science in a Box,” Data Science in a Box. Available at https://datasciencebox.org/.
- Çetinkaya-Rundel, M., and Ellison, V. (2021), “A Fresh Look at Introductory Data Science,” Journal of Statistics and Data Science Education, 29, S16–S26. DOI: https://doi.org/10.1080/10691898.2020.1804497.
- Cobb, G. (1992), “Teaching Statistics,” in Heeding the Call for Change: Suggestions for Curricular Action, MAA Notes No. 22, Washington, DC, pp. 3–43.
- Collins, C. M., Carrasco, G. A., and Lopez, O. J. (2019), “Participation in Active Learning Correlates to Higher Female Performance in a Pipeline Course for Underrepresented Students in Medicine,” Medical Science Educator, 29, 1175–1178. DOI: https://doi.org/10.1007/s40670-019-00794-2.
- Cooperstein, S. E., and KocevarWeidinger, E. (2004), “Beyond Active Learning: A Constructivist Approach to Learning,” Reference Services Review, 32, 141–148. DOI: https://doi.org/10.1108/00907320410537658.
- Davidson, M. A., Dewey, C. M., and Fleming, A. E. (2019), “Teaching Communication in a Statistical Collaboration Course: A Feasible, Project-Based, Multimodal Curriculum,” The American Statistician, 73, 61–69. DOI: https://doi.org/10.1080/00031305.2018.1448890.
- 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,” Annual Review of Statistics and Its Application, 4, 15–30. DOI: https://doi.org/10.1146/annurev-statistics-060116-053930.
- Epstein, M. L., Lazarus, A. D., Calvano, T. B., Matthews, K. A., Hendel, R. A., Epstein, B. B., and Brosvic, G. M. (2002), “Immediate Feedback Assessment Technique Promotes Learning and Corrects Inaccurate First Responses,” The Psychological Record, 52, 187–201. DOI: https://doi.org/10.1007/BF03395423.
- Faculty Innovation Center. (2012), Team-Based Learning: Group Work That Works, Team-Based Learning, The University of Texas at Austin.
- Farmus, L., Cribbie, R. A., and Rotondi, M. A. (2020), “The Flipped Classroom in Introductory Statistics: Early Evidence From a Systematic Review and Meta-Analysis,” Journal of Statistics Education, 28, 316–325. DOI: https://doi.org/10.1080/10691898.2020.1834475.
- Fosnot, C. T. (2013), Constructivism: Theory, Perspectives, and Practice (2nd ed.), Teachers College Press.
- Foster, E. D., and Deardorff, A. (2017), “Open Science Framework (OSF),” Journal of the Medical Library Association, 105, 203–206. DOI: https://doi.org/10.5195/jmla.2017.88.
- Garfield, J. (1993), “Teaching Statistics Using Small-Group Cooperative Learning,” Journal of Statistics Education, 1, 1–9. DOI: https://doi.org/10.1080/10691898.1993.11910455.
- Garfield, J., and BenZvi, D. (2007), “How Students Learn Statistics Revisited: A Current Review of Research on Teaching and Learning Statistics,” International Statistical Review, 75, 372–396. DOI: https://doi.org/10.1111/j.1751-5823.2007.00029.x.
- Haidet, P., Levine, R. E., Parmelee, D. X., Crow, S., Kennedy, F., Kelly, P. A., Perkowski, L., Michaelsen, L., and Richards, B. F. (2012), “Perspective: Guidelines for Reporting Team-Based Learning Activities in the Medical and Health Sciences Education Literature,” Academic Medicine, 87, 292–299. DOI: https://doi.org/10.1097/ACM.0b013e318244759e.
- 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: https://doi.org/10.1080/00031305.2015.1077729.
- Harris, H., Murphy, S., and Vaisman, M. (2013), Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work, Sepastopol, CA: O’Reilly Media, Inc.
- Henry, L., and Wickham, H. (2020), purrr: Functional Programming Tools. R package version 0.3.4. Available at https://CRAN.R-project.org/package=purrr
- Hettler, P. L. (2015), “Student Demographics and the Impact of Team-Based Learning,” International Advances in Economic Research, 21, 413–422. DOI: https://doi.org/10.1007/s11294-015-9539-7.
- Kalaian, S. A., and Kasim, R. M. (2014), “A Meta-Analytic Review of Studies of the Effectiveness of Small-Group Learning Methods on Statistics Achievement,” Journal of Statistics Education, 22, null. DOI: https://doi.org/10.1080/10691898.2014.11889691.
- Kolaczyk, E. D., Wright, H., and Yajima, M. (2021), “Statistics Practicum: Placing ‘Practice’ at the Center of Data Science Education,” Harvard Data Science Review. DOI: https://doi.org/10.1162/99608f92.2d65fc70.
- Koles, P. G., Stolfi, A., Borges, N. J., Nelson, S., and Parmelee, D. X. (2010), “The Impact of Team-Based Learning on Medical Students’ Academic Performance,” Academic Medicine, 85, 1739–1745. DOI: https://doi.org/10.1097/ACM.0b013e3181f52bed.
- Kopf, S., Tamanaha, J., and Vance, E. (2019), tbltools, R, Kopf Lab at the University of Colorado Boulder.
- Lasserre, P., and Szostak, C. (2011), “Effects of Team-Based Learning on a CS1 Course,” in Proceedings of the 16th annual joint conference on Innovation and Technology in Computer Science Education (ITiCSE ’11), New York, NY, USA: Association for Computing Machinery, pp. 133–137. DOI: https://doi.org/10.1145/1999747.1999787.
- Macke, C., Canfield, J., Tapp, K., and Hunn, V. (2019), “Outcomes for Black Students in Team-Based Learning Courses,” Journal of Black Studies, 50, 66–86. DOI: https://doi.org/10.1177/0021934718810124.
- Michaelsen, L. K., and Fink, L. D. (2002), “Calculating peer evaluation scores,” in Team-Based Learning: A Transformative Use of Small Groups, Westport, Conn: Praeger, pp. 233–244.
- Michaelsen, L. K., Fink, L. D., and Knight, A. (1997), “Designing Effective Group Activities: Lessons for Classroom Teaching and Faculty Development,” To Improve the Academy, 16, 373–397. DOI: https://doi.org/10.1002/j.2334-4822.1997.tb00335.x.
- Michaelsen, L. K., Knight, A. B., and Fink, L. D. (2004), Team-Based Learning: A Transformative Use of Small Groups in College Teaching, Sterling, VA: Stylus.
- Michaelsen, L. K., and Sweet, M. (2008), “The Essential Elements of Team-Based Learning,” New Directions for Teaching and Learning, 2008, 7–27. DOI: https://doi.org/10.1002/tl.330.
- Michaelsen, L., and Richards, B. (2005), “COMMENTARY: Drawing Conclusions from the Team-Learning Literature in Health-Sciences Education: A Commentary,” Teaching and Learning in Medicine, 17, 85–88. DOI: https://doi.org/10.1207/s15328015tlm1701_15.
- National Academies of Sciences, Engineering, and Medicine. (2018), Data Science for Undergraduates: Opportunities and Options, Washington, DC: The National Academies Press.
- Nolan, D., and Lang, D. T. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107. DOI: https://doi.org/10.1198/tast.2010.09132.
- Parmelee, D., Michaelsen, L. K., Cook, S., and Hudes, P. D. (2012), “Team-based learning: A practical guide: AMEE Guide No. 65,” Medical Teacher, 34, e275–e287. DOI: https://doi.org/10.3109/0142159X.2012.651179.
- Paterson, J., and Sneddon, J. (2011), “Conversations About Curriculum Change: Mathematical Thinking and Team-Based Learning in a Discrete Mathematics Course,” International Journal of Mathematical Education in Science and Technology, 42, 879–889. DOI: https://doi.org/10.1080/0020739X.2011.613487.
- Peters, T., Johnston, E., Bolles, H., Ogilvie, C., Knaub, A., and Holme, T. (2020), “Benefits to Students of Team-Based Learning in Large Enrollment Calculus,” PRIMUS, 30, 211–229. DOI: https://doi.org/10.1080/10511970.2018.1542417.
- R Core Team. (2021), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
- Roseth, C. J., Garfield, J. B., and Ben-Zvi, D. (2008), “Collaboration in Learning and Teaching Statistics,” Journal of Statistics Education, 16, 1–15. DOI: https://doi.org/10.1080/10691898.2008.11889557.
- RStudio Team. (2021), RStudio: Integrated Development Environment for R, Boston, MA: RStudio, PBC.
- Sharp, J. L., Griffith, E. H., and Higgs, M. D. (2021), “Setting the Stage: Statistical Collaboration Videos for Training the Next Generation of Applied Statisticians,” Journal of Statistics and Data Science Education, 29, 165–170. DOI: https://doi.org/10.1080/26939169.2021.1934202.
- Sibley, J., and Spiridonoff, S. (2014), Introduction to Team-Based Learning, Vancouver, BC: University of British Columbia Faculty of Applied Science: Centre for Instructional Support.
- St. Clair, K., and Chihara, L. (2012), “Team-Based Learning in a Statistical Literacy Class,” Journal of Statistics Education, 20. DOI: https://doi.org/10.1080/10691898.2012.11889633.
- Stonewall, J., Dorneich, M., Dorius, C., and Rongerude, J. (2018), “A Review of Bias in Peer Assessment,” in Paper presented at 2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference, Crystal City, Virginia. Available at https://peer.asee.org/29510.
- Thabane, L., Walter, S. D., Hanna, S., Goldsmith, C. H., and Pullenayegum, E. (2008), “Developing a Biostatistical Collaboration Course in a Health Science Research Methodology Program,” Journal of Statistics Education, 16, 1–16. DOI: https://doi.org/10.1080/10691898.2008.11889564.
- Thomas, P. A., and Bowen, C. W. (2011), “A Controlled Trial of Team-Based Learning in an Ambulatory Medicine Clerkship for Medical Students,” Teaching and Learning in Medicine, 23, 31–36. DOI: https://doi.org/10.1080/10401334.2011.536888.
- Vance, E. (2013), “Using Team-Based Learning to Teach Effective Communication and Collaboration,” in 5th Annual Conference on Higher Education Pedagogy, Blacksburg, VA: Virginia Tech’s Center for Instructional Development and Educational Research, pp. 296–297.
- Vance, E. A. (2015), “Recent Developments and Their Implications for the Future of Academic Statistical Consulting Centers,” The American Statistician, 69, 127–137. DOI: https://doi.org/10.1080/00031305.2015.1033990.
- Vance, E. A. (2021), “Teaching Introductory Data Science with TBL,” Open Science Framework. Available at https://osf.io/569qy/.
- Vance, E. A., Alzen, J. L., and Seref, M. M. H. (2020), “Assessing Statistical Consultations and Collaborations,” in JSM Proceedings, Alexandria, VA: American Statistical Association: Statistical Consulting Section, pp. 161–169.
- Vance, E. A., and Smith, H. S. (2019), “The ASCCR Frame for Learning Essential Collaboration Skills,” Journal of Statistics Education, 27, 265–274. DOI: https://doi.org/10.1080/10691898.2019.1687370.
- Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag.
- Wickham, H. (2020), modelr: Modelling Functions that Work with the Pipe. R package version 0.1.8. Available at https://CRAN.R-project.org/package=modelr.
- 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: https://doi.org/10.21105/joss.01686.
- Wickham, H., and Grolemund, G. (2016), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Sebastopol, CA: O’Reilly Media, Inc.
- Winquist, J. R., and Carlson, K. A. (2014), “Flipped Statistics Class Results: Better Performance Than Lecture Over One Year Later,” Journal of Statistics Education, 22, 1–10. DOI: https://doi.org/10.1080/10691898.2014.11889717.
- Young-Saver, D. (2021), “Skew The Script: A Website Offering Socially Relevant Math Lessons,” Statistics Teacher, Available at https://www.statisticsteacher.org/2021/04/12/skew-the-script/.