Items

• Assessing the Educational Data Movement

  Piety, P. J. (2013). Assessing the Educational Data Movement. Teachers College Press.
  

• Who's Learning? Using Demographics in EDM Research

  Paquette, L., Ocumpaugh, J., Li, Z., Andres, A., & Baker, R. (2020). Who’s Learning? Using Demographics in EDM Research. Journal of Educational Data Mining, 12(3), Article 3. https://doi.org/10.5281/zenodo.4143612
  

• Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

  O’Neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Penguin Random House.
  

• Reinventing the Role of the University Researcher

  Nelson, I. A., London, R. A., & Strobel, K. R. (2015). Reinventing the Role of the University Researcher. Educational Researcher, 44(1), 17–26. https://doi.org/10.3102/0013189X15570387
  

• Data-Intensive Research in Education: Current Work and Next Steps

  Dede, C. (2015). Data-Intensive Research in Education: Current Work and Next Steps. Computing Research Association. https://cra.org/wp-content/uploads/2015/10/CRAEducationReport2015.pdf
  

• Are these Changes an Improvement? Using Measures to Inform Homework Practices

  Meyer, A., Grunow, A., & Krumm, A. E. (2017). Are these Changes an Improvement? Using Measures to Inform Homework Practices. AERA Annual Meeting, San Antonio, TX.
  

• Tinkering Toward a Learning Utopia: Implementing Learning Engineering

  Means, B. (2018). Tinkering Toward a Learning Utopia: Implementing Learning Engineering. In C. Dede, J. Richards, & B. Saxberg (Eds.), Learning Engineering for Online Education. Routledge.
  

• From Data to Action: A Community Approach to Improving Youth Outcomes

  McLaughlin, M. W., & London, R. (2013). From Data to Action: A Community Approach to Improving Youth Outcomes. Harvard Education Press.
  

• Data Literacy for Educators: Making It Count in Teacher Preparation and Practice

  Mandinach, E. B., & Gummer, E. S. (2016). Data Literacy for Educators: Making It Count in Teacher Preparation and Practice. Teachers College Press.
  

• A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

  Liu, R., Stamper, J. C., & Davenport, J. (2018). A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems. Journal of Learning Analytics, 5(1), Article 1. https://doi.org/10.18608/jla.2018.51.4
  

• Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

  Liu, R., & Koedinger, K. R. (2017). Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains. Journal of Educational Data Mining, 9(1), Article 1. https://doi.org/10.5281/zenodo.3554625
  

• Teaching Problems and the Problems of Teaching

  Lampert, M. (2001). Teaching Problems and the Problems of Teaching. Yale University Press.
  

• A Collaborative Approach to Sharing Learner Event Data

  Krumm, A. E., Boyce, J., & Everson, H. T. (2021). A Collaborative Approach to Sharing Learner Event Data. Journal of Learning Analytics, 8(2), Article 2. https://doi.org/10.18608/jla.2021.7375
  

• Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement

  Krumm, A. E., Beattie, R., Takahashi, S., D’Angelo, C., Feng, M., & Cheng, B. (2016). Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement. Journal of Learning Analytics, 3(2), Article 2. https://doi.org/10.18608/jla.2016.32.6
  

• Measuring Learning Behaviors Using Data from a Digital Learning Environment

  

• Explanatory and Predictive Modeling Within Improvement Science Projects

  Krumm, A. E., Yeager, D. S., & Yamada, H. (2019). Explanatory and Predictive Modeling Within Improvement Science Projects. AERA Annual Meeting, Toronto, ON. http://tinyurl.com/yaxj9my4
  

• Strategies for Making Digital Learning System Data Usable: A Design Workshop Approach

  Krumm, A. E., & Beattie, R. (2017). Strategies for Making Digital Learning System Data Usable: A Design Workshop Approach. AERA Anual Meeting, San Antonio, TX.
  

• Extracting Information from Big Data: Issues of Measurement, Inference and Linkage

  Kreuter, F., & Peng, R. D. (2014). Extracting Information from Big Data: Issues of Measurement, Inference and Linkage. In H. Nissenbaum, J. Lane, S. Bender, & V. Stodden (Eds.), Privacy, Big Data, and the Public Good: Frameworks for Engagement (pp. 257–275). Cambridge University Press. https://doi.org/10.1017/CBO9781107590205.016
  

• 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