Early Bridges to Data Science Program
Our society, economy, and everyday lives are increasingly being shaped by the rapid advances in Artiﬁcial Intelligence (AI). According to the World Economic Forum, AI will fundamentally alter up to 30% of future jobs by the end of the decade.1 There is growing recognition that today’s young adults and students must develop a fundamental understanding of data science to become more “data literate” citizens in preparation for the jobs and AI-related technological advances of tomorrow.2 3 Unfortunately, current K-12 math and science curricula have not kept pace in providing educational opportunities for students to engage with and develop data literacy skills that are culturally relevant and grounded in real-world applications.4
We propose a program that attempts to bridge this gap between the classroom and the current and anticipated workforce changes. Towards that end, we plan to partner with middle school teachers within the South Bend and Elkhart region to build communities and develop classroom content that prepares students to thrive in a data-driven economy. Given the current lack of representation of students from underserved communities across STEM disciplines,5 part of our objective will be to make engaging and culturally relevant data science content which can be delivered either in the classroom or via before or after-school programming to underserved students (especially those from rural and urban communities). Focusing on the middle school years is appropriate, given that students should have developed basic concepts of numeracy, arithmetic, probability, and even some basic data visualization skills in those grades.
The ﬁrst phase of the project is split into two parts. During the ﬁrst part, a pilot group of 6th to 8th-grade teachers from area middle schools will participate in a 5-module data science-focused professional development program during the summer of 2022 (the last week of July). The program will be taught by a mix of Notre Dame faculty and industry experts. A key element of the program will be to help teachers acquire the necessary data science domain knowledge to effectively engage 6th to 8th graders in data science. At the conclusion of the training program, each teacher will be assigned a faculty mentor, graduate student, and/or industry expert to serve as a curriculum adviser. In the second part of phase 1, teachers will collaborate with their curriculum adviser to develop pedagogical content to be delivered in the classroom or during an after-school program during a portion of the academic year. The learning modules developed during this process will reﬂect real-world, culturally relevant applications of data science in society, and will focus on the development of skills necessary for a next-generation analytics workforce. We will also partner with the Young Data Scientists League (YDSL)6, a non-proﬁt, whose mission is to empower K-12 students with the data science education they need to succeed in tomorrow’s economy.
In phase 2 (and subsequent phases), we will scale up the teacher professional development program to reach a larger and more varied pool of teachers and schools. While our intent is to deliver the summer professional development in-person during the ﬁrst year, we are evaluating the possibility of delivering the content virtually in subsequent years. This would allow for participants to engage remotely and/or asynchronously, thereby enabling us to reach a wider audience. In phase 3, we will engage in a focused evaluation of the program’s impact, with an emphasis on evaluating student learning outcomes.
We anticipate four speciﬁc project outcomes and societal impacts from the project:
- Develop and test evidence-based middle-school curricular activities to generate interest and engagement among students; to be measured with rigorous ﬁeld experimentation.
- Increase data science knowledge of middle school educators; to be measured with before and after assessments of competencies.
- Contribute to the pipeline of students that are well-prepared and motivated to beneﬁt from future STEM learning opportunities; to be measured by student participation, engagement, and retention outcomes.
- Broaden participation of underrepresented students in disciplines related to data science; to be measured by student participation, engagement, and retention outcomes.
1 Hawksworth, John. “AI and robots could create as many jobs as they displace”. World Economic Forum: Annual Meeting of the New Champions, (2018).
2 Press, G. (2021). Salaries And Job Opportunities For Data Scientists Continue To Rise. Forbes. 27 June 2021. https://www.forbes.com/sites/gilpress/2021/06/27/salaries-and-job-opportunities-for-data-scientists-continue-to-rise/?sh=21c7aeab4276
3 Schroeder, B. (2021). The Data Analytics Profession And Employment Is Exploding—Three Trends That Matter. Forbes. 11 June 2021. https://www.forbes.com/sites/bernhardschroeder/2021/06/11/the-data-analytics-profession-and-employment-is-exploding-three-trends-that-matter/?sh=2c2d764c3f81
4 Lee, V. R., & Wilkerson, M. H. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects. https://digitalcommons.usu.edu/itls_facpub/634/
5 National Science Foundation [NSF]. (2021). Women, Minorities, and Persons with Disabilities in Science and Engineering. Retrieved 13 December 2021 from https://ncses.nsf.gov/pubs/nsf21321