2219 items

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  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
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  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
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  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
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  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.
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  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
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  Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems

  Due to the increasing amount of available published evidence and the continual need to apply and update evidence in practice, we propose a shift in the way evidence generated by learning health systems can be integrated into more traditional evidence reviews. This paper discusses two main mechanisms to close the evidence-to-practice gap: (1) integrating Learning Health System (LHS) results with existing systematic review evidence and (2) providing this combined evidence in a standardized, computable data format. We believe these efforts will better inform practice, thereby improving individual and population health.
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  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
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  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/
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  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
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  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
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  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
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  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
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  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.
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  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
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  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
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  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.
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  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.
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  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
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  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
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  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
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  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
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  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.
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  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
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  Measuring Learning Behaviors Using Data from a Digital Learning Environment

  This paper describes a partnership-driven approach for developing measures of learning behaviors using event data from a digital learning environment is used in all grades and subjects within a charter management organization. The approach that we developed and followed included involved (1) gathering leaders’, teachers’, and students’ perspectives on learning behaviors; (2) collaboratively analyzing data; (3) using exploratory factor analyses to generate an single score; and (4) conducting explicit model-based tests that assessed the degree to which the single score was correlated with outcomes that were important to all members of the partnership.
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  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
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  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
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  Teaching Problems and the Problems of Teaching

  Lampert, M. (2001). Teaching Problems and the Problems of Teaching. Yale University Press.
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  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
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  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
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  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.
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  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.
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  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.
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  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.
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  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
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  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
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  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.
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  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
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  Assessing the Educational Data Movement

  Piety, P. J. (2013). Assessing the Educational Data Movement. Teachers College Press.
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  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
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  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
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  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.
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  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
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  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
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  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.
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  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
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  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
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  Quantitative Ethnography

  Shaffer, D. W. (2017). Quantitative Ethnography. Cathcart Press.
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  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
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  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
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  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