Bio

I am an Assistant Professor at UCLA in the departments of Community Health Sciences, Biostatistics, and Education Policy; a lecturer at Stanford's Graduate School of Education; and the Founder and Principal of BITJustice (Bend It To Justice) LLC.

Approach:
I seek to expand belonging and well-being by conducting research in two domains:
1) identifying k-12 practices that reduce racial disparities in health, discipline, and academic performance
2) identifying social policies that combat and sideline racial bias.
I aim to eschew departmental and methodological silos by conducting research that bridges the fields of public health, education, psychology, law, and public policy. While I have conducted dozens of qualitative research projects and produced work in the critical race theory tradition, my research mainly employs quantitative and econometric methods to analyze large-scale data, understand drivers of population health, and elevate effective public policies (e.g., school-based restorative practices).

Recent scholarship:
A recent article in the Proceedings of the National Academy of Sciences elucidates a new mental health risk for Black students—that schools rapidly grow more punitive towards Black students in the first weeks of the school year. It also shows how early-year data can be used to detect—and target interventions towards—schools where racial disparities in discipline are likely to emerge. Another project (published by the Learning Policy Institute and under review at AERA Open) reviews millions of California public student records and finds evidence that student exposure to restorative practices may reduce exclusionary discipline, improve academic achievement, and reduce related racial disparities; and that school adoption may reduce school-wide victimization, misbehavior, gang membership, depression, and sleep deprivation, and may increase school climate and student GPA. Two related projects find that—for Black boys and Black girls—exposure to restorative practices is related to lower depressive symptom, suicidal ideation, and substance abuse rates. A final project using this data finds that Black male and female students who perceive their School Resource Officers as exhibiting racial bias have higher rates of depressive symptoms, anxiety, and psychological distress. I also conduct randomized controlled trials to evaluate means of combatting racial bias. A recent paper in Science Advances reports on a teacher-facing growth-mindset intervention that reduced racial disparities in teachers’ responses to student misbehavior. And a recent field experiment conducted in partnership with the City of Denver found that a scalable racial bias training enhanced public employees’ abilities to combat racial bias in the workplace and society.

Scholarly Impact:
My work has been published in Science Advances, Proceedings of the National Academy of Sciences, and Nature Human Behavior; cited in over 700 peer reviewed articles; and referenced by the New York Times, the Washington Post, NBC, the Department of Education Regional Education Labs, and the House Judiciary Committee.

Teaching:
I have received strong student evaluations in four courses at UCLA (two in health program planning and evaluation, one in approaches to interdisciplinary scholarship, and one to empower students from underrepresented backgrounds to succeed in our comprehensive exam). While earning my PhD at UC Berkeley, I also served as an instructor for multiple statistics classes and received UC Berkeley’s “Outstanding Graduate Student Instructor Award.” Guided by cutting-edge psychological theory, I developed and piloted a package of practices and teaching materials which led multiple cohorts of under-represented minority students to realize minimum raw scores of 90% in statistics classes, and (according to anonymous, in-depth, post-surveys) to overcome many negative internalized beliefs and adopt a growth mindset.

Academic Appointments

  • Lecturer, Graduate School of Education

Contact

  • Academic
    University - Other Teaching/Research Department: Graduate School of Education Position: Lecturer

Additional Info

2023-24 Courses

All Publications

  • The dynamic nature of student discipline and discipline disparities. Proceedings of the National Academy of Sciences of the United States of America Darling-Hammond, S., Ruiz, M., Eberhardt, J. L., Okonofua, J. A. 2023; 120 (17): e2120417120

    Abstract

    Researchers have long used end-of-year discipline rates to identify punitive schools, explore sources of inequitable treatment, and evaluate interventions designed to stem both discipline and racial disparities in discipline. Yet, this approach leaves us with a "static view"-with no sense of how disciplinary responses fluctuate throughout the year. What if daily discipline rates, and daily discipline disparities, shift over the school year in ways that could inform when and where to intervene? This research takes a "dynamic view" of discipline. It leverages 4 years of atypically detailed data regarding the daily disciplinary experiences of 46,964 students from 61 middle schools in one of the nation's largest school districts. Reviewing these data, we find that discipline rates are indeed dynamic. For all student groups, the daily discipline rate grows from the beginning of the school year to the weeks leading up to the Thanksgiving break, falls before major breaks, and grows following major breaks. During periods of escalation, the daily discipline rate for Black students grows significantly faster than the rate for White students-widening racial disparities. Given this, districts hoping to stem discipline and disparities may benefit from timing interventions to precede these disciplinary spikes. In addition, early-year Black-White disparities can be used to identify the schools in which Black-White disparities are most likely to emerge by the end of the school year. Thus, the results reported here provide insights regarding not only when to intervene, but where to intervene to reduce discipline rates and disparities.

    View details for DOI 10.1073/pnas.2120417120

    View details for PubMedID 37068236

  • Insights into the accuracy of social scientists' forecasts of societal change NATURE HUMAN BEHAVIOUR Grossmann, I., Rotella, A. A., Hutcherson, C., Sharpinskyi, K., Varnum, M. W., Achter, S. K., Dhami, M., Guo, X., Kara-Yakoubian, M. R., Mandel, D., Raes, L., Tay, L., Vie, A., Wagner, L., Adamkovic, M., Arami, A., Arriaga, P., Bandara, K., Banik, G., Bartos, F., Baskin, E., Bergmeir, C., Bialek, M. K., Borsting, C. T., Browne, D. M., Caruso, E., Chen, R., Chie, B. J., Chopik, W. N., Collins, R., Cong, C. G., Conway, L., Davis, M. V., Day, M. A., Dhaliwal, N. D., Durham, J., Dziekan, M. T., Elbaek, C., Shuman, E., Fabrykant, M., Firat, M. T., Fong, G. A., Frimer, J. M., Gallegos, J. B., Goldberg, S., Gollwitzer, A., Goyal, J., Graf-Vlachy, L. D., Gronlund, S., Hafenbraedl, S., Hartanto, A. J., Hirshberg, M. J., Hornsey, M., Howe, P. L., Izadi, A., Jaeger, B., Kacmar, P., Kim, Y., Krenzler, R. G., Lannin, D., Lin, H., Lou, N., Lua, V. W., Lukaszewski, A. L., Ly, A. R., Madan, C., Maier, M. M., Majeed, N. S., March, D. A., Marsh, A., Misiak, M., Myrseth, K. M., Napan, J., Nicholas, J., Nikolopoulos, K., Otterbring, T., Paruzel-Czachura, M., Pauer, S., Protzko, J., Raffaelli, Q., Ropovik, I., Ross, R. M., Roth, Y., Roysamb, E., Schnabel, L., Schuetz, A., Seifert, M., Sevincer, A. T., Sherman, G. T., Simonsson, O., Sung, M., Tai, C., Talhelm, T., Teachman, B. A., Tetlock, P. E., Thomakos, D., Tse, D. K., Twardus, O. J., Tybur, J. M., Ungar, L., Vandermeulen, D., Vaughan Williams, L., Vosgerichian, H. A., Wang, Q., Wang, K., Whiting, M. E., Wollbrant, C. E., Yang, T., Yogeeswaran, K., Yoon, S., Alves, V. R., Andrews-Hanna, J. R., Bloom, P. A., Boyles, A., Charis, L., Choi, M., Darling-Hammond, S., Ferguson, Z. E., Kaiser, C. R., Karg, S. T., Ortega, A., Mahoney, L., Marsh, M. S., Martinie, M. C., Michaels, E. K., Millroth, P., Naqvi, J. B., Ng, W., Rutledge, R. B., Slattery, P., Smiley, A. H., Strijbis, O., Sznycer, D., Tsukayama, E., van Loon, A., Voelkel, J. G., Wienk, M. A., Wilkening, T. 2023

    Abstract

    How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.

    View details for DOI 10.1038/s41562-022-01517-1

    View details for Web of Science ID 000931761000002

    View details for PubMedID 36759585

https://orcid.org/0000-0002-6353-4670