Items

• 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
• 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
• Evaluating the Quality of Medical Care
  Donabedian, A. (2005). Evaluating the Quality of Medical Care. The Milbank Quarterly, 83(4), 691–729. https://doi.org/10.1111/j.1468-0009.2005.00397.x
• Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief
  Bienkowski, M., Feng, M., & Means, B. (2014). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief (p. 60). U.S. Department of Education.
• 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
• 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
• Educational Data Sciences: Framing Emergent Practices for Analytics of Learning, Organizations, and Systems
  Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014). Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, 193–202. https://doi.org/10.1145/2567574.2567582
• Educational Data Mining and Learning Analytics
  Baker, R., & Siemens, G. (2014). Educational Data Mining and Learning Analytics. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253–272). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.016
• Do We Know Who Will Drop Out? A Review of the Predictors of Dropping out of High School: Precision, Sensitivity, and Specificity
  Bowers, A. J., Sprott, R., & Taff, S. A. (2012). Do We Know Who Will Drop Out? A Review of the Predictors of Dropping out of High School: Precision, Sensitivity, and Specificity. The High School Journal, 96(2), 77–100.
• 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
• 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
• 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
• 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.
• Creating Research-Practice Partnerships in Education
  Penuel, W. R., & Gallagher, D. J. (2017). Creating Research-Practice Partnerships in Education. Harvard Education Press. https://www.hepg.org/hep-home/books/creating-research-practice-partnerships-in-educati
• Course Signals at Purdue: Using Learning Analytics to Increase Student Success
  Arnold, K. E., & Pistilli, M. D. (2012). Course Signals at Purdue: Using Learning Analytics to Increase Student Success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267–270. https://doi.org/10.1145/2330601.2330666
• 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
• Conceptualizing Research–Practice Partnerships as Joint Work at Boundaries
  Penuel, W. R., Allen, A.-R., Coburn, C. E., & Farrell, C. C. (2015). Conceptualizing Research–Practice Partnerships as Joint Work at Boundaries. Journal of Education for Students Placed at Risk (JESPAR), 20(1–2), 182–197. https://doi.org/10.1080/10824669.2014.988334
• 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
• 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
• Components, Infrastructures, and Capacity: The Quest for the Impact of Actionable Data Use on P–20 Educator Practice
  Piety, P. J. (2019). Components, Infrastructures, and Capacity: The Quest for the Impact of Actionable Data Use on P–20 Educator Practice. Review of Research in Education, 43(1), 394–421. https://doi.org/10.3102/0091732X18821116
• 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
• Communities of Practice: Learning, Meaning, and Identity
  Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press.
• 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
• 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
• 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
• Building a Village Through Data: A Research–Practice Partnership to Improve Youth Outcomes
  Biag, M. (2017). Building a Village Through Data: A Research–Practice Partnership to Improve Youth Outcomes. The School Community Journal, 27, 9–27.
• 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
• Better to Be Frustrated Than Bored: The Incidence, Persistence, and Impact of Learners’ Cognitive–Affective States During Interactions with Three Different Computer-Based Learning Environments
  Baker, R. S. J. d., D’Mello, S. K., Rodrigo, Ma. M. T., & Graesser, A. C. (2010). Better to Be Frustrated Than Bored: The Incidence, Persistence, and Impact of Learners’ Cognitive–Affective States During Interactions with Three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003
• 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
• Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
  Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Publishing Group.
• Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning with an Intelligent Tutoring System
  Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2010). Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning with an Intelligent Tutoring System. Educational Psychologist, 45(4), 224–233. https://doi.org/10.1080/00461520.2010.517740
• Assessing the Educational Data Movement
  Piety, P. J. (2013). Assessing the Educational Data Movement. Teachers College Press.
• Artificial Intelligence in Health Care: Will the Value Match the Hype?
  Emanuel, E. J., & Wachter, R. M. (2019). Artificial Intelligence in Health Care: Will the Value Match the Hype? JAMA, 321(23), 2281–2282. https://doi.org/10.1001/jama.2019.4914
• 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.
• 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.
• Adapting to When Students Game an Intelligent Tutoring System
  Baker, R. S. J. d., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, I., Wagner, A. Z., Naim, M., Raspat, J., Baker, D. J., & Beck, J. E. (2006). Adapting to When Students Game an Intelligent Tutoring System. In M. Ikeda, K. D. Ashley, & T.-W. Chan (Eds.), Intelligent Tutoring Systems (pp. 392–401). Springer. https://doi.org/10.1007/11774303_39
• 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/
• A Time Series Interaction Analysis Method for Building Predictive Models of Learners Using Log Data
  Brooks, C., Thompson, C., & Teasley, S. (2015). A Time Series Interaction Analysis Method for Building Predictive Models of Learners Using Log Data. Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 126–135. https://doi.org/10.1145/2723576.2723581
• A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning
  D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., & Graesser, A. (2010). A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent Tutoring Systems (pp. 245–254). Springer. https://doi.org/10.1007/978-3-642-13388-6_29
• 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
• 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
• 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.
• 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