Items

• Using Data-Driven Discovery of Better Student Models to Improve Student Learning

  Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., & Nixon, T. (2013). Using Data-Driven Discovery of Better Student Models to Improve Student Learning. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 421–430). Springer. https://doi.org/10.1007/978-3-642-39112-5_43
  

• Data Mining and Education

  Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data Mining and Education. WIREs Cognitive Science, 6(4), 333–353. https://doi.org/10.1002/wcs.1350
  

• Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom

  Koedinger, K. R., & Corbett, A. (2005). Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 61–78). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.006
  

• A Data Repository for the EDM Community: The PSLC DataShop

  Koedinger, K. R., Baker, R. S. J. d, Cunningham, K., Skogsholm, A., Leber, B., & Stamper, and J. (2010). A Data Repository for the EDM Community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. d. Baker (Eds.), Handbook of Educational Data Mining (pp. 43–56). CRC Press.
  

• Towards Demonstrating the Value of Learning Analytics for K–12 Education

  Baker, R. S., & Koedinger, K. R. (2018). Towards Demonstrating the Value of Learning Analytics for K–12 Education. In D. Niemi, R. D. Pea, B. Saxberg, & R. E. Clark (Eds.), Learning Analytics in Education (pp. 49–62). IAP.
  

• Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin

  Knowles, J. E. (2015). Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin. Journal of Educational Data Mining, 7(3), Article 3. https://doi.org/10.5281/zenodo.3554725
  

• The In-Task Assessment Framework for Behavioral Data

  Kerr, D., Andrews, J. J., & Mislevy, R. J. (2016). The In-Task Assessment Framework for Behavioral Data. In The Wiley Handbook of Cognition and Assessment (pp. 472–507). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118956588.ch20
  

• An Introduction to Statistical Learning: with Applications in R

  James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R. Springer Science & Business Media.
  

• Computer-Tailored Student Support in Introductory Physics

  Huberth, M., Chen, P., Tritz, J., & McKay, T. A. (2015). Computer-Tailored Student Support in Introductory Physics. PLOS ONE, 10(9), e0137001. https://doi.org/10.1371/journal.pone.0137001
  

• Efficiency of Automated Detectors of Learner Engagement and Affect Compared with Traditional Observation Methods

  Hollands, F., & Bakir, I. (2015). Efficiency of Automated Detectors of Learner Engagement and Affect Compared with Traditional Observation Methods [Working Paper]. Center for Benefit-Cost Studies of Education, Teachers College, Columbia University. https://repository.upenn.edu/cbcse/4
  

• Discovery With Models: A Case Study on Carelessness in Computer-Based Science Inquiry

  Hershkovitz, A., de Baker, R. S. J., Gobert, J., Wixon, M., & Pedro, M. S. (2013). Discovery With Models: A Case Study on Carelessness in Computer-Based Science Inquiry. American Behavioral Scientist, 57(10), 1480–1499. https://doi.org/10.1177/0002764213479365
  

• Predictive Learning Analytics 'At Scale': Guidelines to Successful Implementation in Higher Education

  Herodotou, C., Rienties, B., Verdin, B., & Boroowa, A. (2019). Predictive Learning Analytics “At Scale”: Guidelines to Successful Implementation in Higher Education. Journal of Learning Analytics, 6(1), Article 1. https://doi.org/10.18608/jla.2019.61.5
  

• A Toolkit for Centering Racial Equity Throughout Data Integration – Actionable Intelligence for Social Policy

  Hawn Nelson, A., Jenkins, D., Zanti, S., Katz, M., Berkowitz, E., Burnett, T. C., & Culhane, D. (2020). A Toolkit for Centering Racial Equity Throughout Data Integration – Actionable Intelligence for Social Policy. Actionable Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/resource-article/a-toolkit-for-centering-racial-equity-throughout-data-integration/
  

• Unintended Consequences of Information Technologies in Health Care—An Interactive Sociotechnical Analysis

  Harrison, M. I., Koppel, R., & Bar-Lev, S. (2007). Unintended Consequences of Information Technologies in Health Care—An Interactive Sociotechnical Analysis. Journal of the American Medical Informatics Association, 14(5), 542–549. https://doi.org/10.1197/jamia.M2384
  

• Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems

  

• Readiness of US General Surgery Residents for Independent Practice

  George, B. C., Bohnen, J. D., Williams, R. G., Meyerson, S. L., Schuller, M. C., Clark, M. J., Meier, A. H., Torbeck, L., Mandell, S. P., Mullen, J. T., Smink, D. S., Scully, R. E., Chipman, J. G., Auyang, E. D., Terhune, K. P., Wise, P. E., Choi, J. N., Foley, E. F., Dimick, J. B., … Collaborative (PLSC), on behalf of the P. L. and S. (2017). Readiness of US General Surgery Residents for Independent Practice. Annals of Surgery, 266(4), 582–594. https://doi.org/10.1097/SLA.0000000000002414
  

• Thick Description: Toward an Interpretive Theory of Culture

  Geertz, C. (1973). Thick Description: Toward an Interpretive Theory of Culture. In Interpretation of Cultures: Selected Essays (pp. 3–30). Basic Books.
  

• Toward an Information Infrastructure for Global Health Improvement

  Friedman, C. P., Rubin, J. C., & Sullivan, K. J. (2017). Toward an Information Infrastructure for Global Health Improvement. Yearbook of Medical Informatics, 26(01), 16–23. https://doi.org/10.15265/IY-2017-004
  

• Computable Knowledge: An Imperative for Learning Health Systems

  Friedman, C. P., & Flynn, A. J. (2019). Computable Knowledge: An Imperative for Learning Health Systems. Learning Health Systems, 3(4), e10203. https://doi.org/10.1002/lrh2.10203
  

• Mining Big Data in Education: Affordances and Challenges

  Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732X20903304