Items

• 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
• What Matters for Staying On-Track and Graduating in Chicago Public High Schools: A Close Look at Course Grades, Failures, and Attendance in the Freshman Year
  Allensworth, E. M., & Easton, J. Q. (2007). What Matters for Staying On-Track and Graduating in Chicago Public High Schools: A Close Look at Course Grades, Failures, and Attendance in the Freshman Year (p. 68). University of Chicago Consortium on Chicago School Research.
• 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.
• 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
• Using Health Systems Engineering Approaches to Prepare for Tailoring of Implementation Interventions
  Barnes, G. D., Acosta, J., Kurlander, J. E., & Sales, A. E. (2021). Using Health Systems Engineering Approaches to Prepare for Tailoring of Implementation Interventions. Journal of General Internal Medicine, 36(1), 178–185. https://doi.org/10.1007/s11606-020-06121-5
• 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
• 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
• 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.
• 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.
• 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
• 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.
• 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
• 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.
• The Use and Effectiveness of Electronic Clinical Decision Support Tools in the Ambulatory/Primary Care Setting: A Systematic Review of the Literature
  Bryan, C., & Boren, S. A. (2008). The Use and Effectiveness of Electronic Clinical Decision Support Tools in the Ambulatory/Primary Care Setting: A Systematic Review of the Literature. Informatics in Primary Care, 16(2), 79–91. https://doi.org/10.14236/jhi.v16i2.679
• The Three Faces of Performance Measurement: Improvement, Accountability, and Research
  Solberg, L. I., Mosser, G., & McDonald, S. (1997). The Three Faces of Performance Measurement: Improvement, Accountability, and Research. The Joint Commission Journal on Quality Improvement, 23(3), 135–147. https://doi.org/10.1016/S1070-3241(16)30305-4
• 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
• The Improvement Guide: A Practical Approach to Enhancing Organizational Performance
  Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. John Wiley & Sons.
• The Feasibility of Real-Time Intraoperative Performance Assessment With SIMPL (System for Improving and Measuring Procedural Learning): Early Experience From a Multi-institutional Trial
  Bohnen, J. D., George, B. C., Williams, R. G., Schuller, M. C., DaRosa, D. A., Torbeck, L., Mullen, J. T., Meyerson, S. L., Auyang, E. D., Chipman, J. G., Choi, J. N., Choti, M. A., Endean, E. D., Foley, E. F., Mandell, S. P., Meier, A. H., Smink, D. S., Terhune, K. P., Wise, P. E., … Fryer, J. P. (2016). The Feasibility of Real-Time Intraoperative Performance Assessment With SIMPL (System for Improving and Measuring Procedural Learning): Early Experience From a Multi-institutional Trial. Journal of Surgical Education, 73(6), e118–e130. https://doi.org/10.1016/j.jsurg.2016.08.010
• Teaching Problems and the Problems of Teaching
  Lampert, M. (2001). Teaching Problems and the Problems of Teaching. Yale University Press.
• 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.
• Supporting the Initial Work of Evidence-Based Improvement Cycles Through a Data-Intensive Partnership
  Bowers, A. J., & Krumm, A. E. (2021). Supporting the Initial Work of Evidence-Based Improvement Cycles Through a Data-Intensive Partnership. Information and Learning Sciences, 122(9/10), 629–650. https://doi.org/10.1108/ILS-09-2020-0212
• Stupid Tutoring Systems, Intelligent Humans
  Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614. https://doi.org/10.1007/s40593-016-0105-0
• 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.
• Sorting Things Out: Classification and Its Consequences
  Bowker, G. C., & Star, S. L. (2000). Sorting Things Out: Classification and Its Consequences. https://doi.org/10.7551/mitpress/6352.001.0001
• Social Science Research and Decision-making
  Weiss, C. H., Bucuvalas, M. J., & Bucuvalas, M. (1980). Social Science Research and Decision-making. Columbia University Press.
• Score Reporting Research and Applications
  Zapata-Rivera, D. (2018). Score Reporting Research and Applications. Routledge.
• 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
• Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes
  Bowers, A. J., & Zhou, X. (2019). Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), 20–46. https://doi.org/10.1080/10824669.2018.1523734
• 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
• Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity
  Bowers, A. J. (2017). Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity. Journal of Research on Leadership Education, 12(1), 72–96. https://doi.org/10.1177/1942775116659462
• Quantitative Ethnography
  Shaffer, D. W. (2017). Quantitative Ethnography. Cathcart 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
• 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
• 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
• Practical Measurement
  Yeager, D., Bryk, A., Muhich, J., Hausman, H., & Morales, L. (2013). Practical Measurement. Carnegie Foundation for the Advancement of Teaching.
• Off-Task Behavior in the Cognitive Tutor Classroom: When Students "Game the System"
  Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game the System.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 383–390. https://doi.org/10.1145/985692.985741
• 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
• 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
• Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems
• 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.
• Measuring Equitable Science Instruction at Scale
  Krumm, A., Penuel, W. R., Pazera, C., & Landel, C. (2020). Measuring Equitable Science Instruction at Scale. The Interdisciplinarity of the Learning Sciences, 4, 2461–2468. https://repository.isls.org//handle/1/6607
• Measurement Matters: Assessing Personal Qualities Other Than Cognitive Ability for Educational Purposes
  Duckworth, A. L., & Yeager, D. S. (2015). Measurement Matters: Assessing Personal Qualities Other Than Cognitive Ability for Educational Purposes. Educational Researcher, 44(4), 237–251. https://doi.org/10.3102/0013189X15584327
• 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
• 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
• Learning to Improve: How America’s Schools Can Get Better at Getting Better
  Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to Improve: How America’s Schools Can Get Better at Getting Better. Harvard Education Press. https://www.hepg.org/hep-home/books/learning-to-improve
• Learning Analytics Goes to School: A Collaborative Approach to Improving Education
  Krumm, A., Means, B., & Bienkowski, M. (2018). Learning Analytics Goes to School: A Collaborative Approach to Improving Education (1st ed.). Routledge. https://www.routledge.com/Learning-Analytics-Goes-to-School-A-Collaborative-Approach-to-Improving/Krumm-Means-Bienkowski/p/book/9781138121836
• 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
• 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
• 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
• 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.