Items

• 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