If you have used ENA in your research, please send your paper and a complete citation to epistemicanalytics+ENA@gmail.com for inclusion in the list below.
If you use any products, services, or data developed or provided by Epistemic Analytics in your research or in any publications or presentations, please read our guidelines for acknowledgment.
Articles In Press
Ruis, A.R., Rosser, A.A., Quandt-Walle, C., Nathwani, J.N., Shaffer, D.W., & Pugh, C.M. (In Press). The hands and head of a surgeon: Modeling operative competency with multimodal epistemic network analysis. American Journal of Surgery.
Shaffer, D.W. (In Press). Big data for thick description of deep learning. In K. Millis, D. Long, J. Magliano, and K. Weimer (Eds.), Deep learning: Multi-disciplinary approaches. NY, NY: Routledge/Taylor Francis.
Sullivan, S. A., Warner-Hillard, C., Eagan, B. R., Thompson, R., Ruis, A. R., Haines, K., Jung, H. S. (2018). Using epistemic network analysis to identify targets for educational interventions in trauma team communication. Surgery, 163(4), 938–943.
Wooldridge, A.R, Carayon, P., Shaffer, D.W., & Eagan, B. (2018). Quantifying the qualitative with epistemic network analysis: A human factors case study of task-allocation communication in a primary care team. IISE Transactions on Healthcare Systems Engineering, 8(1), 72–82.
Shaffer, D.W. (2018). Epistemic network analysis: Understanding learning by using big data for thick description. Fischer, F., Hmelo-Silver, C. E., Goldman, S. R., & Reimann, P. (Eds.). International Handbook of the Learning Sciences (pp. 520-531). New York: Routledge.
Ruis, A.R., Siebert-Evenstone, A.L., Pozen, R., Eagan, B., & Shaffer, D.W. (2018). A Method for determining the extent of recent temporal context in analyses of complex, collaborative thinking. In Kay, J. & Luckin, R (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS), III, 1625–1626.
S.S. Fougt, A. Siebert-Evenstone, B. Eagan, S. Tabatabai, and M. Misfeldt. 2018. Epistemic network analysis of students’ longer written assignments as formative/summative evaluation. In Proceedings of the International Conference on Learning Analytics and Knowledge, Sydney, Australia, March 2018 (LAK’18), 5 pages.
Herder, T., Swiecki, Z., Fougt, S. S., Tamborg, A. L., Allsopp, B. B., Shaffer, D. W., Misfeldt, M. (2018). Supporting teacher’s intervention in student’s virtual collaboration using a network based model. In Proceedings of the International Conference on Learning Analytics and Knowledge, Sydney, Australia, March 2018 (LAK’18), 21-25.
Swiecki, Z. & Shaffer, D.W. (2018). Toward a taxonomy of team performance visualization tools. Paper presented at the 13th International Conference of the Learning Sciences (ICLS). London, United Kingdom.
Shaffer, D.W. (2017). Quantitative Ethnography. Madison, WI: Cathcart Press.
Siebert-Evenstone, A.L., Arastoopour Irgens, G., Collier, W., Swiecki, Z., Ruis, A.R., & Shaffer, D.W. (2017). In search of conversational grain size: Modelling semantic structure using moving stanza windows. Journal of Learning Analytics, 4(3), 123–139.
Shaffer, D.W. & Ruis, A.R. (2017). Epistemic network analysis: A worked example of theory-based learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Grasevic (Eds.), Handbook of Learning Analytics (pp. 175–187). Society for Learning Analytics Research.
Cai, Z., Eagan, B., Dowell, N.M., Pennebaker, J.W., Shaffer, D.W., & Graesser, A.C. (2017). Epistemic network analysis and topic modeling for chat data from collaborative learning environment. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the 10th International Conference on Educational Data Mining (pp. 104-111). Wuhan, Hubei, China: EDM Society.
Shaffer, D.W., Collier, W., & Ruis, A.R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45.
Arastoopour, G., Shaffer, D.W., Swiecki, Z., Ruis, A.R., & Chesler, N.C. (2016). Teaching and assessing engineering design thinking with virtual internships and epistemic network analysis. International Journal of Engineering Education, 32(3B), 1492–1501.
Csanadi, A., Eagan, B., Shaffer, D. W., Kollar, I., & Fischer, F. (2017). Collaborative and individual scientific reasoning of pre-service teachers: New insights through epistemic network analysis (ENA). In B. K. Smith, M. Borge, E. Mercier, & K. Y. Lim (Eds.), Making a difference: Prioritizing equity and access in CSCL: 12th International Conference on Computer-Supported Collaborative Learning (Vol. I, pp. 215–222).
Quardokus Fisher, K. Hirshfield, L., Siebert-Evenstone, A., Arastoopour, G., & Koretsky, M. (2016). Network analysis of interactions between students and an instructor during design meetings. Paper presented at the American Society for Engineering Education. New Orleans, LA.
Siebert-Evenstone, A. L., Arastoopour, G., Collier, W., Swiecki, Z., Ruis, A. R., & Shaffer, D.W. (2016). In search of conversational grain size: Modeling semantic structure using moving stanza windows. Paper presented at the 12th International Conference of the Learning Sciences. Singapore.
Chesler, N.C., Ruis, A.R., Collier, W., Swiecki, Z., Arastoopour, G., & Shaffer, D.W. (2015). A novel paradigm for engineering education: Virtual internships with individualized mentoring and assessment of engineering thinking. Journal of Biomechanical Engineering, 137(2).
Arastoopour, G., Swiecki, Z., Chesler, N.C., & Shaffer, D.W. (2015). Epistemic Network Analysis as a Tool for Engineering Design Assessment. Paper presented at the American Society for Engineering Education. Seattle, WA.
Arastoopour, G., Shaffer, D.W., Swiecki, Z., Ruis, A.R., & Chesler, N.C. (2015). Teaching and Assessing Engineering Design Thinking with Virtual Internships and Epistemic Network Analysis. Paper presented at the Harvey Mudd Design Workshop. Claremont, CA.
Knight, S., Arastoopour, G., Shaffer, D.W., Shum, S.B., & Littleton, K. (2014). Epistemic networks for epistemic commitments. Paper presented at the International Conference of the Learning Sciences. Boulder, CO.
Chesler, N., Arastoopour, G., D’Angelo, C., Bagley, E., & Shaffer, D.W. (2013). Design of a professional practice simulator for educating and motivating first-year engineering students. Advances in Engineering Education 3(3): 1-29.
Knight, S., Arastoopour, G., Shaffer, D.W., Shum, S.B., & Littleton, K. (2013). Epistemic networks for epistemic commitments. Technical Report KMI-13-03. Knowledge Media Institute, Open University, Netherlands.
Orill, C.H., Shaffer, D.W., & Burke, J. (2013). Exploring coherence in teacher knowledge using epistemic network analysis. Presented at the American Educational Research Association Annual Meeting. San Francisco, CA.
Arastoopour, G., Chesler, N., D’Angelo, C., Shaffer, D.W., Opgenorth, J., Reardan, C., Haggerty, N., & Lepak, C. (2012). Nephrotex: Measuring first-year students’ ways of professional thinking in a virtual internship. Paper presented at the annual meeting of the American Society of Engineering Education. San Antonio, TX.
D’Angelo, C. M., Clark, D. B., & Shaffer, D. W. (2012). Epistemic network analysis: An alternative analysis technique for complex STEM thinking. Presented at the National Association of Research on Science Teaching Conference. Indianapolis, IN.
Rupp, A. A., Sweet, S., & Choi, Y. (2010). Modeling learning trajectories with epistemic network analysis: A simulation-based investigation of a novel analytic method for epistemic games. Presented at the Educational Data Mining Conference. Pittsburgh, PA.
Shaffer, D.W., Hatfield, D., Svarovsky, G., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A., & Mislevy, R. (2009). Epistemic Network Analysis: A prototype for 21st century assessment of learning. International Journal of Learning and Media 1(2): 33-53.
Rupp, A., Choi, Y., Gushta, M., Mislevy, R., Thies, M.C., Bagley, E., Nash, P., Hatfield, D., Svarovsky, G., & Shaffer, D.W. (2009). Modeling learning progressions in epistemic games with epistemic network analysis: Principles for data analysis and generation. Paper presented at the Learning Progressions in Science Conference. Iowa City, IA.