I am a quantitative ethnographer known for developing new methodologies and tools, finding patterns and qualitative stories that generalize within large datasets, and distilling complex ideas into coherent, engaging, and compelling articles and presentations. At heart, I love working on messy data and problem-solving ways to tell meaningful stories.
I am an evaluator and subject matter expert in environmental and science education, the Next Generation Science Standards, virtual learning environments, mixed methods, and the learning sciences. For more information about Siebert-Evenstone Research Consultants, LLC, please email Amanda at amanda.evenstone@gmail.com.
Book Editor
Quantitative Ethnography of the NGSS
I developed a method to identify components of the NGSS and automatically evaluate curricular materials. For this analysis, I operationalized the key NGSS practices and concepts and defined how each practice and concept was used in different curricular materials. After automated classification, each curriculum was modeled and visualized using the same set of codes using ENA. By creating codes and a measurement space based on the NGSS, this computational model can compare how each curriculum addresses or fails to address state standards.
Best Paper Nominations
Shah, M., Barany, A., & Siebert-Evenstone, A.L. (2020). “What would happen if humans disappeared from earth?” Tracing and visualizing change in a pre-school child’s domain-related curiosities. In A.R. Ruis & S.B. Lee (eds.) Advances in Quantitative Ethnography. ICQE 2021. CCIS, vol. 1312, pp. 232-247. Springer, Cham.
*Nominated for Best Paper
Cai, Z., Siebert-Evenstone, A.L., Eagan, B., & Shaffer, D.W. (2020). Using topic modeling for code discovery in large scale text data. In A.R. Ruis & S.B. Lee (eds.) Advances in Quantitative Ethnography. ICQE 2021. CCIS, vol. 1312, pp. 18-31. Springer, Cham.
*Nominated for Best Paper
Fogel, A., Swiecki, Z., Marquart, C., Cai, Z., Wang, Y., Brohinsky, J., Siebert-Evenstone, A. L., Eagan, B., Ruis, A. R., & Shaffer, D. W. (2020). Directed epistemic network analysis. In A.R. Ruis & S.B. Lee (eds.) Advances in Quantitative Ethnography. ICQE 2021. CCIS, vol. 1312, pp. 122-136. Springer, Cham.
*Winner of Best Student Paper
Siebert-Evenstone, A.L., & Shaffer, D.W. (2019). Cause and because: Using epistemic network analysis to model causality in the Next Generation Science Standards. Paper accepted to the International Conference on Quantitative Ethnography, Madison, WI.
*Nominated for Best Student Paper
Cai, Z., Siebert-Evenstone, A.L., Eagan, B., Shaffer, D.W., Hu, X., & Graesser, A. (2019). nCoder+: A semantic tool for improving recall of nCoder coding. Paper accepted to the International Conference on Quantitative Ethnography, Madison, WI.
*Nominated for Best Paper
Siebert-Evenstone, A.L., & Shaffer, D.W. (2019). Location, Location, Location: The effects of place in place-based simulations. In K. Lund, G. Niccolai, E. Lavoué, C. Hmelo-Silver, G. Gweon, & M. Baker (Eds.) A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 1 (pp. 152-159). Lyon, France: International Society of the Learning Sciences.
*Nominated for Best Student Paper
Ruis, A. R., Siebert-Evenstone, A. L., Pozen, R., Eagan, B., & Shaffer, D. W. (2019). Finding common ground: A method for measuring recent temporal context in analyses of complex, collaborative thinking. In K. Lund, G. Niccolai, E. Lavoué, C. Hmelo-Silver, G. Gweon, & M. Baker (Eds.) A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019, Volume 1 (pp. 136-143). Lyon, France: International Society of the Learning Sciences.
* Best Paper Winner
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. In C.K. Looi, J.L. Polman, U. Cress, & P. Reimann (Eds.) Transforming Learning, Empowering Learners: The International Conference of the Learning Sciences (ICLS) 2016, (Volume 1, pp. 631-638), Singapore: International Society of the Learning Sciences.
* Nominated for Best Student Paper
Journal Publication
Siebert-Evenstone, A.L., Arastoopour, G., Collier, W., Swiecki, Z, Ruis, A.R., & Shaffer, D.W. (2017). In search of conversational grain size: Modeling semantic structure using moving stanza windows. Journal of Learning Analytics, 4(3), 123-139.
Software
Ruis, A.R., Lark, T., Siebert-Evenstone, A.L., Barford, C., Klein, J., Hinojosa, C., Dumas, V., Ares, J.N., Tian, B., Bougie, M., Ramakrishnan, C., Vachuska, K., Marshall, L., Dohan, A., Scopinich, K., Marquart, C., Linderoth, J., & Shaffer, D.W. (2019). Local Environmental Modeling (Version 0.1.0) [Software]. Available from https://www.i-plan.us/
Hinojosa, C., Siebert-Evenstone, A.L., Eagan, B.R., Swiecki, Z., Gleicher, M., & Marquart, C. (2019). nCoder (Version 0.2.0) [Software]. Available from http://www.n-coder.org/
Amanda Siebert-Evenstone 