We are so excited to engage in discussion around our work and to garner feedback from the larger community of STEM educators and researchers interested in Data Science and Social Justice.  We are particularly interested in learning about key introductory Data Science content that should be prioritized in our curriculum activities.

Hi Rebecca,

Our model is driven by the passion of the students for their research topics. As you know it is so powerful when students can answer questions of interest to them. We too find our students choose hot topic's like the datasets that were more popular from your project. Our Passion-Driven Statistics model is always looking for good public access archival datasets to use with our students. We only use datasets that have large number of variables and cases with a good code book and clean data. Based on your work with the curate datasets do you have any that you recommend I explore first? 

In the summer of 2019 myself and Kristel Gallagher, a partner at Thiel College held a boot camp at Campbell University Medical School about an hour south of you. The partner at that university is hosting a boot camp using our model this summer for another cohort of medical students.

I have not heard of CODAP. I was thankful that your website links of resources for me to explore. This looks like a great option for our middle school students in STEM/STEAM classes that want to incorporate data but do not have the time or experience with coding and data. 

I am sure as I explore your website I will have more thoughts and questions.

Hi Rebecca - thanks for sharing your work in this great video! Data science is a rapidly growing field with such important implications for addressing systemic issues, but it can be a challenge to engage students with data analysis—especially for younger students who might not have a lot of math skills or interests. Did you investigate students' perceptions of data analysis and if so, what did you find? What are some of the long term impacts you would hope to see for students who participate in this type of learning? 

Thanks for taking a look at our video on community science investigations for social justice!

Best regards,

Sarah

This is great. As a teacher of introductory statistics, the biggest problem is finding datasets. Having datasets that focus on social justice issues is also very important and helpful in our classes. I don't have great datasets, but have you looked to see what Our World in Data has that may be social justice focused?

Hi Rebecca & Ravanasamudram!  I love the CODAP project! We discovered it and considered integrating it with our high school storyline challenge. Although we are still editing the final document for the website, you can see the personalization teachers used to focus the storyline on 10th grade students' interests. Data is always more exciting when the students are asking the questions! The communication of that data plays a huge role in influencing public perception and decision-making. We found that students experienced that influence as they debated or critiqued others' arguments. I love that you use a data approach to visualize social justice issues. Demonstrating explicit evidence goes a long way in convincing opinions! Bravo!

Dr. Lowe and Team, thank you for your wonderful contribution to the showcase and for the tremendous work you all do. I love the focus on social justice issues as personally-relevant and culturally situated topics on which to support science learning. I especially liked how multiple points of entry to making personally relevant connections seemed to be present in your example topic, racial disparities in maternal mortality. I'm curious about the choice of topics and I'm encouraged by your student-centered approach to determining those. What other factors do/did you consider when deciding on which topics to prioritize in your program development? As a showcase facilitator, I'm also curious about others' experiences or thoughts on topic selection.

The two components of this project that stands out are the use of relevant examples to display the injustice (e.g., mortality rates) and the focus on teaching students how find and communicate data in a way that is meaningful to others. Together these factors can contribute greatly to attracting scientists from marginalized communities. I would like to learn more about how the students were positioned to lead this study and advocate for the needs of their communities.

Greetings,

This seems like a very interesting project.  I am very interested in learning more about methodologies used to engage high school and college students in data science.  What were some out the outcomes of students' learning experiences? 

I love this. Pretty much everybody participating in this Video Showcase is thinking hard about engaging students and/or the public in scientific thinking. All sorts of neat approaches to a similar set of goals, but this project is the first one I've seen that lets college freshmen choose a topic of personal importance, then let that serve as the motivation to learn as much as they can. In my experience, grad students usually take the "minimum" amount of statistical coursework. But I wonder if their mentality would be different if they started with data that has deep personal relevance, then followed the trail of "think about how much more you could do with the data if you learned these other techniques" motivation. What a great idea. I'm going to think about how to implement this strategy in my own teaching and the grad program that I lead.

Thanks for this work. Combining an emerging field like data science to address social justice issues provides students an opportunity to leverage the STEM expertise they are developing to address issues in their community and issues that they care about in the real world. I look forward to learning more about the potential impact this project will have. 

Hi DSSJ Team,

It is great to learn about your work! We had a video in the showcase last year sharing our social justice data literacy curriculum modules targeted toward high school students in non-AP mathematics classes from historically marginalized populations, also using CODAP (https://stemforall2021.videohall.com/presentati...). This year our video entry describes our project supporting data literacy in middle school science classes in low-income rural communities, with a focus on examining large-scale extreme weather data (https://stemforall2022.videohall.com/presentati...). Our missions are aligned!

For the age groups we've been working with, and given the limited time that teachers can give to our curriculum units (usually 3 weeks max per fall or spring half of the school year), we found we needed to constrain the questions that students could choose from to explore in their data investigations and problem-based projects. Although we give students access to dozens of variables and thousands of cases from the U.S. decennial census, the American Community Survey, and from NOAA, we have needed to create curated datasets to help students meet lesson and unit learning objectives. How much have students explored freely with the data you have provided, and what have been the results?

In addition, one of the research foci in our high school social justice statistics project has been to help students recognize and understand multivariable relationships (particularly interaction effects among three variables). We have found that analyzing data in the context of real and compelling social questions helps students with this type of thinking, but it's still hard for them. What efforts have you made in this domain, which is so critical when working with large, multivariable datasets?

Thanks Josephine for your thoughtful questions! Similar to your project, in our implementation as well (in a Freshman Seminar class), students had only limited time (3-4 lectures per semester) that they could devote to this project. Within that limited time, they had to learn how to use CODAP, understand the data, and visualize the data and hopefully do some analyses. And remote learning didn't make it any easier. So we don't know how much additional time students devoted to freely exploring the data. Anecdotally, we are aware of a few students doing it. And as Adrienne and Rebecca have noted in an earlier response: "We did look at students' perceptions of data science and we found statistically significant changes in pre-to-post measures of student interest in learning about data science as a career option and their desire to become more knowledgeable about data science."

Thanks for raising the second question. In the current implementation we have not had the time to discuss the interactions of multivariable relationships. Our freshman seminar students included both STEM and non-STEM majors. In future, we are planning to use these topics/datasets in a semester-long course for STEM majors where we hope to address these issues in more depth.

Hi,

Thank you for sharing about your project and for sharing your site!  I really love that you're sharing your data sets, I have been wanting more cleaned up data sets to go into these topics that you have looked at.  

I love this project! I was wondering if you had explored the possibility to connect with human behavioral datasets or survey data? (e.g., from mTurk) and if so: what your experience might have been? We are exploring ways to connect student authentic inquiry using behavioral tasks & surveys to existing datasets that they can explore for inspiration/iteration. 

Thanks Suzanne! We are not familiar with mTurk (we will check it out). We do have plans to collect qualitative data. But given the nature of the social justice topics we are working with, we will need to be sensitive in how we design the questionnaire and to that end we are intending to collaborate with psychology researchers to help us design and collect such data.