93 items

• 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
• 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
• 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/
• 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
• Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems
• 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
• 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.
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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.
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• Communities of Practice: Learning, Meaning, and Identity
  Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press.
• 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
• 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
• Practical Measurement
  Yeager, D., Bryk, A., Muhich, J., Hausman, H., & Morales, L. (2013). Practical Measurement. Carnegie Foundation for the Advancement of Teaching.
• 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
• 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
• 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.
• 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
• 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