Publications and Papers

Elizabeth Adams

This mixed methods study describes teachers’ experiences implementing STEM instruction in the context of the STEM Academy for Science Teachers and Leaders. Using a latent profile analysis, we grouped teachers based on implementation as measured by monthly classroom observations during the school year. We analyzed interviews with teachers to identify themes within each group of teachers. We found that teachers with higher implementation consistently encouraged students’ conceptual thinking or productive struggle related to the content. Teachers with higher implementation also reported more support from school leadership.

Adams, E. L., Ketterlin-Geller, L. R., Pierce, K., & Knox T. (2022). Teachers’ implementation of STEM instructional practices in the context of the STEM Academy: A mixed methods study. In P. Jenlink (Ed.), STEM Teacher Preparation and Practice for the 21st Century: Research-Based Insights (pp. 29-49). Charlotte, NC: Information Age Publishing

 

Sapna Cheryan

Sapna Cheryan published an o-ed in Scientific American entitled There are too few women in computer science and engineering in July 2022. She also received the 2022 Society for Experimental Social Psychology (SESP) Diversity Science Award that recognizes early to mid-career individuals who have made substantial, innovative, and sustained contributions to advancing the social psychology of diversity. She also received two REU Supplements in 2022 to her NSF grants to fund a cohort of community college students to work on research in her lab.

Published an article in Scientific American: There are too few women in computer science and engineering, Scientific American, July 2022
Received the Society for Experimental Social Psychology (SESP) Diversity Science Award, 2022
Received two REUs
National Science Foundation Human Resources Development, REU Supplement ($20,898), PI, 2022-2023
National Science Foundation Social, Behavioral, and Economic Sciences REU Supplement ($8,000), PI, 2022-2023

 

Prashant Loyalka

Moon K., Bergemann, P., Brown, D., Chen, A., Chu, J., Eisen, E., Fischer, G., Loyalka, P., Rho, S., Cohen, J. (2022). “Manufacturing Productivity with Worker Turnover.” Management Science.

Loyalka, P., et al. (2022) “The Effect of Faculty Research on Student Learning in College.” Educational Researcher.

Moon K., Loyalka, P., Bergemann, P., Cohen, J. (2022) “The Hidden Cost of Worker Turnover: Attributing Product Reliability to the Turnover of Factory Workers.” Management Science.

 

Daniel Reinholz

These publications describe the use of the EQUIP observation tool for capturing and reducing racial and gender inequities in STEM education (https://www.equip.ninja). The Urban Education paper analyzes 100 mathematics classrooms using hierarchical linear modeling to understand student participation by race and gender, and connects this classroom-level discourse to larger Discourses about students described by district leaders. The JRME paper analyzes 20 undergraduate mathematics classrooms to show how inequitable participation by gender is linked to inequitable student outcomes by gender. The Change article provides a high-level summary of the use of the EQUIP tool to provide data to instructors to help them revise their practice and address inequities like those shown in the other papers.

Reinholz, D. L., Reid, A., & Shah, N. (2022). Not another bias workshop: Using equity analytics to promote anti-racist teaching. Change: The Magazine of Higher Learning.

Reinholz, D. L. & Wilhelm, A. G., (2022). Race-gender D/discourses in mathematics education: (Re)-producing inequitable participation patterns across a diverse, instructionally-advanced district. Urban Education.

Reinholz, D. L., Johnson, E., Andrews-Larson, C., Stone-Johnstone, A., Smith, J., Mullins, B., Fortune, N., Keene, K., & Shah, N. (2022). When active learning is inequitable: Women’s participation predicts gender inequities in mathematical performance. Journal for Research in Mathematics Education.

 

Ben Van Dusen

Researchers often frame quantitative research as objective, but every step in data collection and analysis can bias findings in often unexamined ways. In this investigation, we examined how the process of selecting variables to include in regression models (model specification) can bias findings about inequities in science and math student outcomes. We identified the four most commonly used methods for model specification in discipline-based education research about equity: a priori, statistical significance, variance explained, and information criterion. Using a quantitative critical perspective that blends statistical theory with critical theory, we reanalyzed the data from a prior publication (Van Dusen & Nissen, 2020) using each of the four methods and compared the findings from each. We concluded that using information criterion produced models that best aligned with our quantitative critical perspective’s emphasis on intersectionality and models with more accurate coefficients and uncertainties. Based on these findings, we recommend researchers use information criterion for specifying models about inequities in STEM student outcomes. https://www.dl.begellhouse.com/journals/00551c876cc2f027,243d389e1da54d31,6f5948f62da28812.html

 

Jamaal Young

Willie C. Harmon Jr., Marlon James, Jamaal Young & Lawrence Scott (2022) Black Fathers Rising: A QuantCrit Analysis of Black Fathers’ Paternal Influence on Sons’ Engagement and Sense of School Belonging in High School, Equity & Excellence in Education, DOI: 10.1080/10665684.2022.210001

Jamaal Young & Jemimah Young (2022) Decoding the data dichotomy: applying QuantCrit to understand racially conscience intersectional meta-analytic research, International Journal of Research & Method in Education, DOI: 10.1080/1743727X.2022.2093847

Young, J. L., & Young, J. (2022). Underrepresentation in Gifted Education Revisited: The Promise of Single-Group Summaries and Meta-Analytic QuantCrit. Gifted Child Quarterly66(2), 136–138. https://doi.org/10.1177/00169862211039731

Tholen, A., Edosomwan, K., Hong, D., Fulmer, G.W., & Young, J. (2022). Increasing Access to Advanced Mathematics through Self-Selection: A Multinomial Logistic Regression Analysis. The High School Journal 105(2), 145-169. doi:10.1353/hsj.2022.0003.

Edosomwan, K., Young, J., Young, J., & Tholen, A. (2022). Mathematics Mobility in the Middle Grades: Tracking the Odds of Completing Calculus. Middle Grades Review, 8(1). https://scholarworks.uvm.edu/mgreview/vol8/iss1/4

Young, Jemimah; Worley, Cristina; and Young, Jamaal (2022) “The Promise of the Taxonomy of Online Racism for Critical Race Media Literacy in Social Studies Education Research,” The Councilor: A Journal of the Social Studies: Vol. 83: No. 2, Article 3.
Available at: https://thekeep.eiu.edu/the_councilor/vol83/iss2/3

 

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