Brianna Heggeseth is an Assistant Professor of Statistics at Williams College and will be joining the Math, Statistics, and Computer Science department at Macalester College in Fall 2018.
Leslie Myint is a PhD candidate in Biostatistics at Johns Hopkins University and will be joining the Math, Statistics, and Computer Science department at Macalester College in Fall 2018.
Brittney Bailey is a PhD candidate in Biostatistics at Ohio State University and will be joining the Math and Statistics department at Amherst College in Fall 2018.
For many years, American higher education institutions have been working to increase the racial and ethnic diversity of their campuses. In 1980, about 16% of U.S. students at postsecondary institutions were non-White and that has increased to 43% by 2014 (U.S. Dept. of Education 2016). Besides promoting equal access to social mobility, a diverse student body with a variety of backgrounds, experience, and opinions elevates the learning environment. However, intentions and initiatives fall short if the campus does not adapt to the needs of the student body. In our current national climate, students of color often feel like tokens and/or targets at predominantly white institutions and much of our pedagogy is rooted in European traditions.
There are many individuals, groups, and organizations across the United States actively working to increase diversity and foster inclusion in higher education. In 2016, the U.S. Department of Education published a report of best practices for successfully increasing diversity among students and faculty (U.S. Dept. of Education 2016). This report highlights issues and possible solutions to the leaky pipeline in higher education. Two that we’d like to highlight are “Providing Strong Supports to Help Students Succeed” (academic and social support) and “Ensuring Safe and Inclusive Campuses” (classroom climate).
Academic and Social Support
To provide academic and social support across STEM fields, conferences focused on underrepresented minority students such as SACNAS (Science), Field of Dreams (Math), ABRCMS (Biomedical), CU2MiP (Physics), Grace Hopper (CS), and Infinite Possibilities (Math), just to name a few, have been designed to build community across institutions and provide a space for mentorship and role modeling, which research suggests play a key role in retention (Dennehy 2017).
In our own field of Statistics, the ASA Committee on Minorities in Statistics organizes StatFest, which is an annual undergraduate conference for underrepresented minorities that has been held since 2001 and will be at Amherst College Sept 2018. Additionally, this committee has hosted the Diversity Workshop and Mentoring Program at JSM since 2009 (the workshop is held every 3 years and the mentoring program occurs every year). The Eastern North Atlantic Region (ENAR) of the International Biometric Society (IBS) has offered a workshop on Fostering Diversity in Biostatistics at the annual meeting since 1999. These programs play a vital role in improving the pipeline in Statistics and supporting underrepresented minority students and faculty at a national level. These programs occur once a year.
Classroom Climate
What can we do on a daily basis to provide an inclusive classroom and engage all of our students in the statistical content?
We contend that we all have a role to play in cultivating inclusive classroom climates and developing pedagogies that better serve our all of our students, no matter their past experience. In an attempt to get new ideas, Brianna started an email thread on the Isolated Statistician email list and below we share ideas from that thread as well as our own experience. We hope this incomplete list will inspire you to join the conversation about best teaching practices for an inclusive classroom in Statistics.
First Day: Setting the Tone
On the first day of a course, we talk about our own identity (gender, preferred pronouns, race/ethnicity, education, culture) as a first step to acknowledging that the students have other identities. The time and effort it takes is small but it has resulted in a noticeable impact.
We also make sure that every student talks in class on the first day by having them introduce the person sitting next to them. The goal of this is to establish a collaborative environment in which students know each other and work together to overcome the hurdle of participating in class for the first time.
Syllabi Statements about Diversity, Inclusion, and Disability
While the research is slim, it is widely accepted that including and highlighting a statement about diversity and inclusion on a syllabus (and throughout the course) can signal to student the instructor’s awareness of social issues and show support for students with different identities and experiences. It establishes a classroom tone that is friendly, caring, and supportive so that students know that they belong. Research indicates that students are more likely to prosper academically with collaborative modes of learning that acknowledge students’ personal experiences (Kaplan and Miller 2007).
At Williams, Brianna has worked with other faculty to craft syllabi statements that discuss inclusion, disability support, as well as title IX statements to signal that we want to establish a safe classroom that encourages respectful and equitable participation (Williams Statements). Many institutions have done the same and we encourage you to use these statements or create your own.
Show Diverse Statisticians
On the IsoStat email list, Katie Kinnaird, Data Science postdoctoral fellow at Brown University, started a great discussion about introducing a statistician once a week in her class. Jo Hardin from Pomona College made an additional suggestion that we show our students statisticians that reflect their identities.
To aid in this process, we’ve compiled the list of names from Katie’s email thread, added a few names, and asked our students to help fill in a database of statisticians (past and present) along with information about their identity (country of origin, gender, person of color), and what they are known for. Our drafted database is publicly available on Google Docs. Disclaimer: This document has not been edited to make sure information is correct, but the students provided source links. Please feel free to add, update, and correct.
Choose Data Examples Wisely
As Rouncefield (1995) asserts, “[S]tudents can ask real questions about real-life situations. These in turn raise ethical and moral questions, which motivate students’ learning, making the subject matter more relevant and interesting.”
We have become more mindful about how we choose data examples to use in class and on homework. We think twice about what we assume is “common knowledge” and consider whether it is culture-specific knowledge. Additional information that provides definitions and additional context helps my students who are unfamiliar with the data context.
Lawrence Lesser wrote about Teaching Statistics with Social Justice in 2007 as a way to show students how statistics can change society.
Data Examples: Student-Teacher Ratios, SAT Scores and Income, Health Insurance Coverage, Racial Profiling, Death Penalty
Chance recently had three special issues on human rights, climate change, and modern slavery that are ripe for examples to use in class.
Some Crime Data Examples we’ve used in class
San Francisco Crime Data and Predictive Policing (Topics: Multivariate Thinking, Selection Bias, Predictive Modeling, Algorithmic Bias)
Shiny Apps to explore the data: https://bcheggeseth.shinyapps.io/SFCrimeShinyApp/, https://bcheggeseth.shinyapps.io/sfcrimeeda/
Predictive Policing Contex: http://theconversation.com/why-big-data-analysis-of-police-activity-is-inherently-biased-72640
Police Violence Data (Topics: Selection Bias, Undercoverage, Data Quality, Observed Data v. Randomized Experiments)
Context: https://www.fastcompany.com/3045724/fatal-encounters-crowdsourcing-deaths-by-police
Experimental Study: DC Body Cameras Experiment
Choose Resources Wisely
Along with the increase in racial and ethnic diversity, there is also an increase in economic diversity. The rising costs of textbooks pose a financial burden to students, and the use of expensive, single-code access to online resources for homework and quizzes adds to that burden. While the fields of statistics and data science are rapidly changing, the foundations of statistics are not. We should be mindful of how our choices in textbooks and other class resources affect our students’ abilities to be successful. If a used or older version of a textbook can do the job, we should encourage that as an option for students. (Related: https://www.theatlantic.com/education/archive/2015/05/the-disproportionate-burden-of-student-loan-debt-on-minorities/392456/)
Supplemental resources can also benefit students who are underprepared and may need more time to get up to speed.
Incorporate More Active Learning/Projects
Research supports active learning and peer-led team learning as beneficial pedagogical approaches for everyone but “particular disproportionate benefit for capable but poorly prepared students” (Freeman et al 2014, Snyder 2016, Wieman 2014). Projects that allow students to work with real data and perhaps for a community partner can increase engagement of students, improve collaboration and problem-solving skills, and establish a growth mindset so as to reduce achievement gap in STEM fields and increase retention by enhancing a sense of belonging to the discipline (Kogan and Laursen 2014).
Community-based learning can be a great way to have students apply their knowledge to help organizations in their surrounding community. Having students interface personally with organization leaders throughout the project can give them practice asking meaningful scientific questions and interacting with collaborators. A big motivation of community-based learning is its potential to have students see the positive impact they can have on others through working with data. Many colleges and universities have community-based outreach centers that can provide such opportunities.
Developing Awareness
We all have blind spots, but we can help ourselves discover those blind spots by actively seeking out perspectives from people outside of our identities. There are a number of blogs, articles, and yes, Twitter communities, available that will provide those perspectives (Vanguard Stem, Blackademia, and this dissertation, to name a few) without having to rely on questioning the nearest person whose identity differs from yours.
This blog is not intended to be an exhaustive list of resources but rather a starting point for a conversation. What are you doing in your class to support your diverse student body? Please join us in this conversation at a Birds of a Feather discussion called “Teaching Statistics to Diverse Student Populations” at JSM 2018 in Vancouver (date/time TBA).
References
Dennehy, T.C. and Dasgupta, N. (2017) “Female peer mentors early in college increase women’s positive academic experiences and retention in engineering” Proceedings of the National Academy of Sciences, 114 (23) 5964-5969. https://doi.org/10.1073/pnas.1613117114
Freeman, S. et al. (2014) “Active learning boosts performance in STEM courses” Proceedings of the National Academy of Sciences, 111 (23) 8410-8415. https://doi.org/10.1073/pnas.1319030111
Kaplan, M. & Miller, A.T. (Eds.). (2007). “Special Issue: Scholarship of multicultural teaching and learning.” New Directions for Teaching and Learning, (111).
Kogan, M. & Laursen, S.L. (2014) “Assessing Long-Term Effects of Inquiry-Based Learning: A Case Study from College Mathematics”
Innovative Higher Education 39: 183. https://doi.org/10.1007/s10755-013-9269-9
Lesser, L. (2007), “Critical Values and Transforming Data: Teaching Statistics with Social Justice.” Journal of Statistics Education [Online], 15(1). http://ww2.amstat.org/publications/jse/v15n1/lesser.html
Rouncefield, M. (1995) “The Statistics of Poverty and Inequality.” Journal of Statistics Education [Online], 3(2).
Snyder, J.J. et al. (2016) “Peer-Led Team Learning Helps Minority Students Succeed.” PLoS Biol 14(3): e1002398. https://doi.org/10.1371/journal.pbio.1002398
U.S. Department of Education, Office of Planning, Evaluation and Policy Development and Office of the Under Secretary, (2016) Advancing Diversity and Inclusion in Higher Education, Washington, D.C.. https://www2.ed.gov/rschstat/research/pubs/advancing-diversity-inclusion.pdf
Wieman, C.E. (2014) “Large-scale comparison of STEM teaching methods” Proceedings of the National Academy of Sciences, 111 (23) 8319-8320; https://doi.org/10.1073/pnas.1407304111