Items

• Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics

  Zheng, G., Fancsali, S. E., Ritter, S., & Berman, S. (2019). Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics. Journal of Learning Analytics, 6(2), Article 2. https://doi.org/10.18608/jla.2019.62.11
  

• Score Reporting Research and Applications

  Zapata-Rivera, D. (2018). Score Reporting Research and Applications. Routledge.
  

• Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

  Yarkoni, T., & Westfall, J. (2017). Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
  

• Big Data for Enhancing Measurement Quality

  Woo, S. E., Tay, L., Jebb, A. T., Ford, M. T., & Kern, M. L. (2020). Big Data for Enhancing Measurement Quality. In S. E. Woo, L. Tay, & R. W. Proctor (Eds.), Big Data in Psychological Research (pp. 59–85). American Psychological Association. https://doi.org/10.1037/0000193-004
  

• Construct and Consequential Validity for Learning Analytics Based on Trace Data

  Winne, P. H. (2020). Construct and Consequential Validity for Learning Analytics Based on Trace Data. Computers in Human Behavior, 112, 106457. https://doi.org/10.1016/j.chb.2020.106457
  

• Social Science Research and Decision-making

  Weiss, C. H., Bucuvalas, M. J., & Bucuvalas, M. (1980). Social Science Research and Decision-making. Columbia University Press.
  

• Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems

  Walonoski, J. A., & Heffernan, N. T. (2006). Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In M. Ikeda, K. D. Ashley, & T.-W. Chan (Eds.), Intelligent Tutoring Systems (pp. 722–724). Springer. https://doi.org/10.1007/11774303_80
  

• Likely to Stop? Predicting Stopout in Massive Open Online Courses

  Taylor, C., Veeramachaneni, K., & O’Reilly, U.-M. (2014). Likely to Stop? Predicting Stopout in Massive Open Online Courses (arXiv:1408.3382). arXiv. https://doi.org/10.48550/arXiv.1408.3382
  

• A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

  Suresh, H., & Guttag, J. V. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9. https://doi.org/10.1145/3465416.3483305
  

• Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39

  Star, S. L., & Griesemer, J. R. (1989). Institutional Ecology, `Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-39. Social Studies of Science, 19(3), 387–420. https://doi.org/10.1177/030631289019003001
  

• This is Not a Boundary Object: Reflections on the Origin of a Concept

  Star, S. L. (2010). This is Not a Boundary Object: Reflections on the Origin of a Concept. Science, Technology, & Human Values, 35(5), 601–617. https://doi.org/10.1177/0162243910377624
  

• Human-Machine Student Model Discovery and Improvement Using DataShop

  Stamper, J. C., & Koedinger, K. R. (2011). Human-Machine Student Model Discovery and Improvement Using DataShop. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 353–360). Springer. https://doi.org/10.1007/978-3-642-21869-9_46
  

• Making Machine Learning Models Clinically Useful

  Shah, N. H., Milstein, A., & Bagley, P., Steven C. (2019). Making Machine Learning Models Clinically Useful. JAMA, 322(14), 1351–1352. https://doi.org/10.1001/jama.2019.10306
  

• Quantitative Ethnography

  Shaffer, D. W. (2017). Quantitative Ethnography. Cathcart Press.
  

• Barriers to Achieving Economies of Scale in Analysis of EHR Data

  Sendak, M. P., Balu, S., & Schulman, K. A. (2017). Barriers to Achieving Economies of Scale in Analysis of EHR Data. Applied Clinical Informatics, 08(03), 826–831. https://doi.org/10.4338/ACI-2017-03-CR-0046
  

• Comparing Lean and Quality Improvement

  Scoville, R., & Little, K. (2014). Comparing Lean and Quality Improvement [White Paper]. Institute for Healthcare Improvement (IHI). https://www.ihi.org:443/resources/Pages/IHIWhitePapers/ComparingLeanandQualityImprovement.aspx
  

• Teacher Inquiry into Students' Learning: Researching Pedagogical Innovations

  Luckin, R., Hansen, C., Wasson, B., Clark, W., Avramides, K., Hunter, J., & Oliver, M. (2015). Teacher Inquiry into Students’ Learning: Researching Pedagogical Innovations. In P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu, & B. Wasson (Eds.), Measuring and Visualizing Learning in the Information-Rich Classroom. Routledge.
  

• From New Technological Infrastructures to Curricular Activity Systems: Advanced Designs for Teaching and Learning

  Roschelle, J., Knudsen, J., & Hegedus, S. (2010). From New Technological Infrastructures to Curricular Activity Systems: Advanced Designs for Teaching and Learning. In M. J. Jacobson & P. Reimann (Eds.), Designs for Learning Environments of the Future: International Perspectives from the Learning Sciences (pp. 233–262). Springer US. https://doi.org/10.1007/978-0-387-88279-6_9
  

• Educational Opportunity in Early and Middle Childhood: Using Full Population Administrative Data to Study Variation by Place and Age

  Reardon, S. F. (2019). Educational Opportunity in Early and Middle Childhood: Using Full Population Administrative Data to Study Variation by Place and Age. RSF: The Russell Sage Foundation Journal of the Social Sciences, 5(2), 40–68. https://doi.org/10.7758/rsf.2019.5.2.03
  

• Understanding Learning Analytics Across Practices

  Piety, P. J., & Pea, R. D. (2018). Understanding Learning Analytics Across Practices. In D. Niemi, R. D. Pea, B. Saxberg, & R. E. Clark (Eds.), Learning Analytics in Education (pp. 215–232). IAP.