93 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.
• 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
• 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
• 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
  Krumm, Andrew E. “Measuring Learning Behaviors Using Data from a Digital Learning Environment.” National Council on Measurement in Education Annual Meeting, San Francisco, CA, 2020.
• 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
• 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