David Williamson Shaffer

Vilas Distinguished Achievement Professor of Learning Sciences, Area Chair: Learning Sciences

dws@education.wisc.edu


1065 Educational Sciences

1025 West Johnson Street

Madison, WI 53706-1706

Williamson Shaffer, David

      Download CV   https://www.epistemicanalytics.org/david-williamson-shaffer/  

David Williamson Shaffer is the Vilas Distinguished Achievement Professor of Learning Sciences at the University of Wisconsin-Madison in the Department of Educational Psychology and a Data Philosopher at the Wisconsin Center for Education Research. Before coming to the University of Wisconsin, Professor Shaffer taught grades 4-12 in the United States and abroad, including two years working with the US Peace Corps in Nepal. His M.S. and Ph.D. are from the Media Laboratory at the Massachusetts Institute of Technology. Professor Shaffer taught in the Technology and Education Program at the Harvard Graduate School of Education, and was a 2008-2009 European Union Marie Curie Fellow. His current focus is on merging statistical and qualitative methods to model complex and collaborative thinking skills. Professor Shaffer has led the development of a suite of quantitative ethnographic tools that are being used by more than 500 researchers in 20 countries, which has resulted in more than 550 publications, as well as the establishment of an annual International Conference on Quantitative Ethnography. He has authored more than 250 publications with over 100 co-authors, including How Computer Games Help Children Learn and Quantitative Ethnography.

Education

  • PhD Media Arts and Sciences, Massachusetts Institute of Technology, 1998
  • MS Media Arts and Sciences, Massachusetts Institute of Technology, 1996
  • AB History and East Asian Studies, Harvard University, 1987

Select Publications

  • Swiecki, Z., Ruis, A. R., Farrell, C., & Williamson Shaffer, D. (2020). Assessing individual contributions to collaborative problem solving: A network analysis approach. Computers in Human Behavior, 104 Online Publication/Abstract.
  • Williamson Shaffer, D., (2018). Epistemic network analysis: Understanding learning by using big data for thick description.. In Fischer, F., Hmelo-Silver, C. E., Goldman, S. R., & Reimann, P. (Eds.), International Handbook of the Learning Sciences (pp. 520-531). New York, NY: Routledge Online Publication/Abstract.
  • Williamson Shaffer, D., (2017). Quantitative Ethnography. Madison, WI: Cathcart Press Online Publication/Abstract.
  • Wise, A. F., & Williamson Shaffer, D. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5-13. Online Publication/Abstract.
  • Williamson Shaffer, D., (2012). Models of situated action: Computer games and the problem of transfer. In C. Steinkuehler, K. Squire, S. Barab (Ed.), Games learning, and society: Learning and meaning in the digital age (pp. 403-433). Cambridge: Cambridge University Press Online Publication/Abstract.
  • Williamson Shaffer, D., (2006). How Computer Games Help Children Learn. New York, NY: Palgrave Online Publication/Abstract.
  • Williamson Shaffer, D., Squire, K. D., Halverson, R. R., & Gee, J. P. (2005). Video games and the future of learning. Phi Delta Kappan, 87(2), 104-111. Online Publication/Abstract.
  • Williamson Shaffer, D., (2004). Pedagogical praxis: The professions as models for post-industrial education. Teachers College Record, 106(7), 1401-1421. Online Publication/Abstract.
  • Williamson Shaffer, D., & Serlin, R. (2004). What good are statistics that don't generalize?. Educational Researcher, 33(9), 14-25. Online Publication/Abstract.
  • Williamson Shaffer, D., & Kaput, J. J. (1999). Mathematics and virtual culture: An evolutionary perspective on technology and mathematics. Educational Studies in Mathematics, 37, 97-119. Online Publication/Abstract.

Select Presentations

  • Williamson Shaffer, D. (2019). Reconfiguring Education in the Age of The Smart Machine. presented at the Fremtidens Digitale Skole Conference, University of Copenhagen.
  • Williamson Shaffer, D. (2018). La Etnographia Cuantitatíva: Nueves posibilidades de investigación. presented at the IV Coloquio Internacional: Derecho al Bienestar Humano, Ética Global y Educación, University of Guanajuato.
  • Williamson Shaffer, D. (2018). The Importance of Meaning: Going Beyond Mixed Methods to Turn Big Data into Real Understanding. Keynote/Plenary Address presented at the Learning Analytics and Knowledge Conference. Link
  • Williamson Shaffer, D. (2017). Quantitative ethnography. presented at the , Tokyo University.
  • Williamson Shaffer, D. W. (20152015). ENA as theory-based learning analytics. presented at the Computer Supported Collaborative Learning Conference.
  • Williamson Shaffer, D. W. (20152015). Multimodal ENA. presented at the JENlab Research Meeting.
  • Williamson Shaffer, D. W. (20152015). Virtual internships as authentic STEM experiences. presented at the National Research Council Convocation on Discovery-Based Research Experiences for Undergraduates.
  • Williamson Shaffer, D. W. (20152015). Land Science. presented at the UW/Native Nations Summit on Environment and Health.
  • Williamson Shaffer, D. W. (20142014). Automated mentoring and virtual internships. presented at the Army Research Lab Meeting on Authoring Tools and Expert Modeling Techniques.
  • Williamson Shaffer, D. W. (20142014). Epistemic network analysis. presented at the Educational Data Mining Conference.

Select Awards and Honors

  • Marie Curie Fellowship, European Union, 2008
  • Postdoctoral Fellow, National Academy of Education/Spencer Foundation, 2003
  • Phi Beta Kappa, Harvard University, 1987