References

Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the united states, 2008. ERIC.

Bambrick-Santoyo, P. (2010). Driven by data: A practical guide to improve instruction. John Wiley & Sons.

Bates, D., Maechler, M., Bolker, B., & Walker, S. (2020). Lme4: Linear mixed-effects models using ’eigen’ and s4. https://CRAN.R-project.org/package=lme4

Betebenner, D. W. (2019). RandomNames: Generate random given and surnames. https://CRAN.R-project.org/package=randomNames

Bransford, J. D., Brown, A. L., Cocking, R. R., & others. (2000). How people learn (Vol. 11). Washington, DC: National academy press.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Bryan, J. (2017). Project-oriented workflow. https://www.tidyverse.org/blog/2017/12/workflow-vs-script/

Bryan, J. (2019). Reproducible examples and the ‘reprex‘ package. https://community.rstudio.com/t/video-reproducible-examples-and-the-reprex-package/14732

Bryan, J. (2020). Happy git with r. https://happygitwithr.com/

Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How america’s schools can get better at getting better. Harvard Education Press.

Campaign, D. Q. (2018). Teachers see the power of data - but don’t have the time to use it. https://dataqualitycampaign.org/wp-content/uploads/2018/09/DQC_DataEmpowers-Infographic.pdf

Conway, D. (2010). The data science venn diagram. Drew Conway, 10. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

Datnow, A., & Hubbard, L. (2015). Teachers’ use of assessment data to inform instruction: Lessons from the past and prospects for the future. Teachers College Record, 117(4), n4.

Dirksen, J. (2015). Design for how people learn. New Riders.

Dweck, C. (2015). Carol dweck revisits the growth mindset. Education Week, 35(5), 20–24.

Education Statistics U.S. Department of Education, N. C. for. (2019). Concentration of public school students eligible for free or reduced-price lunch. The Condition of Education 2019. https://nces.ed.gov/fastfacts/display.asp?id=898

Elbers, B. (2020). Tidylog: Logging for ’dplyr’ and ’tidyr’ functions. https://CRAN.R-project.org/package=tidylog

Emdin, C. (2016). For white folks who teach in the hood... And the rest of y’all too: Reality pedagogy and urban education. Beacon Press.

Estrellado, R. A., Bovee, E. A., Motsipak, J., Rosenberg, J. M., & Vel’asquez, I. C. (2019). Taylor and francis book proposal for data science in education. https://github.com/data-edu/DSIEUR_support_files/blob/master/planning/T%26F%20Book%20Proposal%20for%20Data%20Science%20in%20Education.docx

Estrellado, R., Bovee, E., Mostipak, J., Rosenberg, J., & Vel’asquez, I. (2020). Dataedu: Package for data science in education using r. https://github.com/data-edu/dataedu

Firke, S. (2020). Janitor: Simple tools for examining and cleaning dirty data. https://CRAN.R-project.org/package=janitor

for Education Statistics, N. C. (2018). Public elementary/secondary school universe survey. https://nces.ed.gov/programs/digest/d17/tables/dt17_204.10.asp?current=yes

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.

Grimm, K. J., Ram, N., & Estabrook, R. (2016). Growth modeling: Structural equation and multilevel modeling approaches. Guilford Publications.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.

Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge.

Healy, K. (2019). Data visualization: A practical introduction. Princeton University Press.

Heath, C. H. D. (2006). The curse of knowledge. Harvard Business Review. https://hbr.org/2006/12/the-curse-of-knowledge

Hill, A. (2017). Up and running with blogdown. https://alison.rbind.io/post/2017-06-12-up-and-running-with-blogdown/

Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261–266.

Ismay, C., & Kim, A. Y. (2019). Statistical inference via data science. CRC Press.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer.

Jarvis, C. (2019). Creating calling. HarperCollins.

Jordan, R. (2015). High-poverty schools undermine education for children of color. https://www.urban.org/urban-wire/high-poverty-schools-undermine-education-children-color

Kahneman, D. (2011). Thinking fast and slow.

Kearney, M. W. (2016). Rtweet: Collecting twitter data. Comprehensive R Archive Network. Available at: Https://Cran. R-Project. Org/Package= Rtweet.

Kearney, M. W. (2020). Rtweet: Collecting twitter data. https://CRAN.R-project.org/package=rtweet

Kleon, A. (2012). Steal like an artist: 10 things nobody told you about being creative. Workman Publishing.

Kozol, J. (2012). Savage inequalities: Children in america’s schools. Broadway Books.

Krist, C., Schwarz, C. V., & Reiser, B. J. (2019). Identifying essential epistemic heuristics for guiding mechanistic reasoning in science learning. Journal of the Learning Sciences, 28(2), 160–205.

Kuhn, M. (2020). Caret: Classification and regression training. https://CRAN.R-project.org/package=caret

Kuhn, M., & others. (2008). Building predictive models in r using the caret package. Journal of Statistical Software, 28(5), 1–26.

Kurz, S. (2019). Statistical rethinking with brms, ggplot2, and the tidyverse. https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/

Lee, V. R., & Wilkerson, M. H. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects. https://pdfs.semanticscholar.org/811d/3e7bbbea05a8954c09823629e81819554382.pdf?_ga=2.195337642.763980897.1582512794-1526781779.1582512794

Lehrer, R., Kim, M.-j., & Schauble, L. (2007). Supporting the development of conceptions of statistics by engaging students in measuring and modeling variability. International Journal of Computers for Mathematical Learning, 12(3), 195–216.

Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. Handbook of Child Psychology and Developmental Science, 1–44.

Lemov, D. (2015). Teach like a champion 2.0: 62 techniques that put students on the path to college. John Wiley & Sons.

Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive analysis in education: A guide for researchers. https://ies.ed.gov/ncee/pubs/20174023/pdf/20174023.pdf) (NCEE 2017–4023

Lüdecke, D. (2020). SjPlot: Data visualization for statistics in social science. https://CRAN.R-project.org/package=sjPlot

Lüdecke, D., Makowski, D., Waggoner, P., & Patil, I. (2020). Performance: Assessment of regression models performance. https://CRAN.R-project.org/package=performance

Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37.

McTighe, J., & Willis, J. (2019). Upgrade your teaching: Understanding by design meets neuroscience. ASCD.

Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465.

Moore Jr, E., Michael, A., & Penick-Parks, M. W. (2017). The guide for white women who teach black boys. Corwin Press.

Murphy, M. (2011). The adventures of spielberg: An interview. https://carpetbagger.blogs.nytimes.com/2011/12/20/the-adventures-of-spielberg-an-interview/

National Academies of Sciences, E., Medicine, & others. (2018). How people learn ii: Learners, contexts, and cultures. National Academies Press.

Navarro, D. (2020). Learning statistics with r. https://learningstatisticswithr.com/

Newton, E. L. (1991). The rocky road from actions to intentions [PhD thesis]. Stanford University.

of Education Reform, T. G. (2015). Student subgroup. https://www.edglossary.org/student-subgroup

on Education Statistics., N. F. (2016). Forum guide to collecting and using disaggregated data on racial/ethnic subgroups. https://nces.ed.gov/pubs2017/NFES2017017.pdf

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.

Pedersen, T. L. (2020a). Ggraph: An implementation of grammar of graphics for graphs and networks. https://CRAN.R-project.org/package=ggraph

Pedersen, T. L. (2020b). Tidygraph: A tidy api for graph manipulation. https://CRAN.R-project.org/package=tidygraph

Peng, R. D., & Matsui, E. (2015). The art of data science. A Guide for Anyone Who Works with Data. Skybrude Consulting, LLC.

R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Robinson, D. (2018). Advice to aspiring data scientists: Start a blog.

Robinson, D., & Silge, J. (2020). Tidytext: Text mining using ’dplyr’, ’ggplot2’, and other tidy tools. https://CRAN.R-project.org/package=tidytext

Rosenberg, J. M., Greenhalgh, S. P., Koehler, M. J., Hamilton, E. R., & Akcaoglu, M. (2016). An investigation of state educational twitter hashtags (seths) as affinity spaces. E-Learning and Digital Media, 13(1-2), 24–44.

Rosenberg, J. M., Lawson, M., Anderson, D., Rutherford, T., & Jones, R. S. (2020). Making data science “count”: Data science and learning, design, and technology research. Research Methods in Learning Design & Technology., 1–13.

RStudio Team. (2015). RStudio: Integrated development environment for r. RStudio, Inc. http://www.rstudio.com/

Schools, G. (n.d.). Aggregate data. In Ed Glossary. https://www.edglossary.org/aggregate-data/

Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254.

Silge, J., & Robinson, D. (2017). Text mining with r: A tidy approach. " O’Reilly Media, Inc.".

Snyder, T., & Musu-Gillette, L. (2015). Free or reduced price lunch: A proxy for poverty? https://nces.ed.gov/blogs/nces/post/free-or-reduced-price-lunch-a-proxy-for-poverty

Spillane, J. P., Kim, C. M., & Frank, K. A. (2012). Instructional advice and information providing and receiving behavior in elementary schools: Exploring tie formation as a building block in social capital development. American Educational Research Journal, 49(6), 1112–1145.

Stanley, D. (2018). ApaTables: Create american psychological association (apa) style tables. https://CRAN.R-project.org/package=apaTables

Trust, T., Krutka, D. G., & Carpenter, J. P. (2016). “Together we are better”: Professional learning networks for teachers. Computers & Education, 102, 15–34.

Victore, J. (2019). Feck perfuction: Dangerous ideas on the business of life. Chronicle books.

West, B. T., Welch, K. B., & Galecki, A. T. (2014). Linear mixed models: A practical guide using statistical software. CRC Press.

Wickham, H. (2015). R packages. O’Reilly Media.

Wickham, H. (2019a). Advanced r (2nd ed.). https://adv-r.hadley.nz/

Wickham, H. (2019b). Tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., & others. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.

Wickham, H., & Bryan, J. (2019). Readxl: Read excel files. https://CRAN.R-project.org/package=readxl

Wickham, H., François, R., Henry, L., & Müller, K. (2020). Dplyr: A grammar of data manipulation. https://CRAN.R-project.org/package=dplyr

Wickham, H., & Grolemund, G. (2018). R for data science. O’Reilly.

Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/https://doi.org/10.1006/ceps.1999.1015

Wiggins, G., Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Ascd.

Wilson, G. (2009). Teaching tech together. https://teachtogether.tech/

Woo, K. (2018). Anyone can play git/r: Tips for first-time contributions to r packages. https://speakerdeck.com/karawoo/r-tips-for-first-time-contributions-to-r-packages

Xie, Y. (2016). Bookdown: Authoring books and technical documents with R markdown. Chapman; Hall/CRC. https://github.com/rstudio/bookdown

Xie, Y. (2019). Bookdown: Authoring books and technical documents with r markdown. CRC Press. https://bookdown.org/yihui/bookdown/

Xie, Y., Thomas, A., & Hill, A. P. (2019). Blogdown: Creating websites with r markdown. CRC Press. https://bookdown.org/yihui/blogdown/