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. (2024). lme4: Linear mixed-effects models using eigen and S4. https://github.com/lme4/lme4/
Betebenner, D. W. (2024). randomNames: Generate random given and surnames. https://centerforassessment.github.io/randomNames/
Bransford, J. D., Brown, A. L., Cocking, R. R., et al. (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.
Dobbyn, A., Stawitz, C., Vel’asquez, I., Hester, J., & DeCicco, L. (n.d.). roomba. https://github.com/cstawitz/roomba.
Dweck, C. (2015). Carol dweck revisits the growth mindset. Education Week, 35(5), 20–24.
Elbers, B. (2024). Tidylog: Logging for dplyr and tidyr functions. https://github.com/elbersb/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. (2024). Dataedu: Package for data science in education using r. https://github.com/data-edu/dataedu
Firke, S. (2023). Janitor: Simple tools for examining and cleaning dirty data. https://github.com/sfirke/janitor
Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Great Schools. (n.d.). Aggregate data. In Ed Glossary. https://www.edglossary.org/aggregate-data/
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.
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, Michael W. (2016). Rtweet: Collecting twitter data. Comprehensive R Archive Network. Available at: Https://Cran. R-Project. Org/Package= Rtweet.
Kearney, Michael W., Revilla Sancho, L., & Wickham, H. (2024). Rtweet: Collecting twitter data. https://docs.ropensci.org/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. et al. (2008). Building predictive models in r using the caret package. Journal of Statistical Software, 28(5), 1–26.
Kuhn, M. (2023). Caret: Classification and regression training. https://github.com/topepo/caret/
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., & 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.
Lewis, C. (2024). Data management in large-scale education research. Chapman; Hall/CRC.
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. (2024). sjPlot: Data visualization for statistics in social science. https://strengejacke.github.io/sjPlot/
Lüdecke, D., Makowski, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., Wiernik, B. M., & Th’eriault, R. (2024). Performance: Assessment of regression models performance. https://easystats.github.io/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, et al. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press.
National Center for Education Statistics. (2018). Public elementary/secondary school universe survey. https://nces.ed.gov/programs/digest/d17/tables/dt17_204.10.asp?current=yes
National Forum on Education Statistics. (2016). Forum guide to collecting and using disaggregated data on racial/ethnic subgroups. https://nces.ed.gov/pubs2017/NFES2017017.pdf
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.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
of Education, U. S. D. (2020). https://www2.ed.gov/programs/osepidea/618-data/state-level-data-files/index.html#bccee
Paris, D., & Alim, H. S. (2017). Culturally sustaining pedagogies: Teaching and learning for justice in a changing world. Teachers College Press.
Pedersen, T. L. (2024a). Ggraph: An implementation of grammar of graphics for graphs and networks. https://ggraph.data-imaginist.com
Pedersen, T. L. (2024b). Tidygraph: A tidy API for graph manipulation. https://tidygraph.data-imaginist.com
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/
Reachable: Data collection methods for sexual orientation and gender identity. (2016). https://williamsinstitute.law.ucla.edu/wp-content/uploads/Reachable-Data-collection-methods-for-sexual-orientation-gender-identity-March-2016.pdf](https://williamsinstitute.law.ucla.edu/wp-content/uploads/Reachable-Data-collection-methods-for-sexual-orientation-gender-identity-March-2016.pdf
Robinson, D. (2018). Advice to aspiring data scientists: Start a blog.
Robinson, D., & Silge, J. (2024). Tidytext: Text mining using dplyr, ggplot2, and other tidy tools. https://juliasilge.github.io/tidytext/
Rosenberg, Joshua M., Beymer, P. N., Anderson, D. J., Van Lissa, C., & Schmidt, J. A. (2019). tidyLPA: An r package to easily carry out latent profile analysis (LPA) using open-source or commercial software. Journal of Open Source Software, 3(30), 978.
Rosenberg, Joshua M., Galas, E., & Willet, K. (2021). Who are the data scientists in education? An investigation of the identities and work of individuals in diverse roles. Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021.
Rosenberg, Joshua 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, Joshua M., Lawson, M., Anderson, D., Rutherford, T., & Jones, R. S. (2020). Making data science count in and for education. Research Methods in Learning Design & Technology., 1–13.
RStudio Team. (2015). RStudio: Integrated development environment for r. RStudio, Inc. http://www.rstudio.com/
Sepulveda, M. V. (2024). Tabulapdf: Extract tables from PDF documents. https://github.com/ropensci/tabulapdf
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. (2021). apaTables: Create american psychological association (APA) style tables. https://github.com/dstanley4/apaTables
The Glossary of Education Reform. (2015). Student subgroup. https://www.edglossary.org/student-subgroup
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. (2014). Tidy data. Journal of Statistical Software, 59. https://www.jstatsoft.org/article/view/v059i10/v59i10.pdf](https://www.jstatsoft.org/article/view/v059i10/v59i10.pdf
Wickham, H. (2019). Advanced r (2nd ed.). https://adv-r.hadley.nz/
Wickham, H. (2023). Tidyverse: Easily install and load the tidyverse. https://tidyverse.tidyverse.org
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686.
Wickham, H., & Bryan, J. (2023a). R packages (2nd ed.). O’Reilly Media. https://r-pkgs.org/
Wickham, H., & Bryan, J. (2023b). Readxl: Read excel files. https://readxl.tidyverse.org
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). Dplyr: A grammar of data manipulation. https://dplyr.tidyverse.org
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.
Wikipedia. (2020). Reproducible research. https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research
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://bookdown.org/yihui/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/